Capturing agricultural soil freeze/thaw state through remote sensing and ground observations: A soil freeze/thaw validation campaign Remote Sens. Environ. (IF 6.265) Pub Date : 2018-04-12 Tracy L. Rowlandson, Aaron A. Berg, Alexander Roy, Edward Kim, Renato Pardo Lara, Jarrett Powers, Kristin Lewis, Paul Houser, Kyle McDonald, Peter Toose, Albert Wu, Eugenia De Marco, Chris Derksen, Jared Entin, Andreas Colliander, Xiaolan Xu, Alex Mavrovic
A field campaign was conducted October 30th to November 13th, 2015 with the intention of capturing diurnal soil freeze/thaw state at multiple scales using ground measurements and remote sensing measurements. On four of the five sampling days, we observed a significant difference between morning (frozen scenario) and afternoon (thawed scenario) ground-based measurements of the soil relative permittivity. These results were supported by an in situ soil moisture and temperature network (installed at the scale of a spaceborne passive microwave pixel) which indicated surface soil temperatures fell below 0 °C for the same four sampling dates. Ground-based radiometers appeared to be highly sensitive to F/T conditions of the very surface of the soil and indicated normalized polarization index (NPR) values that were below the defined freezing values during the morning sampling period on all sampling dates. The Scanning L-band Active Passive (SLAP) instrumentation, flown over the study region, showed very good agreement with the ground-based radiometers, with freezing states observed on all four days that the airborne observations covered the fields with ground-based radiometers. The Soil Moisture Active Passive (SMAP) satellite had morning overpasses on three of the sampling days, and indicated frozen conditions on two of those days. It was found that >60% of the in situ network had to indicate surface temperatures below 0 °C before SMAP indicated freezing conditions. This was also true of the SLAP radiometer measurements. The SMAP, SLAP and ground-based radiometer measurements all indicated freezing conditions when soil temperature sensors installed at 5 cm depth were not frozen.
Long-term record of top-of-atmosphere albedo over land generated from AVHRR data Remote Sens. Environ. (IF 6.265) Pub Date : 2018-04-12 Zhen Song, Shunlin Liang, Dongdong Wang, Yuan Zhou, Aolin Jia
Top-of-atmosphere (TOA) albedo is a fundamental component of Earth's energy budget. To date, long-term global land TOA albedo products with spatial resolution higher than 20-km are not available. In this study, we propose a novel algorithm to retrieve TOA albedo from multispectral imager observations acquired by Advanced Very High Resolution Radiometer (AVHRR), which provides the longest continuous record of global satellite observations since 1981. Direct estimation models were established first to derive instantaneous TOA broadband albedo under various atmospheric and surface conditions, including cloudy-sky, clear-sky (snow-free) and snow-cover conditions. To perform long-term series analysis, the instantaneous TOA albedo were then converted to daily/monthly mean values based on the diurnal curves from multi-year Clouds and the Earth's Radiant Energy System (CERES) 3-hourly flux dataset. Calibration differences between sequential AVHRR sensors were further mitigated by invariant targets normalization. The retrieved TOA albedo at 0.05° × 0.05° was validated against two TOA albedo datasets, CM SAF (Climate Monitoring Satellite Application Facility) flux data and CERES flux data, at spatial resolutions of 0.05° × 0.05°, 20 km × 20 km and 1° × 1°. The instantaneous TOA albedo had an overall Root-Mean-Square-Error (RMSE) of 0.047 when compared with 20-km CERES fluxes, whereas the 1° by 1° monthly mean TOA albedo showed closer agreements with both CM SAF and CERES, with RMSE ranging from 0.029 to 0.040 and from 0.022 to 0.031, respectively. Moreover, our product was found to be highly consistent with both CERES and CM SAF at long-term trend detection. The extensive validation indicated the robustness of our algorithm and subsequently, comparable data quality with existing datasets. With global coverage and long time series (1981–2017), our product is expected to provide valuable information for climate change studies.
Tracking crop phenological development using multi-temporal polarimetric Radarsat-2 data Remote Sens. Environ. (IF 6.265) Pub Date : 2017-09-22 Francis Canisius, Jiali Shang, Jiangui Liu, Xiaodong Huang, Baoluo Ma, Xianfeng Jiao, Xiaoyuan Geng, John M. Kovacs, Dan Walters
Information on crop phenological development stages such as emergence, flowering, fruiting, maturing and senescence is essential for crop production surveillance and yield prediction. It has long been related to optical spectral signatures such as the Normalized Difference Vegetation Index (NDVI) or spectral shifts in the red-edge range. In recent years, more efforts have been made to explore the sensitivity of Synthetic Aperture Radar (SAR), particularly polarimetric SAR signatures, to crop biophysical parameters or phenological stages. In this study, phenological metrics of canola (Brassica napus) and spring wheat (Triticum spp.) are related with temporal evolution of polarimetric SAR parameters derived from the C-band RADARSAT-2 full polarimetric SAR data. Both crops are very common in north eastern Ontario, Canada, but have very anatomically different development processes. From multi-temporal RADARSAT-2 data acquired in three consecutive years (2012–2014), significant correlations were observed between a number of SAR polarimetric parameters and the growth parameters of both crops. Strong correlation was observed between plant height and the Alpha angle of the Cloude-Pottier decomposition, with the R2 of 0.91 and 0.66 for canola and wheat, respectively. The R2 increased when the polarimetric parameters were smoothed in the time domain (R2 of 0.98 for canola and 0.88 for wheat). Strong correlation was also observed for the two crops between the effective leaf area index (LAIe) and the Beta angle, and between days-after-seeding (DAS) and a combination of the Alpha and the Beta angles. These findings show that multi-temporal C-band polarimetric SAR parameters could be used for tracking crop phenological development stages.
A novel method to retrieve the nocturnal boundary layer structure based on CCD laser aerosol detection system measurements Remote Sens. Environ. (IF 6.265) Pub Date : 2018-04-11 Yuxuan Bian, Chunsheng Zhao, Wanyun Xu, Ye Kuang, Jiangchuan Tao, Wei Wei, Nan Ma, Gang Zhao, Shaopeng Lian, Wangshu Tan, John E. Barnes
Mixing layer height (MLH) is a key parameter for evaluating the transport and diffusion of atmospheric pollutants in both air quality forecasting and satellite data retrieval. However, there is a lack of methods for obtaining the nocturnal MLH. In this study, a novel instrument named the charge-coupled device-laser aerosol detection system (CCD-LADS) was developed to study the nocturnal MLH and boundary layer structure from the surface. The system mainly includes a continuous laser and a charge-coupled device camera with a fisheye lens. Structures of atmospheric layers characterized by the CCD-LADS were compared with those measured by a ceilometer. The heights of two atmospheric layers quantified by measurements with the CCD-LADS and the ceilometer show good agreement, with a relative difference of 5%. The results of this comparison demonstrated that the CCD-LADS is capable of distinctly identifying the nocturnal vertical structure of the atmosphere. The advantage of the CCD-LADS in retrieving the nocturnal MLH is that the CCD-LADS can provide the boundary layer structures under 100 m, while the ceilometer and other lidar measurements cannot retrieve the atmospheric structures below that altitude. CCD-LADS was deployed in a comprehensive field campaign measuring air pollution in the University of Chinese Academy of Sciences, located at the border between the North China Plain and Yanshan Mountain, during January 2016. The fine characteristics and patterns of the nocturnal boundary layer structures were derived with the CCD-LADS measurements.
Hierarchical Bayesian space-time estimation of monthly maximum and minimum surface air temperature Remote Sens. Environ. (IF 6.265) Pub Date : 2018-04-11 Ning Lu, Shunlin Liang, Guanghui Huang, Jun Qin, Ling Yao, Dongdong Wang, Kun Yang
Surface air temperature (SAT) is a critical metric that is used to assess regional warming and cooling patterns, and maximum and minimum SATs are required to evaluate the model predictions of climate extremes. Since station SAT data are irregularly distributed, land surface temperature (LST) values derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data are used to estimate regional SAT by using linear regression methods. The deviations between SAT and LST are largely dependent on space and time, which hampers the estimation of linear regression, especially for the maximum SAT. To obtain accurate regional SAT estimates, a three-stage hierarchical Bayesian (HB) model is proposed that incorporates the MODIS LSTs as model covariates and specifies the deviations with structured dependence of MODIS LST fields. Sampling of model parameters and estimation of SAT values are implemented under the Bayesian paradigm using a Markov Chain Monte Carlo algorithm. Sensitivity analyses involving various model configurations and running processes are discussed to help build a robust HB model. The model's performance is evaluated using station measurements that are not used in the modeling process, with RMSEs of 2.15 K (0.75%) and 1.97 K (0.73%) for monthly maximum and minimum SATs, respectively. The evaluation indicates that HB modeling is an effective method to estimate SAT from MODIS LST. The verified HB model with the covariate inputs of both MODIS daytime and nighttime LSTs is used to reproduce monthly maximum and minimum SATs that are spatially continuous over the Qinghai province in Northwestern China for 2003–2011. From the comparison between MODIS LST and HB-estimated SAT, it is found that the spatial structure and warming patterns of LST and SAT show significant distinctions, implying that they cannot be substituted for one another when assessing the regional warming trends. The spatial heterogeneity of HB model estimation is able to provide thorough insights into regional SAT status changes that could otherwise be biased by station deployment.
LiDAR derived forest structure data improves predictions of canopy N and P concentrations from imaging spectroscopy Remote Sens. Environ. (IF 6.265) Pub Date : 2018-04-09 Michael Ewald, Raf Aerts, Jonathan Lenoir, Fabian Ewald Fassnacht, Manuel Nicolas, Sandra Skowronek, Jérôme Piat, Olivier Honnay, Carol Ximena Garzón-López, Hannes Feilhauer, Ruben Van De Kerchove, Ben Somers, Tarek Hattab, Duccio Rocchini, Sebastian Schmidtlein
Imaging spectroscopy is a powerful tool for mapping chemical leaf traits at the canopy level. However, covariance with structural canopy properties is hampering the ability to predict leaf biochemical traits in structurally heterogeneous forests. Here, we used imaging spectroscopy data to map canopy level leaf nitrogen (Nmass) and phosphorus concentrations (Pmass) of a temperate mixed forest. By integrating predictor variables derived from airborne laser scanning (LiDAR), capturing the biophysical complexity of the canopy, we aimed at improving predictions of Nmass and Pmass. We used partial least squares regression (PLSR) models to link community weighted means of both leaf constituents with 245 hyperspectral bands (426–2425 nm) and 38 LiDAR-derived variables. LiDAR-derived variables improved the model's explained variances for Nmass (R2cv 0.31 vs. 0.41, % RSMEcv 3.3 vs. 3.0) and Pmass (R2cv 0.45 vs. 0.63, % RSMEcv 15.3 vs. 12.5). The predictive performances of Nmass models using hyperspectral bands only, decreased with increasing structural heterogeneity included in the calibration dataset. To test the independent contribution of canopy structure we additionally fit the models using only LiDAR-derived variables as predictors. Resulting R2cv values ranged from 0.26 for Nmass to 0.54 for Pmass indicating considerable covariation between biochemical traits and forest structural properties. Nmass was negatively related to the spatial heterogeneity of canopy density, whereas Pmass was negatively related to stand height and to the total cover of tree canopies. In the specific setting of this study, the importance of structural variables can be attributed to the presence of two tree species, featuring structural and biochemical properties different from co-occurring species. Still, existing functional linkages between structure and biochemistry at the leaf and canopy level suggest that canopy structure, used as proxy, can in general support the mapping of leaf biochemistry over broad spatial extents.
Enhanced canopy growth precedes senescence in 2005 and 2010 Amazonian droughts Remote Sens. Environ. (IF 6.265) Pub Date : 2018-04-09 Yi Y. Liu, Albert I.J.M. van Dijk, Diego G. Miralles, Matthew F. McCabe, Jason P. Evans, Richard A.M. de Jeu, Pierre Gentine, Alfredo Huete, Robert M. Parinussa, Lixin Wang, Kaiyu Guan, Joe Berry, Natalia Restrepo-Coupe
Unprecedented droughts hit southern Amazonia in 2005 and 2010, causing a sharp increase in tree mortality and carbon loss. To better predict the rainforest's response to future droughts, it is necessary to understand its behavior during past events. Satellite observations provide a practical source of continuous observations of Amazonian forest. Here we used a passive microwave-based vegetation water content record (i.e., vegetation optical depth, VOD), together with multiple hydrometeorological observations as well as conventional satellite vegetation measures, to investigate the rainforest canopy dynamics during the 2005 and 2010 droughts. During the onset of droughts in the wet-to-dry season (May–July) of both years, we found large-scale positive anomalies in VOD, leaf area index (LAI) and enhanced vegetation index (EVI) over the southern Amazonia. These observations are very likely caused by enhanced canopy growth. Concurrent below-average rainfall and above-average radiation during the wet-to-dry season can be interpreted as an early arrival of normal dry season conditions, leading to enhanced new leaf development and ecosystem photosynthesis, as supported by field observations. Our results suggest that further rainfall deficit into the subsequent dry season caused water and heat stress during the peak of 2005 and 2010 droughts (August–October) that exceeded the tolerance limits of the rainforest, leading to widespread negative VOD anomalies over the southern Amazonia. Significant VOD anomalies were observed mainly over the western part in 2005 and mainly over central and eastern parts in 2010. The total area with significant negative VOD anomalies was comparable between these two drought years, though the average magnitude of significant negative VOD anomalies was greater in 2005. This finding broadly agrees with the field observations indicating that the reduction in biomass carbon uptake was stronger in 2005 than 2010. The enhanced canopy growth preceding drought-induced senescence should be taken into account when interpreting the ecological impacts of Amazonian droughts.
Retrieving forest canopy clumping index using terrestrial laser scanning data Remote Sens. Environ. (IF 6.265) Pub Date : 2018-04-04 Lixia Ma, Guang Zheng, Xiaofei Wang, Shiming Li, Yi Lin, Weimin Ju
Quantitatively characterizing the non-random spatial distributions of foliage elements including coniferous needles is critical to map the radiation regime and retrieve the biophysical parameters of a given forest canopy from three-dimensional (3-D) perspective. Different experimental setups bring various challenges to the process of retrieving forest canopy clumping index (CI) using terrestrial laser scanning (TLS). In this paper, through developing a voxel-based gap size (VGS) algorithm, we compared the TLS-based forest canopy CIs with the ones obtained using the digital hemispherical photography (DHP)-based and tracing radiation and architecture of canopy (TRAC)-based approaches. Moreover, we investigated the effects of incident directions of solar beams, voxel size, and woody canopy components on the final retrieval accuracy of forest canopy CIs. Our results showed that: (1) TLS-based CIs accounted for 81% (N = 30, p < 0.001) of variations in the DHP-based method. (2) the anisotropic nature of forest canopy CIs suggested that a relatively comprehensive TLS data of a forest canopy was required to investigate the 3-D spatial variations of forest gap size distributions and CIs. (3) The user-defined laser sampling spacing was a reliable reference value to determine the voxel size when using the VGS algorithm. (4) It was recommended to separate woody canopy components when computing the forest canopy CI, especially for forest plots with higher proportions of woody material. (5) The effects of the penumbra on TLS-based forest canopy CIs were much more limited compared with the traditional optical instruments (i.e., DHP or TRAC). This work provides a solid foundation to dramatically improve the retrieval accuracy of leaf area index (LAI) using TLS.
A spatial ensemble approach for broad-area mapping of land surface properties Remote Sens. Environ. (IF 6.265) Pub Date : 2018-04-06 Sam Hooper, Robert E. Kennedy
Understanding rapid global change requires land cover maps with broad spatial extent, but also fine spatial and temporal resolution. Developing such maps presents a unique challenge, as variability in relationships between spectral characteristics (i.e., predictors) and a response variable is likely to increase with the size of the region across which a model is built and applied. Although most mapping approaches apply the same predictor-response relationships globally across the entire modeling region, learned relationships from one local area may be invalid for another when predicting across broad extents. Here, we adapted a spatial ensemble approach borrowed from species distribution modeling to land cover mapping, and evaluated whether the approach could faithfully represent spatial variation in relationships between land cover and spectral data. The spatiotemporal exploratory model (STEM) uses an ensemble of regression trees defined within spatially overlapping support sets, producing a broad-extent map that reflects variability at the spatial scale of each constituent support set. As test cases for reference maps, we used 30-m-resolution forest canopy and impervious surface cover layers from the 2001 U.S. National Land Cover Database (NLCD) for the states of Washington, Oregon, and California. When testing strategies for support set size and sampling intensity, we found that predictor-response relationships were strongest when individual components of the spatial ensemble were small and when sampling intensity was high. Compared to aspatial bagged decision tree and random forest models, we found that the STEM approach successfully captured variation in our source maps, both globally and at scales smaller than the modeling region. Leveraging the spatial structure of a STEM, we also mapped per-pixel spatial variation in prediction confidence and the importance of different predictor variables. After testing appropriate spatial ensemble and sampling strategies, we extended the predictor-response relationships gleaned from the 2001 source maps into a yearly time series based on temporally-smoothed spectral data from the LandTrendr algorithm. The end products were yearly forest canopy and impervious surface cover time series representing 1990–2012. Formal evaluation showed that our temporally extended maps also closely resembled NLCD maps from 2011. The aim of this research was to cultivate the implicit relationships between spectral data and a given map, not improve them, but as the need for time series maps produced at both broad extents and fine resolutions increases, our results demonstrate that an ensemble of locally defined estimators is potentially more appropriate than conventional ensemble models for land cover mapping across broad extents.
Mapping of forest alliances with simulated multi-seasonal hyperspectral satellite imagery Remote Sens. Environ. (IF 6.265) Pub Date : 2018-04-06 Matthew L. Clark, Jennifer Buck-Diaz, Julie Evens
A consistent and hierarchical classification of vegetation, such as the U.S. National Vegetation Classification (NVC) system, supports comprehensive conservation and management of natural ecosystems. At a detailed level, the NVC alliance is defined by diagnostic species and composition. Maps at this level of classification are often produced at local to regional scales (areas <25,000 km2) with costly manual to semi-automated interpretation of high resolution imagery. The main objective of this study was to assess the effectiveness of machine learning for automated, per-pixel (30 m) mapping of forest alliances with multi-seasonal hyperspectral imagery from a future satellite mission (HyspIRI), as simulated from Airborne Visible/Infrared Imaging Spectrometer Classic (AVIRIS-C) data. The study area was the San Francisco (S.F.) Bay Area, California, where we mapped forest alliances at regional and county scales. We implemented the Support Vector Machine (SVM) classifier in a two-stage approach, first mapping regional land cover followed by forest alliances in closed-canopy tree pixels. Predictor variables were reflectance bands and hyperspectral metrics based on indices, derivatives and absorption-fitting techniques applied to reflectance spectra, with data grouped into summer and three-season (spring, summer, fall) sets. For forest alliances, hyperspectral metrics improved overall accuracy of classifications by 2.9 to 6.4% relative to classifications based on the original reflectance bands. Multi-seasonal data improved overall accuracy by 1.3 to 6.2% relative to summer-only data. Using multi-seasonal metrics, the S.F. Bay Area regional classification with 21 alliances had an overall accuracy of 65.7% (Kappa 0.63), while the Sonoma County classification with 16 alliances had an accuracy of 75.9% (Kappa 0.72). Most forest alliances had internal variation in lifeform, species and structural properties that increased within-class spectral-temporal variation and complicated discrimination. Despite this challenge, classification accuracies were similar to regional NVC alliance reference data. We conclude that a hyperspectral satellite, with its repeat and global image acquisitions, has strong potential for accurate and economical mapping and monitoring the Earth's vegetation communities.
The accuracy of snow melt-off day derived from optical and microwave radiometer data — A study for Europe Remote Sens. Environ. (IF 6.265) Pub Date : 2018-04-03 Sari Metsämäki, Kristin Böttcher, Jouni Pulliainen, Kari Luojus, Juval Cohen, Matias Takala, Olli-Pekka Mattila, Gabriele Schwaizer, Chris Derksen, Sampsa Koponen
This paper describes the methodology for deriving yearly pixel-wise snow melt-off day maps from optical data-based FSC (Fractional Snow Cover) without conducting any interpolation for cloud-obscured pixels or otherwise missing data. The Copernicus CryoLand Pan-European FSC time series for 2001–2016 re-gridded to 0.1° serves as input for the production of 16 years of melt-off day maps for Europe. These maps are compared with passive microwave radiometer (MWR) melt retrievals, to compare the performance of these two independent datasets, particularly concerning the effect of physiographic and snow conditions on the accuracy of the melt-off day estimates. Both these datasets are evaluated against melt-off day derived from in situ snow depth (SD) time series observed at European weather stations. We also present the relationship of these snow melt-off day products to a passive microwave radiometer-derived landscape freeze/thaw product. Our results show that the melt-off day derived from optical springtime FSC time series provides the strongest correlation with the snow melt-off day with respect to the in situ data. Overall the deviation of CryoLand FSC data derived melt-off day to that indicated by in situ observations is quite small, with positive bias of 0.9 days, and RMSE of 13.1 days. For 85% of the analyzed cases the differences are between ±10 days. Across Europe the MWR-based detection of melt-off day is less accurate; the investigated method performs the best for areas with sustained seasonal snow cover. Based on the time series for MWR-based melt-off day (1980–2016) and FT-ESDR (1980–2014), separately for boreal forests and tundra, we also found a clear trend towards earlier snow clearance: a decrease of melt-off day by as much as ~5 days per decade in boreal forest region was observed.
Topographic controls on the surging behaviour of Sabche Glacier, Nepal (1967 to 2017) Remote Sens. Environ. (IF 6.265) Pub Date : 2018-04-03 Arminel M. Lovell, J. Rachel Carr, Chris R. Stokes
Using a combination of Landsat, Pléiades and CORONA satellite imagery from 1967 to 2017, we map changes in the terminus position, ice surface velocity and surface elevation of Sabche Glacier, and report the first observations of surging behaviour in central Nepal. Our observations show that Sabche Glacier surged four times over the last 50 years. The three most recent surges occurred at 10 to 11-year cycles, which is one of the shortest surge cycles ever recorded. Detailed analysis of the most recent surge (2012 onwards), indicates that the glacier advanced 2.2 km and experienced maximum velocities of 1.6 ± 0.10 m day−1. During this surge, there was a surface elevation gain at the terminus of up to 90 ± 6.19 m a−1, with a corresponding surface lowering of between 10 ± 6.19 and 60 ± 6.19 m a−1, 3 km up-glacier of the terminus. This transfer of mass amounted to a volume of ~2.7 × 107 ± 0.1 × 107 m3a−1. Sabche Glacier is the first surge-type glacier to be observed in the central Himalayas, but this is consistent with a previous global analysis which indicates that surge-type glaciers should exist in the region. We hypothesise that the surge is at least partially controlled by subglacial topography, whereby a major subglacial overdeepening and constriction 3 km up-glacier of the terminus provides resistance to glacier flow from the accumulation area to the ablation area. This overdeepening appears to store mass until a threshold is crossed, after which the glacier flows out of the subglacial depression and rapidly surges over a bedrock lip and down the valley. Thus, whilst the surges are likely to be facilitated by subglacial processes (e.g. changes in subglacial hydrology and/or basal thermal regime), the topographic setting of the glacier appears to be modulating both the timing and duration of each surge.
A continent-wide search for Antarctic petrel breeding sites with satellite remote sensing Remote Sens. Environ. (IF 6.265) Pub Date : 2018-04-03 Mathew R. Schwaller, Heather J. Lynch, Arnaud Tarroux, Brandon Prehn
The Antarctic petrel (Thalassoica antarctica) has been identified as a key species for monitoring the status and health of the Southern Ocean and Antarctic ecosystems. Breeding colonies of the Antarctic petrel are often found on isolated nunataks far from inhabited stations, some up to hundreds of kilometers from the shoreline. It is difficult therefore to monitor and census known colonies, and it is believed that undiscovered breeding locations remain to be found. We developed an algorithm that can detect Antarctic petrel colonies and used it to complete a continent-wide survey using Landsat-8 Operational Line Imager (OLI) imagery in Antarctica up to the southernmost extent of Landsat's orbital view at 82.68°S. Our survey successfully identified 8 known Antarctic petrel colonies containing 86% of the known population of Antarctic petrels. The survey also identified what appears to be a significant population of breeding birds in areas not known to host breeding Antarctic petrel colonies. Our survey suggests that the breeding population at Mt. Biscoe (66°13′S 51°21′E), currently reported to be in the 1000s, may actually be on the order of 400,000 breeding pairs, which would make it the largest known Antarctic petrel breeding colony in the world. The algorithm represents a first-ever attempt to apply satellite remote sensing to assess the distribution and abundance of the Antarctic petrel on a continent-wide basis. As such, we note several algorithm shortcomings and identify research topics for algorithm improvement. Even with these caveats, our algorithm for identifying Antarctic petrel colonies with Landsat imagery demonstrates the feasibility of monitoring their populations using satellite remote sensing and identifies breeding locations, including Mt. Biscoe, that should be considered high priorities for validation with directed field surveys.
Mapping population density in China between 1990 and 2010 using remote sensing Remote Sens. Environ. (IF 6.265) Pub Date : 2018-03-29 Litao Wang, Shixin Wang, Yi Zhou, Wenliang Liu, Yanfang Hou, Jinfeng Zhu, Futao Wang
Knowledge of the spatial distribution of populations at finer spatial scales is of significant value and fundamental to many applications such as environmental change, urbanization, regional planning, public health, and disaster management. However, detailed assessment of the population distribution data of countries that have large populations (such as China) and significant variation in distribution requires improved data processing methods and spatialization models. This paper described the construction of a novel population spatialization method by combining land use/cover data and night-light data. Based on the analysis of data characteristics, the method used partial correlation analysis and geographically weighted regression to improve the distribution accuracy and reduce regional errors. China's census data for the years 1990, 2000, and 2010 were assessed. The results showed that the method was better at population spatialization than methods that use only night-light data or land use/cover data and global linear regression. Evaluation of overall accuracies revealed that the coefficient of correlation R-square was >0.90 and increased by >0.13 in the years 1990, 2000, and 2010. Moreover, the local R-square of over 90% of the samples (counties) was higher than the adjusted R-square of the general linear regression model. Furthermore, the gridded population density datasets obtained by this method can be used to analyse spatial-temporal patterns of population density and provide population distribution information with increased accuracy and precision compared to conventional models.
Measuring short-term post-fire forest recovery across a burn severity gradient in a mixed pine-oak forest using multi-sensor remote sensing techniques Remote Sens. Environ. (IF 6.265) Pub Date : 2018-03-29 Ran Meng, Jin Wu, Feng Zhao, Bruce D. Cook, Ryan P. Hanavan, Shawn P. Serbin
Understanding post-fire forest recovery is pivotal to the study of forest dynamics and global carbon cycle. Field-based studies indicated a convex response of forest recovery rate to burn severity at the individual tree level, related with fire-induced tree mortality; however, these findings were constrained in spatial/temporal extents, while not detectable by traditional optical remote sensing studies, largely attributing to the contaminated effect from understory recovery. Here, we examined whether the combined use of multi-sensor remote sensing techniques (i.e., 1 m simultaneous airborne imaging spectroscopy and LiDAR and 2 m satellite multi-spectral imagery) to separate canopy recovery from understory recovery would enable to quantify post-fire forest recovery rate spanning a large gradient in burn severity over large-scales. Our study was conducted in a mixed pine-oak forest in Long Island, NY, three years after a top-killing fire. Our studies remotely detected an initial increase and then decline of forest recovery rate to burn severity across the burned area, with a maximum canopy area-based recovery rate of 10% per year at moderate forest burn severity class. More intriguingly, such remotely detected convex relationships also held at species level, with pine trees being more resilient to high burn severity and having a higher maximum recovery rate (12% per year) than oak trees (4% per year). These results are one of the first quantitative evidences showing the effects of fire adaptive strategies on post-fire forest recovery, derived from relatively large spatial-temporal scales. Our study thus provides the methodological advance to link multi-sensor remote sensing techniques to monitor forest dynamics in a spatially explicit manner over large-scales, with important implications for fire-related forest management and constraining/benchmarking fire effect schemes in ecological process models.
Quantifying vulnerability of Antarctic ice shelves to hydrofracture using microwave scattering properties Remote Sens. Environ. (IF 6.265) Pub Date : 2018-03-30 K.E. Alley, T.A. Scambos, J.Z. Miller, D.G. Long, M. MacFerrin
Recent ice shelf disintegrations on the Antarctic Peninsula and subsequent increases in ice sheet mass loss have highlighted the importance of tracking ice shelf stability with respect to surface melt ponding and hydrofracture. In this study, we use active microwave scatterometry in time-series to estimate melt season duration, and winter backscatter levels as a proxy for relative concentration of refrozen ice lenses in Antarctic ice shelf firn. We demonstrate a physical relationship between melt days and firn/ice backscatter using scatterometry and field data from Greenland, and apply the observed relationship to derive and map a vulnerability index for Antarctica's ice shelves. The index reveals that some remaining Antarctic Peninsula ice shelves have already reached a firn state that is vulnerable to hydrofracture. We also show that the progression of an ice shelf towards vulnerability is affected by many factors, such as surface mass balance, internal stresses, and ice shelf geometry.
An algorithm for optically-deriving water depth from multispectral imagery in coral reef landscapes in the absence of ground-truth data Remote Sens. Environ. (IF 6.265) Pub Date : 2018-03-30 Jeremy M. Kerr, Sam Purkis
Although numerous approaches for deriving water depth from bands of remotely-sensed imagery in the visible spectrum exist, digital terrain models for remote tropical carbonate landscapes remain few in number. The paucity is due, in part, to the lack of in situ measurements of pertinent information needed to tune water depth derivation algorithms. In many cases, the collection of the needed ground-truth data is often prohibitively expensive or logistically infeasible. We present an approach for deriving water depths up to 15 m in Case 1 waters, whose inherent optical properties can be adequately described by phytoplankton, using multi-spectral satellite imagery without the need for direct measurement of water depth, bottom reflectance, or water column properties within the site of interest. The reliability of the approach for depths up to 15 m is demonstrated for ten satellite images over five study sites. For this depth range, overall RMSE values range from 0.89 m to 2.62 m when using a chlorophyll concentration equal to 0.2 mg m−3 and a generic seafloor spectrum generated from a spectral library of common benthic constituents. Accuracy of water depth predictions drastically decreases beyond these depths. Sensitivity analyses show that the model is robust to selection of bottom reflectance inputs and sensitive to parameterization of chlorophyll concentration.
A modified version of the kernel-driven model for correcting the diffuse light of ground multi-angular measurements Remote Sens. Environ. (IF 6.265) Pub Date : 2018-03-30 Yadong Dong, Ziti Jiao, Anxin Ding, Hu Zhang, Xiaoning Zhang, Yang Li, Dandan He, Siyang Yin, Lei Cui
When using the kernel-driven bidirectional reflectance distribution function (BRDF) model to process multi-angular measurements, the input multi-angular measurements must be corrected for atmospheric effects. However, in current databases, a significant number of ground-based multi-angular measurements contain either no corrections or only approximate corrections for atmospheric effects. Thus, the blended diffuse light in the total incident irradiance will result in considerable smoothing of the reflectance anisotropy retrieved by the kernel-driven model unless an atmospheric correction process is conducted. In this study, we propose a diffuse-light correction (DLC) form of the kernel-driven model that improves its ability to process multi-angular measurements blended with hemispherical diffuse light. The DLC form of the kernel-driven model can be used to retrieve the intrinsic reflectance anisotropy of the observed target from atmospheric-uncorrected multi-angular measurements. This study used multi-angular data simulated by the PROSAIL and Radiosity Applicable to Porous IndiviDual objects (RAPID) BRDF model, atmospheric-corrected Polarization and Directionality of the Earth's Reflectances (POLDER), Cloud Absorption Radiometer (CAR) multi-angular measurements and their simulated data based on the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) tools to validate the effectiveness of the DLC form of the kernel-driven model. The results indicated that the reflectance factors directly retrieved by the kernel-driven model are considerably smoothed by the blended diffuse light, especially in hotspot regions. Even under clear and cloudless sky conditions, the retrieved hotspot reflectance in the red band is still underestimated by an average of 9.25%, 7.72%, 11.0% and 13.8% for the PROSAIL, RAPID, POLDER and CAR data, respectively. In contrast, the hotspot reflectance retrieved by the DLC form of the kernel-driven model is very close to the intrinsic reflectance anisotropy of the targets; the average relative error of the DLC form of the kernel-driven model is only 1.99%, 1.50%, 4.57% and 3.42%, respectively. Although the reflectance reconstructed by the DLC form of the kernel-driven model in the hotspot region represents a considerable improvement compared with the reflectance retrieved by the original kernel-driven model, its improvement on the root mean square error (RMSE) and the bias of the entire datasets is not very apparent. Using the DLC form of the kernel-driven model can significantly improve the ability of the kernel-driven model to process multi-angular measurements blended with hemispherical diffuse irradiance.
Influence of reconstruction scale, spatial resolution and pixel spatial relationships on the sub-pixel mapping accuracy of a double-calculated spatial attraction model Remote Sens. Environ. (IF 6.265) Pub Date : 2018-03-30 Shangrong Wu, Jianqiang Ren, Zhongxin Chen, Wujun Jin, Xingren Liu, He Li, Haizhu Pan, Wenqian Guo
Mixed pixels universally exist in remote sensing images, and they are one of the main obstacles for further improving the accuracy of land cover recognition and classification. Since the concept of sub-pixel mapping (SPM) is proposed, SPM technology has rapidly become an important method to solve the problem of mixed pixels. To further improve SPM accuracy, this paper first proposes a double-calculated spatial attraction model (DSAM) combining the advantages of the spatial attraction model (SAM) and the pixel swap model (PSM). Then, based on the full validation of the proposed DSAM, how multiple factors affect the SPM accuracy is analyzed using the multispectral remote sensing (MRS) images. Finally, by analyzing the maximum variations in the ranges of the overall accuracy and the kappa coefficient under different multiple factors, the order of factors influencing SPM accuracy is determined as follows: reconstruction scale > image spatial resolution > pixel spatial relationships. The results can serve as a reference for other scholars in setting model parameters and selecting the appropriate remote sensing data, thereby helping them achieve more accurate SPM results.
Spatially-explicit monitoring of crop photosynthetic capacity through the use of space-based chlorophyll fluorescence data Remote Sens. Environ. (IF 6.265) Pub Date : 2018-03-30 Yongguang Zhang, Luis Guanter, Joanna Joiner, Lian Song, Kaiyu Guan
Plant functional traits such as photosynthetic capacity are critical parameters for terrestrial biosphere models. However, their spatial and temporal characteristics are still poorly represented. In this study, we used satellite observations of sun-induced fluorescence (SIF) to estimate top-of-canopy photosynthetic capacity (maximum carboxylation rate, Vcmax at a reference temperature of 25 °C) for crops, which was in turn utilized to simulate regional gross primary production (GPP). We first estimate the key parameter, Vcmax, in the widely-used FvCB photosynthesis model using field measurements of CO2 and water fluxes during 2007–2012 at seven crop eddy covariance flux sites over the US Corn Belt. The results showed that satellite far-red SIF retrievals have a stronger link to Vcmax at the seasonal scale (R2 = 0.70 for C4 and R2 = 0.63 for C3 crop) as compared with widely-used vegetation indices. We calibrate an empirical model linking Vcmax with SIF that was used to estimate spatially and temporally varying crop Vcmax for the US Corn Belt region. The resulting Vcmax maps are used together with meteorological data from MERRA reanalysis data and vegetation structural parameters derived from the satellite-based spectral reflectance data to constrain the Soil-Canopy Observation of Photosynthesis and Energy (SCOPE) balance model in order to estimate regional crop GPP. Our results show a substantial improvement in the seasonal and spatial patterns of cropland GPP when compared with crop yield inventory data. The evaluation with tall tower atmospheric CO2 measurements further supports our estimation of spatiotemporal Vcmax from space-borne SIF. Considering that SIF has a direct link to photosynthetic activity, our findings highlight the potential to infer regional Vcmax using remotely sensed SIF data and to use this information for a better quantification of regional cropland carbon cycles.
Particle size effects on soil reflectance explained by an analytical radiative transfer model Remote Sens. Environ. (IF 6.265) Pub Date : 2018-03-30 Morteza Sadeghi, Ebrahim Babaeian, Markus Tuller, Scott B. Jones
Experimental evidence points to an intimate link between soil reflectance, R, and particle/aggregate diameter, D. Based on this strong correlation, various statistical methods for remote and proximal sensing of soil texture and hydraulic properties have been developed. In this paper, we derive a more fundamental and physically-based analytical radiative transfer model that yields a closed-form functional R(D) relationship for dry soils. Despite several simplifying assumptions, the proposed model shows good agreement with measured spectral reflectance (350–2500 nm) data of six soils covering a broad range of textures, colors, and mineralogies. The proposed S-shaped R(D) function resembles cumulative particle and pore size distributions as well as the soil water characteristic function. These analogies may potentially lead to new avenues for developing novel physical models for extracting important soil properties from remotely sensed reflectance data.
Field-scale mapping of evaporative stress indicators of crop yield: An application over Mead, NE, USA Remote Sens. Environ. (IF 6.265) Pub Date : 2018-03-30 Yang Yang, Martha C. Anderson, Feng Gao, Brian Wardlow, Christopher R. Hain, Jason A. Otkin, Joseph Alfieri, Yun Yang, Liang Sun, Wayne Dulaney
The Evaporative Stress Index (ESI) quantifies temporal anomalies in a normalized evapotranspiration (ET) metric describing the ratio of actual-to-reference ET (fRET) as derived from satellite remote sensing. At regional scales (3–10 km pixel resolution), the ESI has demonstrated the capacity to capture developing crop stress and impacts on regional yield variability in water-limited agricultural regions. However, its performance in some regions where the vegetation cycle is intensively managed appears to be degraded due to spatial and temporal limitations in the standard ESI products. In this study, we investigated potential improvements to ESI by generating maps of ET, fRET, and fRET anomalies at high spatiotemporal resolution (30-m pixels, daily time steps) using a multi-sensor data fusion method, enabling separation of landcover types with different phenologies and resilience to drought. The study was conducted for the period 2010–2014 covering a region around Mead, Nebraska that includes both rainfed and irrigated crops. Correlations between ESI and measurements of maize yield were investigated at both the field and county level to assess the potential of ESI as a yield forecasting tool. To examine the role of crop phenology in yield-ESI correlations, annual input fRET time series were aligned by both calendar day and by biophysically relevant dates (e.g. days since planting or emergence). At the resolution of the operational U.S. ESI product (4 km), adjusting fRET alignment to a regionally reported emergence date prior to anomaly computation improves r2 correlations with county-level yield estimates from 0.28 to 0.80. At 30-m resolution, where pure maize pixels can be isolated from other crops and landcover types, county-level yield correlations improved from 0.47 to 0.93 when aligning fRET by emergence date rather than calendar date. Peak correlations occurred 68 days after emergence, corresponding to the silking stage for maize when grain development is particularly sensitive to soil moisture deficiencies. The results of this study demonstrate the utility of remotely sensed ET in conveying spatially and temporally explicit water stress information to yield prediction and crop simulation models.
The impacts of spatial baseline on forest canopy height model and digital terrain model retrieval using P-band PolInSAR data Remote Sens. Environ. (IF 6.265) Pub Date : 2018-03-30 Zhanmang Liao, Binbin He, Albert I.J.M. van Dijk, Xiaojing Bai, Xingwen Quan
Polarimetric Synthetic Aperture Radar Interferometry (PolInSAR) has shown potential for the retrieval of a forest canopy height model (CHM) and the underlying solid earth digital terrain model (DTM). However, because of non-volume decorrelation and other unavoidable errors, the robustness of retrieval heights is sensitive to the spatial baseline of the selected InSAR pairs, which relates forest parameters to measured coherence. Within the context of the random volume over ground (RVoG) model and the three-stage inversion method, we aimed to quantify the influence of spatial baseline on the inversions at P-band, which are distinct from the inversions at higher frequency due to the non-negligible ground contributions. This information assists in optimal baseline selection and the development of robust inversion schemes. Assumptions about the extinction coefficient and additional DTM or DEM were used to reduce the influence of ground contribution on CHM and DTM inversion, respectively. Inversions from published airborne repeat-pass P-band PolInSAR data with four different spatial baselines were validated against LiDAR-derived DTM and CHM data. The results show that a longer spatial baseline performed better in DTM inversion. The longest baseline produced the best R2 of 0.995 and RMSE of 0.555 m, much better than the smallest baseline with an R2 of 0.794 and RMSE of 3.74 m. A threshold height could be identified that determines the overestimation and underestimation of CHM inversion due to the non-volume decorrelation. Different baselines produced different threshold heights, making CHM inversion only accurate for a limited range of forest height around the threshold. The optimal baseline produced a CHM with R2 of 0.605 and RMSE of 2.67 m. Additionally, we found that using multiple baselines has the potential to improve CHM inversion, improving the R2 to 0.827 and RMSE to 0.876 m in our study.
Drivers of spatial variability in greendown within an oak-hickory forest landscape Remote Sens. Environ. (IF 6.265) Pub Date : 2018-03-30 V.C. Reaves, A.J. Elmore, D.M. Nelson, B.E. McNeil
Declining near-infrared (NIR) surface reflectance between early and late summer, here termed greendown, is a common, yet poorly understood phenomena in remote sensing time series of temperate deciduous forests. As revealed by phenology analysis of Landsat satellite data, there are strong spatial patterns in the rate of greendown across temperate deciduous forest landscapes, and analyzing these patterns could help advance our understanding of surface reflectance drivers. Within an oak-hickory (Quercus spp. – Carya spp.) forest landscape in western Maryland, USA, we tested how spatial patterns in greendown related to potential drivers at the landscape-, tree crown- and leaf-levels. We found that 50% of the spatial variability in greendown was explained by landscape variables, with greendown particularly higher in locations with higher maximum greenness, more southerly aspects, or locations with greater abundance of white oak (Quercus alba). The importance of species composition as a driver of greendown was supported at the tree crown level, where, relative to three other tree species, white oak exhibited the most consistent trend toward more vertical leaf angles later in the season. At the leaf level, NIR reflectance decreased in productive sites where %N increased, and δ13C decreased, through the season. However, among all sites, there were no consistent seasonal trends in foliar NIR reflectance, and we found no correlation among leaf-level NIR reflectance and satellite-observed greendown. Collectively, these results suggest that the spatial variability of greendown in this oak-hickory forest is most strongly controlled by an interaction of topographic and species compositional drivers operating at the landscape and tree crown levels. We found spatial analysis of greendown to be a useful approach to explore landscape-, tree crown-, and leaf-level controls on surface reflectance, and thereby help translate land surface phenology data into predictions of ecosystem structure and functioning.
Land cover and land use change analysis using multi-spatial resolution data and object-based image analysis Remote Sens. Environ. (IF 6.265) Pub Date : 2018-03-29 Sory I. Toure, Douglas A. Stow, Hsiao-chien Shih, John Weeks, David Lopez-Carr
Remote sensing data and techniques are reliable tools for monitoring and studying urban land cover and land use (LCLU) change. Fine spatial resolution (FRes) commercial satellite image in conjunction with geographic object-based image change analysis (GEOBICA) methods have been used to generate detailed and accurate urban LCLU maps. The integration of a backdating approach improves LCLU change classification results for the first date of a bi-temporal image sequences. Conversely, moderate spatial resolution satellite images such as those from Landsat sensors may not allow for detailed urban land use and land cover mapping. The objective of this study is to test a new bi-temporal change identification approach that integrates image classification of fine spatial resolution satellite imagery at time-2 and moderate spatial resolution satellite imagery (Landsat) at time-1, in a backdating and GEOBICA framework for mapping urban land use change. We compare the results from this approach to those of a GEOBICA approach based on fine spatial resolution imagery in both periods. The overall accuracy of the time-1 Landsat image classification is 0.82 and that of the fine spatial resolution image is 0.87. Moreover, the overall accuracy of the areal change data estimated from the pixel-wise spatial overlay of bi-temporal FRes LCLU maps is 0.80 while that from overlaying a time-2 FRes-generated map to that from a Landsat time-1 image is 0.81. The proposed method can be used in areas that lack FRes data due to limited coverage in the early 2000s.
A method for combining SRTM DEM and ASTER GDEM2 to improve topography estimation in regions without reference data Remote Sens. Environ. (IF 6.265) Pub Date : 2018-03-23 Hung T. Pham, Lucy Marshall, Fiona Johnson, Ashish Sharma
Digital Elevation Models (DEMs) such as Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Models (ASTER GDEM), or Shuttle Radar Topography Mission DEM (SRTM) are widely used in remote areas and non-industrial countries because of their availability rather than their accuracy. Although a global DEM can be considerably enhanced using additional reference information such as higher resolution DEMs or ground truth points, improving accuracy in areas without reference data remains a challenge. This paper develops an approach to improve the accuracy of the estimated topography by combining two complementary DEMs (ASTER GDEM 1 arc-second and SRTM DEM 1 arc-second) in regions missing reference data. The combination approach is based on formulating relationships between slopes and weights in sites with reference data. Then the developed relationships are applied to sites with similar geomorphology to determine the combination weight for each DEM without using reference data. The results indicate that combined DEMs offer significant improvements of 47% and 20% in mean bias over a mountainous site, and 16% and 58% at a low-relief site when compared with the SRTM and ASTER GDEM products, respectively. DEM-derived drainages were also found to be more accurate for the combined DEMs than the near-global DEMs in areas where reference data is not available.
Deblurring DMSP nighttime lights: A new method using Gaussian filters and frequencies of illumination Remote Sens. Environ. (IF 6.265) Pub Date : 2018-03-23 Alexei Abrahams, Christopher Oram, Nancy Lozano-Gracia
A well known difficulty with the Defense Meteorological Satellite Program's nighttime lights series (DMSP-NTL 1992–2012) is that the images suffer from pervasive blurring, dubbed ‘overglow’ or ‘blooming’. In this paper we devise a new method that significantly mitigates blurring. We assemble a sample of isolated light sources around the globe and discover that blurring is governed by a symmetric Gaussian point-spread function (PSF), but that the brightness of sources widens the PSF. To make sense of this, we recreate step-by-step the satellite's data collection and storage process, and discover an important fact: any pixel containing a light source will tend to be lit at least as often as its neighbors. This regularity provides a second filter on the data that allows us to calibrate the dimensions of the PSF to each part of the globe, each satellite, and each year. We generate a user-friendly, open-access MATLAB script that deblurs all DMSP-NTL images for all years, and we showcase the enhanced images for a sample of locations around the globe.
Modeling the precision of structure-from-motion multi-view stereo digital elevation models from repeated close-range aerial surveys Remote Sens. Environ. (IF 6.265) Pub Date : 2018-03-22 Jason Goetz, Alexander Brenning, Marco Marcer, Xavier Bodin
The accuracy of digital elevation models (DEMs) derived from structure-from-motion (SFM) multi-view stereo (MVS) 3D reconstruction is commonly computed for a single realization of model elevations. This approach may be adequate to estimate an overall measure of systematic error; however, it cannot provide a good estimation of measurement precision. Knowing measurement precision is crucial for measuring elevation surface changes observed by DEM comparisons. In this paper, we illustrate an approach to characterize spatial variation in the precision for SFM-MVS derived DEMs. We use a snow-covered surface of an active rock glacier located in the southern French Alps as the case study. A spatially varying precision estimate is calculated from repeated close-range aerial surveys for a single acquisition period by calculating the standard deviation per grid cell between the DEMs created for each flight repetition. Regression analysis using a generalized additive model (GAM) is performed to model the estimated precision and provide insights regarding how sensor, survey design and field site conditions may spatially influence the measurement precision. Additionally, we define how DEM error can be described differently depending on the available validation data. In our study image height above ground level and distance to ground control points had the greatest explanatory power for spatial variation in DEM precision. Image overlap, mean reprojection error and saturation were also useful for explaining spatially varying measurement precision of the DEMs. Field site characteristics, such as slope angle and shading, had the least importance in our model of precision. From a practical point of view, regression-modeled relationships between precision and image and site characteristics can be utilized to design future surveys with similar sensing platforms and site conditions for improved DEM precision.
Mapping patterns of urban development in Ouagadougou, Burkina Faso, using machine learning regression modeling with bi-seasonal Landsat time series Remote Sens. Environ. (IF 6.265) Pub Date : 2018-03-22 Franz Schug, Akpona Okujeni, Janine Hauer, Patrick Hostert, Jonas Ø. Nielsen, Sebastian van der Linden
Rapid urban population growth in Sub-Saharan Western Africa has important environmental, infrastructural and social impacts. Due to the low availability of reliable urbanization data, remote sensing techniques become increasingly popular for monitoring land use change processes in that region. This study aims to quantify land cover for the Ouagadougou metropolitan area between 2002 and 2013 using a Landsat-TM/ETM+/OLI time series. We use a support vector regression approach and synthetically mixed training data. Working with bi-seasonal image stacks, we account for spectral variability between dry and rainy season and incorporate a new class - seasonal vegetation - that describes surfaces that are soil and vegetation during parts of the year. We produce fraction images of urban surfaces, soil, permanent vegetation and seasonal vegetation for each time step. Statistical evaluation shows that a temporally generalized, bi-seasonal model over all time steps performs equally or better than yearly or mono-seasonal models and provides reliable cover fractions. Urban fractions can be used to visualize pixel-based spatial-temporal patterns of urban densification and expansion. A simple rule set based on a seasonal vegetation to soil ratio is appropriate to delineate areas of unplanned and planned settlements and, thus, contributes to monitoring urban development on a neighborhood scale.
Calibration of nationwide airborne laser scanning based stem volume models Remote Sens. Environ. (IF 6.265) Pub Date : 2018-03-21 Eetu Kotivuori, Matti Maltamo, Lauri Korhonen, Petteri Packalen
In-situ field measurements of sample plots are a major cost component in airborne laser scanning (ALS) based forest inventories. Field measurements on new inventory areas can be reduced by utilizing existing stand attribute models from former inventory areas. We constructed a nationwide model for stem volume, and examined seven different calibration scenarios using 22 inventory areas distributed evenly throughout Finland. These scenarios can be divided into three main categories: 1) calibration with additional predictor variables, 2) calibration with 200 geographically nearest sample plots, and 3) calibration with MS-NFI (Multi-source National Forest Inventory of Finland) volume at the target inventory area. Calibration with degree days, precipitation, and proportion of birch resulted in the greatest decrease in error rate of stem volume predictions. The mean of the root mean square errors (RMSE) among the 22 inventory areas decreased from 28.6% to 25.9%, and the standard deviation of RMSEs from 4.3% to 3.9% using three additional predictor variables. Correspondingly, the mean and standard deviation of absolute values of mean differences (|MD|) decreased from 8.3% to 5.6% and from 5.6% to 4.4%, respectively. All calibration scenarios decreased the error rate, especially the high |MDs| observed in the northern part of Finland. Calibration with sample plots from geographically nearest inventory areas was useful when there were sample plots that offered a good representation of the target area. MS-NFI based calibration was also a reasonable option if loggings and other inconsistencies between datasets could be detected and accounted for.
Improved mapping of forest type using spectral-temporal Landsat features Remote Sens. Environ. (IF 6.265) Pub Date : 2018-03-22 Valerie J. Pasquarella, Christopher E. Holden, Curtis E. Woodcock
Multi-spectral imagery from the Landsat family of satellites has been used to map forest properties for decades, but accurate forest type characterizations at a 30-m Landsat resolution have remained an ongoing challenge, especially over large areas. We combined existing Landsat time series algorithms to quantify both harmonic and phenological metrics in a new set of spectral-temporal features that can be produced seamlessly across many Landsat scenes. Harmonic metrics characterize mean annual reflectance and seasonal variability, while phenological metrics quantify the timing of seasonal events. We assessed the performance of spectral-temporal features derived from time series of all available observations (1985–2015) relative to more conventional single date and multi-date inputs. Performance was determined based on agreement with a reference dataset for eight New England forest types at both the pixel and polygon scale. We found that spectral-temporal features consistently and significantly (paired t-test, p ≪ 0.01) outperformed all feature sets derived from individual images and multi-date combinations in all measures of agreement considered. Harmonic features, such as annual amplitude and model fit error, aid in distinguishing deciduous hardwoods from conifer species, while phenology features, like the timing of autumn onset and growing season length, were useful in separating hardwood classes. This study represents an important step toward large-scale forest type mapping using spectral-temporal Landsat features by providing a quantitative assessment of the advantages of harmonic and phenology features derived from time series of Landsat data as compared with more conventional single-date and multi-date classification inputs.
NASA's Black Marble nighttime lights product suite Remote Sens. Environ. (IF 6.265) Pub Date : 2018-03-19 Miguel O. Román, Zhuosen Wang, Qingsong Sun, Virginia Kalb, Steven D. Miller, Andrew Molthan, Lori Schultz, Jordan Bell, Eleanor C. Stokes, Bhartendu Pandey, Karen C. Seto, Dorothy Hall, Tomohiro Oda, Robert E. Wolfe, Gary Lin, Navid Golpayegani, Sadashiva Devadiga, Carol Davidson, Sudipta Sarkar, Cid Praderas, Jeffrey Schmaltz, Ryan Boller, Joshua Stevens, Olga M. Ramos González, Elizabeth Padilla, José Alonso, Yasmín Detrés, Roy Armstrong, Ismael Miranda, Yasmín Conte, Nitza Marrero, Kytt MacManus, Thomas Esch, Edward J. Masuoka
NASA's Black Marble nighttime lights product suite (VNP46) is available at 500 m resolution since January 2012 with data from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) onboard the Suomi National Polar-orbiting Platform (SNPP). The retrieval algorithm, developed and implemented for routine global processing at NASA's Land Science Investigator-led Processing System (SIPS), utilizes all high-quality, cloud-free, atmospheric-, terrain-, vegetation-, snow-, lunar-, and stray light-corrected radiances to estimate daily nighttime lights (NTL) and other intrinsic surface optical properties. Key algorithm enhancements include: (1) lunar irradiance modeling to resolve non-linear changes in phase and libration; (2) vector radiative transfer and lunar bidirectional surface anisotropic reflectance modeling to correct for atmospheric and BRDF effects; (3) geometric-optical and canopy radiative transfer modeling to account for seasonal variations in NTL; and (4) temporal gap-filling to reduce persistent data gaps. Extensive benchmark tests at representative spatial and temporal scales were conducted on the VNP46 time series record to characterize the uncertainties stemming from upstream data sources. Initial validation results are presented together with example case studies illustrating the scientific utility of the products. This includes an evaluation of temporal patterns of NTL dynamics associated with urbanization, socioeconomic variability, cultural characteristics, and displaced populations affected by conflict. Current and planned activities under the Group on Earth Observations (GEO) Human Planet Initiative are aimed at evaluating the products at different geographic locations and time periods representing the full range of retrieval conditions.
Inversion of deformation fields time-series from optical images, and application to the long term kinematics of slow-moving landslides in Peru Remote Sens. Environ. (IF 6.265) Pub Date : 2018-03-20 Noélie Bontemps, Pascal Lacroix, Marie-Pierre Doin
Slow-moving landslides are numerous in mountainous areas and pose a large threat to populations. Many observations show that their kinematics is driven by climatic forcings and earthquakes. In this study, we document the complex interaction between those two forcings on the slow-moving landslide kinematics, based on the retrieval of landslide displacements over 28-years using optical satellite images. To overcome the decorrelation effect over this large time-span, and possible misalignment between images, we develop a method that uses the redundancy of displacement fields from image pairs to derive a robust time-series of displacement. The method is tested on the 28-year long SPOT1/5-Pléiades archive, over an area in Peru affected by both earthquakes and rainfall. Errors are estimated on stable areas and by comparison with one 13-year long and eleven 3-year long GPS time-series on the Maca landslide. The methodology diminishes by up to 30% the uncertainty and reduces significantly the gaps due to decorrelation. The data set allows detecting 3 major landslides, moving at a rate of 35 to 50 m over 28 years, and smaller landslides with lower displacement rates. Time-series obtained over the three main landslides provide interesting results of their long-term kinematics, primarily driven by precipitation. We propose simple statistical hydro-kinematic models, relating yearly motion to seasonal rainfall, to explain the observed time-series. We found that annual precipitation is controlling the landslide displacements after a certain rainfall threshold is reached. Besides this control, we show the possible impact of a local Mw 5.4 earthquake in 1991 on the kinematics of the Maca landslide. Our results suggest that the earthquake accelerated the landslide and has an effect during several years on the precipitation threshold required for triggering a motion. These results suggest that the rainfall threshold can vary in time following strong earthquakes shaking.
Influences of multiple layers of air temperature differences on tidal forces and tectonic stress before, during and after the Jiujiang earthquake Remote Sens. Environ. (IF 6.265) Pub Date : 2018-03-20 Ma Weiyu, Zhang Xuedong, Jun Liu, Qi Yao, Bo Zhou, Chong Yue, Chunli Kang, Xian Lu
Using the air temperature data of the National Center for Environmental Prediction (NCEP), we compared multiple layers of air temperature differences before, during and after the Jiujiang earthquake, and explored its relationship with the additive tectonic stress caused by celestial tide-generating force (ATSCTF). The earthquake occurred at the 1 of 4 high phases of ATSCTF, while the temperature rise came from land surface to high sky. It indicated that the tide force could trigger an earthquake when the tectonic stress was in critical status, and the air temperature rise reflected the terra stress change modulated under the tidal force. During the shock period of ATSCTF, the distribution of air temperature changes both near land surfaces and upper multi-layers along the active fault zones showed a tectonic disturbance pattern of calm before earthquake, rise during earthquake, calm after earthquake as well as a heat distribution pattern of the surface air warmed by land, uplifted by heat flux, cooled and dissipated in the sky. The pattern of changes obeyed the rule of thermal rise of rocks broken under stress loading and the principle of atmospheric thermal dynamic diffusion in vertical. We argued that an earthquake may also be a reason for air temperature differences rather than a simple weather process. At the same time, the rise of air temperature was synchronized with the ATSCTF fluctuant, which showed that tidal force had a particular indicative significance for the identification of temperature anomaly on seismic faults. Because of the mechanical characteristics of the study of earthquake thermal anomalies, it could help to identify the earthquake thermal anomalies and the climatic thermal anomalies, and provided a clear time-indication for the choice of the background temperature in the seismic thermal anomaly recognition.
Earthquake damage mapping: An overall assessment of ground surveys and VHR image change detection after L'Aquila 2009 earthquake Remote Sens. Environ. (IF 6.265) Pub Date : 2018-03-20 Roberta Anniballe, Fabrizio Noto, Tanya Scalia, Christian Bignami, Salvatore Stramondo, Marco Chini, Nazzareno Pierdicca
Earth Observation (EO) data are used to map mostly affected urban areas after an earthquake generally exploiting change detection techniques applied at pixel scale. However, Civil Protection Services require damage assessment of each building according to a well-established scale to manage rescue operations and to estimate the economic losses. Considering the earthquake that hit L'Aquila city (Italy) on April 6, 2009, this work assess the feasibility of producing damage maps at the scale of single building from Very High Resolution (VHR) optical images collected before and after the seismic event. We considered the European Macroseismic Scale 1998 (EMS-98) and assessed the possibility to discriminate between collapsed or heavy damaged buildings (damage grade DG equal to 5 in the EMS-98 scale) and less damaged or undamaged buildings (DG < 5 in the EMS-98). The proposed approach relies on a pre-existing urban map to identify image objects corresponding to building footprints. The image analysis is carried out according to many different parameters with the objective of assessing their effectiveness in singling out changes associated to the building collapse. Features describing texture and colour changes, as well statistical similarity and correlation descriptors, such as the Kullbach Leibler Distance and the Mutual Information, were included in our analysis. Two supervised classification approaches, respectively, based on the use of the Bayesian Maximum A Posteriori (MAP) criterion and on Support Vector Machines (SVM), were compared. In our experiment, we considered the whole L'Aquila historical centre comparing classification results with the ground survey performed by the Istituto Nazionale di Geofisica e Vulcanologia (INGV). The work represents one of the first attempt to detect damage at the scale of single building, validated against an extensive ground survey. It addresses methodological aspects, highlighting the potential of textural features computed at object scale and SVMs, and discuss potential and limitations of EO in this field compared to ground surveys.
How far are we from the use of satellite rainfall products in landslide forecasting? Remote Sens. Environ. (IF 6.265) Pub Date : 2018-03-17 M.T. Brunetti, M. Melillo, S. Peruccacci, L. Ciabatta, L. Brocca
Satellite rainfall products have been available for many years (since '90) with an increasing spatial/temporal resolution and accuracy. Their global scale coverage and near real-time products perfectly fit the need of an early warning landslide system. Notwithstanding these characteristics, the number of studies employing satellite rainfall estimates for predicting landslide events is quite limited. In this study, we propose a procedure that allows us to evaluate the capability of different rainfall products to forecast the spatial-temporal occurrence of rainfall-induced landslides using rainfall thresholds. Specifically, the assessment is carried out in terms of skill scores, and receiver operating characteristic (ROC) analysis. The procedure is applied to ground observations and four different satellite rainfall estimates: 1) the Tropical Rainfall Measurement Mission Multi-satellite Precipitation Analysis, TMPA, real time product (3B42-RT), 2) the SM2RASC product obtained from the application of SM2RAIN algorithm to the Advanced SCATterometer (ASCAT) derived satellite soil moisture (SM) data, 3) the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network (PERSIANN), and 4) the Climate Prediction Center (CPC) Morphing Technique (CMORPH). As case study, we consider the Italian territory for which a catalogue listing 1414 rainfall-induced landslides in the period 2008–2014 is available. Results show that satellite products underestimate rainfall with respect to ground observations. However, by adjusting the rainfall thresholds, satellite products are able to identify landslide occurrence, even though with less accuracy than ground-based rainfall observations. Among the four satellite rainfall products, CMORPH and SM2RASC are performing the best, even though differences are small. This result is to be attributed to the high spatial/temporal resolution of CMORPH, and the good accuracy of SM2RSC. Overall, we believe that satellite rainfall estimates might be an important additional data source for developing continental or global landslide warning systems.
Retrievals of cloud droplet size from the research scanning polarimeter data: Validation using in situ measurements Remote Sens. Environ. (IF 6.265) Pub Date : 2018-03-17 Mikhail D. Alexandrov, Brian Cairns, Kenneth Sinclair, Andrzej P. Wasilewski, Luke Ziemba, Ewan Crosbie, Richard Moore, John Hair, Amy Jo Scarino, Yongxiang Hu, Snorre Stamnes, Michael A. Shook, Gao Chen
We present comparisons of cloud droplet size distributions (DSDs) retrieved from the research scanning polarimeter (RSP) data with correlative in situ measurements made during the North Atlantic Aerosols and Marine Ecosystems Study (NAAMES). The airborne portion of this field experiment was based out of St. John's airport, Newfoundland, Canada with the focus of this paper being on the deployment in May–June 2016. RSP was onboard the NASA C-130 aircraft together with an array of in situ and other remote sensing instrumentation. The RSP is an along-track scanner measuring the polarized and total reflectance in 9 spectral channels. Its uniquely high angular resolution allows for characterization of liquid water droplet sizes using the rainbow structure observed in the polarized reflectance over the scattering angle range from 135° to 165°. The rainbow is dominated by single scattering of light by cloud droplets, so its structure is characteristic specifically of the droplet sizes at cloud top (within unit optical depth into the cloud, equivalent to approximately 50 m). A parametric fitting algorithm applied to the polarized reflectance provides retrievals of the droplet effective radius and variance assuming a prescribed size distribution shape (gamma distribution). In addition to this, we use a non-parametric method, the Rainbow Fourier Transform (RFT), which allows us to retrieve the droplet size distribution itself. The latter is important in the case of clouds with complex microphysical structure, or multiple layers of cloud, which result in multi-modal DSDs. During NAAMES the aircraft performed a number of flight patterns specifically designed for comparisons between remote sensing retrievals and in situ measurements. These patterns consisted of two flight segments above the same straight ground track. One of these segments was flown above clouds allowing for remote sensing measurements, while the other was near the cloud top where cloud droplets were sampled. We compare the DSDs retrieved from the RSP data with in situ measurements made by the Cloud Droplet Probe (CDP). The comparisons generally show good agreement (better than 1 μm for effective radius and in most cases better than 0.02 for effective variance) with deviations explainable by the position of the aircraft within the cloud, or by the presence of additional cloud layers between the cloud being sampled by the in situ instrumentation and the altitude of the remote sensing segment. In the latter case, the multi-modal DSDs retrieved from the RSP data were consistent with the multi-layer cloud structures observed in the correlative High Spectral Resolution Lidar (HSRL) profiles. The results of these comparisons provide a rare validation of polarimetric droplet size retrieval techniques, demonstrating their accuracy and robustness and the potential of satellite data of this kind on a global scale.
Glacier mass balance in the Qinghai–Tibet Plateau and its surroundings from the mid-1970s to 2000 based on Hexagon KH-9 and SRTM DEMs Remote Sens. Environ. (IF 6.265) Pub Date : 2018-03-19 Yushan Zhou, Zhiwei Li, Jia Li, Rong Zhao, Xiaoli Ding
In the context of global warming, glacier changes in the Qinghai–Tibet Plateau (QTP) and its surroundings have attracted a great amount of public attention. To date, there have been many studies of glacier mass balance across the QTP. However, given that most of the previous studies have focused on a short observation period (2000–2015), and that long-term mass change measurements are available only for some local regions, we utilized declassified KH-9 images and 1 arc-second Shuttle Radar Topography Mission (SRTM) digital elevation models (DEMs) to provide the region-wide mass balance (from the mid-1970s to 2000) for a larger scale (including 11 sample regions) across the QTP and its surroundings. The final results indicate that the glaciers in the northwest of the QTP have shown a less negative or near-zero mass balance, ranging from −0.11 ± 0.13 m w.e. a−1 to 0.02 ± 0.10 m w.e. a−1, compared to those in the southeast part, with a mass balance range of −0.30 ± 0.12 m w.e. a−1 to −0.11 ± 0.14 m w.e. a−1. The most serious mass loss has emerged in the central-eastern Himalaya. Integrating our results with the observations after 2000 suggests that, over the past four decades (mid-1970s to the mid-2010s), the glaciers in the Himalaya, Nyainqêntanglha, and Tanggula mountains, as a whole, have exhibited accelerated mass loss, and the most significant acceleration has occurred in the eastern Nyainqêntanglha. Moreover, the Hindu Raj glaciers have shown a stable rate of continuous mass loss, while a nearly stable or slight mass gain state in the western Kunlun region can be dated back to at least as far as the mid-1970s.
Validation of Jason-3 tracking modes over French rivers Remote Sens. Environ. (IF 6.265) Pub Date : 2018-03-19 Sylvain Biancamaria, Thomas Schaedele, Denis Blumstein, Frédéric Frappart, François Boy, Jean-Damien Desjonquères, Claire Pottier, Fabien Blarel, Fernando Niño
Satellite nadir radar altimeters have been widely used to measure river and lake surface water elevations. They can now retrieve the elevations of some rivers <200 m wide. However, as these satellite missions are primarily designed to observe ocean surface topography, they are not always able to observe continental surfaces. For steep-sided rivers (i.e. in river valleys no more than a few km wide and surrounded by slopes over 50 m high), altimeters tend to observe the top of the surrounding topography rather than the river itself. The Jason-3 altimetry mission, launched in January 2016, has an alternative instrument operation mode, the so called Open-Loop (OL) or Digital Elevation Model (DEM) tracking mode. This mode is intended to help overcome this issue, by using an on-board DEM. However it was not used in 2016 as the operational mode because of difficulties in defining an accurate on-board global-scale DEM. Mainland France has been chosen to test the OL tracking mode, as water masks and DEMs of sufficient accuracy are available. Following the launch of Jason-3, Jason-2 (its predecessor) was maintained on the same nominal orbit as its follow-on, for more than 6 months. During this tandem period, data from the first 10 Jason-3 cycles (a Jason-2/-3 cycle corresponds to 10 days) were acquired in the traditional Closed-Loop (CL) tracking mode. Jason-3 data from the last 13 cycles were acquired in OL tracking mode. Jason-2 was always in CL tracking mode. Compared to nearby in situ gages and for river wider than 100 m, Jason-3 water elevation anomalies have a RMSE between 0.20 and 0.30 m for most reaches. Jason-3 performance over narrow rivers is similar to that of Jason-2. In CL tracking mode, Jason-3 altimeter tends to be locked over the surrounding topography more frequently than Jason-2 (due to the specific post-launch Jason-2 altimeter tuning). This study shows that Jason-2 observed 60% of river reaches studied (48 of 86 reaches), whereas Jason-3 in OL tracking mode was able to measure all river reaches for every cycle. This result clearly highlights the significant advantages of the OL tracking mode for observation of steep-sided rivers. However, further investigations are required to compute an accurate on-board global-scale DEM and to determine those locations where the use of OL tracking mode is or is not appropriate.
Large-area mapping of Canadian boreal forest cover, height, biomass and other structural attributes using Landsat composites and lidar plots Remote Sens. Environ. (IF 6.265) Pub Date : 2018-03-19 Giona Matasci, Txomin Hermosilla, Michael A. Wulder, Joanne C. White, Nicholas C. Coops, Geordie W. Hobart, Harold S.J. Zald
Passive optical remotely sensed images such as those from the Landsat satellites enable the development of spatially comprehensive, well-calibrated reflectance measures that support large-area mapping. In recent years, as an alternative to field plot data, the use of Light Detection and Ranging (lidar) acquisitions for calibration and validation purposes in combination with such satellite reflectance data to model a range of forest structural response variables has become well established. In this research, we use a predictive modeling approach to map forest structural attributes over the ~ 552 million ha boreal forest of Canada. For model calibration and independent validation we utilize airborne lidar-derived measurements of forest vertical structure (known as lidar plots) obtained in 2010 via a > 25,000 km transect-based national survey. Models were developed linking the lidar plot structural variables to wall-to-wall 30-m spatial resolution surface reflectance composites derived from Landsat Thematic Mapper and Enhanced Thematic Mapper Plus imagery. Spectral indices extracted from the composites, disturbance information (years since disturbance and type), as well as geographic position and topographic variables (i.e., elevation, slope, radiation, etc.) were considered as predictor variables. A nearest neighbor imputation approach based on the Random Forest framework was used to predict a total of 10 forest structural attributes. The model was developed and validated on > 80,000 lidar plots, with R2 values ranging from 0.49 to 0.61 for key response variables such as canopy cover, stand height, basal area, stem volume, and aboveground biomass. Additionally, a predictor variable importance analysis confirmed that spectral indices, elevation, and geographic coordinates were key sources of information, ultimately offering an improved understanding of the driving variables for large-area forest structure modeling. This study demonstrates the integration of airborne lidar and Landsat-derived reflectance products to generate detailed and spatially extensive maps of forest structure. The methods are portable to map other attributes of interest (based upon calibration data) through access to Landsat or other appropriate optical remotely-sensed data sources, thereby offering unique opportunities for science, monitoring, and reporting programs.
An integrated method for validating long-term leaf area index products using global networks of site-based measurements Remote Sens. Environ. (IF 6.265) Pub Date : 2018-03-19 Baodong Xu, Jing Li, Taejin Park, Qinhuo Liu, Yelu Zeng, Gaofei Yin, Jing Zhao, Weiliang Fan, Le Yang, Yuri Knyazikhin, Ranga B. Myneni
Long-term ground LAI measurements from the global networks of sites (e.g. FLUXNET) have emerged as a promising data source to validate remotely sensed global LAI product time-series. However, the spatial scale-mismatch issue between site and satellite observations hampers the use of such invaluable ground measurements in validation practice. Here, we propose an approach (Grading and Upscaling of Ground Measurements, GUGM) that integrates a spatial representativeness grading criterion and a spatial upscaling strategy to resolve this scale-mismatch issue and maximize the utility of time-series of site-based LAI measurements. The performance of GUGM was carefully evaluated by comparing this method to both benchmark LAI and other widely used conventional approaches. The uncertainty of three global LAI products (i.e. MODIS, GLASS and GEOV1) was also assessed based on the LAI time-series validation dataset derived from GUGM. Considering all the evaluation results together, this study suggests that the proposed GUGM approach can significantly reduce the uncertainty from spatial scale mismatch and increase the size of the available validation dataset. In particular, the proposed approach outperformed other widely used approaches in these two respects. Furthermore, GUGM was successfully implemented to validate global LAI products in various ways with advantaging frequent time-series validation dataset. The validation results of the global LAI products show that GLASS has the lowest uncertainty, followed by GEOV1 and MODIS for the overall biome types. However, MODIS provides more consistent uncertainties across different years than GLASS and GEOV1. We believe that GUGM enables us to better understand the structure of LAI product uncertainties and their evolution across seasonal or annual contexts. In turn, this method can provide fundamental information for further LAI algorithm improvements and the broad application of LAI product time-series.
An OSSE evaluation of the GNSS-R altimetry data for the GEROS-ISS mission as a complement to the existing observational networks Remote Sens. Environ. (IF 6.265) Pub Date : 2018-03-19 Jiping Xie, Laurent Bertino, Estel Cardellach, Maximilian Semmling, Jens Wickert
Simulated signals from Global Navigation Satellite Systems (GNSS), reflected off the sea surface and received aboard low Earth orbiting satellites, have been used to derive sea surface height (SSH) and assimilated into an ocean model in an Observing System Simulation Experiment (OSSE). The experimental approach is named GNSS Reflectometry (GNSS-R), which was proposed for the International Space Station (ISS). This scientific experiment was conducted in the frame of the ESA mission called “GNSS REflectometry, Radio Occultation and Scatterometry aboard the International Space Station” (GEROS-ISS). In this study, three sources of uncertainties of the planned GNSS-R altimeter are considered by the GNSS-R simulator: the troposphere, the ionosphere, and a noise term. An ensemble optimal interpolation (EnOI) data assimilation system is set up for an eddy-resolving HYbrid Coordinate Ocean Model (HYCOM) of the South China Sea (SCS), and two data assimilation runs are performed from the 18th June to the 31st July 2014 with and without GNSS-R. In the run assimilating GNSS-R, the measurements come in addition to traditional Sea Level Anomalies (SLA) from present-day altimeters. In spite of the lower precision of individual GNSS-R retrievals, the results obtained in July show an overall improvement of the Root Mean Squared Difference (RMSD) by 14%, compared to traditional altimeter data only. Considering the crossing of Typhoon Rammasun through the SCS, the GNSS-R data improve the realism of the three largest eddies. The temperature sections along the typhoon track show large differences in the upper 200 m depths in excess of 1 °C near the shelf break. Finally, diagnostics of Degree of Freedom for Signal (DFS) provide a quantitative Impact Factor (IF) of the GNSS-R altimetry data over the conventional altimeter data. On average in July, the IF is low (<5%), but for the period of the typhoon it reaches values over 20%. This indicates the complementary of the GNSS-R altimetry data to the present observing system, especially in filling the gaps of the traditional altimeters.
Ionospheric correction of InSAR data for accurate ice velocity measurement at polar regions Remote Sens. Environ. (IF 6.265) Pub Date : 2018-03-19 Heming Liao, Franz J. Meyer, Bernd Scheuchl, Jeremie Mouginot, Ian Joughin, Eric Rignot
Interferometric synthetic aperture radar (InSAR) has become an essential tool for measuring ice sheet velocity in the Polar Regions. At low radar frequencies, e.g. L-band (1.2 GHz) but also at higher frequency, e.g. C-band (5.6 GHz), the ionosphere has been documented to be an important source of noise in these data. In this paper, we employ a split-spectrum technique and investigate its performance for correcting ionospheric effects in InSAR-based ice velocity measurements in Greenland and Antarctica. Three case studies using ALOS PALSAR data are used to assess the performance of the split spectrum technique for ionosphere correction over a range of environmental parameters. We employ several approaches to evaluate the results, including visual inspection, profile analysis, comparison of experimental and theoretic errors, comparison with reference data from other sources, generation of double difference interferograms, and analysis of time series of multi-temporal data. Our experiments show that ionospheric distortions are observed regularly, and in our analyzed Greenland dataset and Antarctic dataset the ionospheric noise reaches 14 m/yr and 10 m/yr, respectively, which exceeds the signal associated with ice motion. Our analysis using several different approaches demonstrates that the split-spectrum technique provides an effective correction. The split spectrum technique is also found to be superior to currently used approaches such as baseline fitting and multi-temporal averaging. The noise level is reduced by a factor of 70% in Greenland test areas and 90% in Antarctic test areas.
Quantifying grazing patterns using a new growth function based on MODIS Leaf Area Index Remote Sens. Environ. (IF 6.265) Pub Date : 2018-03-19 Rui Yu, A.J. Evans, N. Malleson
Monitoring grazing activities on grassland is crucial for ensuring sustainable grassland development and for protecting it from grazing-led degradation. The Leaf Area Index (LAI), which measures leaf coverage over a surface area, is commonly used as a proxy for grassland condition. However, current studies focus on the year-round or seasonal aggregated LAI change rather than the change that can be attributed explicitly to grazing, which is the important indicator for quantifying grassland grazing. This paper presents a new exponential growth function under grazing with an estimation algorithm, the purpose of which is to extract grazing-led LAI changes for every 8 days' satellite observations. All the analyses are based on the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD15A2H products. An improved MODIS LAI and an expected LAI are produced separately, considering both current and previous grazing-led LAI changes. The differences between expected LAI and improved LAI are then converted to the equivalent carbon mass of grazed material. This grazed carbon mass is then aggregated within the growing season, and compared with the expected carbon mass consumed by livestock (calculated from statistics yearbooks). In addition, Net Primary Productivity (NPP) is produced using the improved LAI, simulated by a Light Use Efficiency with Vegetation Photosynthesis Model (LUE-VPM). This is compared with the NPP produced by LUE-VPM based on original MODIS LAI, MODIS NPP products (MOD17A2H) and grassland monitoring stations' in situ measured data. Results show that the NPP calculated from the improved LAI is statistically the same as in situ converted NPP with a p-value equalling 0.998 (the RMSE between the two is 97.77 gC/m2). Conversely, the p-value between converted in situ measured carbon mass and the MODIS NPP product is 0.011 (the RMSE between the two is 133.98 gC/m2), indicating they are statistically different. The results detailed in this paper provide precise and almost real-time grassland grazing monitoring information for policy makers managing grassland.
A framework for detecting conifer mortality across an ecoregion using high spatial resolution spaceborne imaging spectroscopy Remote Sens. Environ. (IF 6.265) Pub Date : 2018-03-19 Zachary Tane, Dar Roberts, Alexander Koltunov, Stuart Sweeney, Carlos Ramirez
Between 2013 and 2015, during a time of severe drought and elevated bark beetle (Dendroctonus spp.) activity in California, the amount of conifer mortality in the Southern Sierra Nevada increased greatly. Remote sensing is a critical means of providing up-to-date information on the location, magnitude, and extent of mortality across a broad geographic area. We used eleven Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) flight lines, resampled to 30 by 30 m pixel size and acquired on six separate dates as part of the HyspIRI Preparatory Campaign to simulate spaceborne imaging spectroscopy. We also spectrally degraded the AVIRIS images to simulate Landsat-8 data. We tested the ability of single-date and multi-temporal remote sensing to identify red stage conifer mortality, healthy conifer, and non-conifer dominated pixels using a random forest algorithm. Accuracy was assessed with an independent validation dataset acquired via WorldView imagery in areas spatially separate from where training data were collected, as well as through comparison with aerial detection survey and canopy water loss data, with generally good agreement. We found that classifications based on imaging spectroscopy significantly outperformed broadband multispectral feature sets (with a highest overall accuracy of 85.1% obtained by imaging spectroscopy and 80.2% obtained by the simulated multispectral images). We also found that classifications based on multi-temporal imaging spectroscopy were more accurate than single-date imaging spectroscopy (the highest overall accuracy obtained for single-date imaging spectroscopy was 83.4%). Imaging spectroscopy that included interseasonal data from the end of the drought outperformed all other datasets, including interannual data that included only images collected in the summer. Multi-date analysis also improved accuracy using broad band systems. Although current spaceborne assets are adequate for monitoring bark beetle mortality in a heterogeneous ecosystem, a spaceborne imaging spectrometer would further improve operational accuracy.
A Cubesat enabled Spatio-Temporal Enhancement Method (CESTEM) utilizing Planet, Landsat and MODIS data Remote Sens. Environ. (IF 6.265) Pub Date : 2018-03-19 Rasmus Houborg, Matthew F. McCabe
Satellite sensing in the visible to near-infrared (VNIR) domain has been the backbone of land surface monitoring and characterization for more than four decades. However, a limitation of conventional single-sensor satellite missions is their limited capacity to observe land surface dynamics at the very high spatial and temporal resolutions demanded by a wide range of applications. One solution to this spatio-temporal divide is an observation strategy based on the CubeSat standard, which facilitates constellations of small, inexpensive satellites. Repeatable near-daily image capture in RGB and near-infrared (NIR) bands at 3–4 m resolution has recently become available via a constellation of >130 CubeSats operated commercially by Planet. While the observing capacity afforded by this system is unprecedented, the relatively low radiometric quality and cross-sensor inconsistencies represent key challenges in the realization of their full potential as a game changer in Earth observation. To address this issue, we developed a Cubesat Enabled Spatio-Temporal Enhancement Method (CESTEM) that uses a multi-scale machine-learning technique to correct for radiometric inconsistencies between CubeSat acquisitions. The CESTEM produces Landsat 8 consistent atmospherically corrected surface reflectances in blue, green, red, and NIR bands, but at the spatial scale and temporal frequency of the CubeSat observations. An application of CESTEM over an agricultural dryland system in Saudi Arabia demonstrated CubeSat-based reproduction of Landsat 8 consistent VNIR data with an overall relative mean absolute deviation of 1.6% or better, even when the Landsat 8 and CubeSat acquisitions were temporally displaced by >32 days. The consistently high retrieval accuracies were achieved using a multi-scale target sampling scheme that draws Landsat 8 reference data from a series of scenes by using MODIS-consistent surface reflectance time series to quantify relative changes in Landsat-scale reflectances over given Landsat-CubeSat acquisition timespans. With the observing potential of Planet's CubeSats approaching daily nadir-pointing land surface imaging of the entire Earth, CESTEM offers the capacity to produce daily Landsat 8 consistent VNIR imagery with a factor of 10 increase in spatial resolution and with the radiometric quality of actual Landsat 8 observations. Realization of this unprecedented Earth observing capacity has far reaching implications for the monitoring and characterization of terrestrial systems at the precision scale.
High-resolution multi-temporal mapping of global urban land using Landsat images based on the Google Earth Engine Platform Remote Sens. Environ. (IF 6.265) Pub Date : 2018-03-19 Xiaoping Liu, Guohua Hu, Yimin Chen, Xia Li, Xiaocong Xu, Shaoying Li, Fengsong Pei, Shaojian Wang
Timely and accurate delineation of global urban land is fundamental to the understanding of global environmental changes. However, most of the contemporary global urban land maps have coarse resolutions and are available for one or two years only. In this study, we developed the multi-temporal global urban land maps based on Landsat images for the 1990–2010 period with a five-year interval (‘Urban land’ in these maps refers to ‘impervious surface’, i.e., artificial cover and structures such as pavement, concrete, brick, stone and other man-made impenetrable cover types). We proposed the method of Normalized Urban Areas Composite Index (NUACI) and utilized the Google Earth Engine to facilitate the global urban land classifications from an extensive number of Landsat images. The global level's overall accuracy, producer's accuracy and user's accuracy for our mapping results are 0.81–0.84, 0.50–0.60 and 0.49–0.61, respectively. The Kappa values are 0.43–0.50 at the global level, and ~0.33 (in China) and ~0.42 (in the U.S.) at the country level. By analyzing the presented dataset, we found that the world's urban land area had increased from 450.97 ± 1.18 thousand km2 in 1990 to 747.05 ± 1.50 thousand km2 in 2010, reaching a global coverage of 0.63%. China, the United States and India together (14% of the world's terrestrial area in total) contributed almost 43% of the total increase of global urban land area. A free download link for these data is attached at the end of this paper.
Snow cover and snow albedo changes in the central Andes of Chile and Argentina from daily MODIS observations (2000–2016) Remote Sens. Environ. (IF 6.265) Pub Date : 2018-03-19 Jeppe K. Malmros, Sebastian H. Mernild, Ryan Wilson, Torbern Tagesson, Rasmus Fensholt
The variables of snow cover extent (SCE), snow cover duration (SCD), and snow albedo (SAL) are primary factors determining the surface energy balance and hydrological response of the cryosphere, influencing snow pack and glacier mass-balance, melt, and runoff conditions. This study examines spatiotemporal patterns and trends in SCE, SCD, and SAL (2000–2016; 16 years) for central Chilean and Argentinean Andes using the MODIS MOD10A1 C6 daily snow product. Observed changes in these variables are analyzed in relation to climatic variability by using ground truth observations (meteorological data from the El Yeso Embalse and Valle Nevado weather stations) and the Multivariate El Niño index (MEI) data. We identified significant downward trends in both SCE and SAL, especially during the onset and offset of snow seasons. SCE and SAL showed high inter-annual variability which correlate significantly with MEI applied with a one-month time-lag. SCE and SCD decreased by an average of ~13 ± 2% and 43 ± 20 days respectively, over the study period. Analysis of spatial pattern of SCE indicates a slightly greater reduction on the eastern side (~14 ± 2%) of the Andes Cordillera compared to the western side (~12 ± 3%). The downward SCE, SAL, and SCD trends identified in this study are likely to have adverse impacts on downstream water resource availability to agricultural and densely populated regions in central Chile and Argentina.
Effects of landscape fragmentation on land loss Remote Sens. Environ. (IF 6.265) Pub Date : 2018-03-19 Nina S.-N. Lam, Weijia Cheng, Lei Zou, Heng Cai
Coastal Louisiana has been facing a serious land loss problem over the past several decades, and extensive research has been undertaken to address the problem. However, the importance of landscape fragmentation on land loss has seldom been examined. This paper evaluates the effects of landscape fragmentation on land loss in the Lower Mississippi River Basin region. The research hypothesis is that the higher the degree of fragmentation in a locality, the greater the amount of land loss in the next time period. We used Landsat-TM data with a pixel size of 30 m × 30 m in 1996 and 2010 and transformed the images into either land or water pixels. We then calculated the fractal dimension and Moran's I spatial autocorrelation statistics and used them to represent the degree of landscape fragmentation. Four sample box sizes, including sizes of 101 × 101, 71 × 71, 51 × 51, and 31 × 31 pixels, were used to detect if there is a relationship between fragmentation and land loss at different neighborhood (context) scales. For each box size, 100 samples were randomly selected. To isolate the fragmentation effect so that it can be better evaluated, we used only sample boxes with a 50% land-water ratio. Regression results between fragmentation and land loss show that the R2 values for box sizes of 71 × 71, 51 × 51 and 31 × 31 were statistically significant (0.20, 0.45, 0.35; p < 0.001 for Moran's I) but not for the 101 × 101 box size. These results imply that land protection may be most effective by prioritizing areas with land patches that have the least fragmentation. Furthermore, the neighborhood scale at which the R2 value is the highest indicates the scale at which the effects are most likely to be observed (51 × 51 box size, approximately 1.5 × 1.5 km2, R2 = 0.45), which suggests that future land loss modeling using this neighborhood scale would be most effective.
A geometric model to simulate thermal anisotropy over a sparse urban surface (GUTA-sparse) Remote Sens. Environ. (IF 6.265) Pub Date : 2018-03-19 Dandan Wang, Yunhao Chen, Wenfeng Zhan
Remote measurements of land surface temperature are prone to significant directional anisotropy. This anisotropy is strong over urban surfaces because of three-dimensional structures and the resulting heterogeneous temperature distributions. However, the development of models that correct urban thermal anisotropy and consider the two factors is rare. In this study, with the assumption of random building orientations, we developed a Geometric model to simulate Thermal Anisotropy over a sparse Urban surface (GUTA-sparse), which does not consider the mutual shadowing effect. GUTA-sparse assumes that anisotropy is the sum of three parts: the vertical wall background temperature contribution, vertical wall orientation effects and shadow contribution. The simulation data provided by the 3-D Discrete Anisotropic Radiative Transfer (DART) model and airborne measurements over the city of Marseille were employed to evaluate model performance. The results show that in this model, the solar zenith angle influences the ability of the fitted coefficients to characterize surface features. GUTA-sparse is applicable over urban surfaces that have aspect ratios smaller than 1.0, where the mutual shadowing effect is negligible. The proposed model can also well simulate the airborne measured anisotropy with root mean square errors (RMSEs) of 0.44, 0.44, 0.56 and 0.40 K, and the anisotropy amplitudes at the four flight times all exceed 8 K. The model is independent of surface parameters that are difficult to obtain, which makes it suitable for remote sensing applications. Because the model is linear with respect to surface parameters, it can be applied to heterogeneous urban surfaces. This model aids in better understanding the relationships among urban surface geometry, component temperatures and thermal anisotropy, and this model may potentially correct the directional temperature values to a common viewing geometry.
Use of bio-optical profiling float data in validation of ocean colour satellite products in a remote ocean region Remote Sens. Environ. (IF 6.265) Pub Date : 2018-03-19 Bożena Wojtasiewicz, Nick J. Hardman-Mountford, David Antoine, François Dufois, Dirk Slawinski, Thomas W. Trull
Utility of data from autonomous profiling floats for the validation of satellite ocean colour products from current satellite ocean colour sensors was assessed using radiometric and chlorophyll a fluorescence data from biogeochemical profiling floats (BGC-Argo) deployed in the subtropical gyre of the Indian Ocean. One of the floats was equipped with downward irradiance and upwelling radiance sensors, allowing the remote sensing reflectance, Rrs, to be determined. Comparisons between satellite and in situ Rrs indicated good agreement for the shorter wavelengths, but weak relationships for both satellites for the 555 nm channel, and showed that radiometers deployed on multipurpose, off-the-shelf BGC-Argo floats can provide validation-quality measurements. About 300 chlorophyll a concentration match-ups were achieved within 18 months, which increased the number of validation data points available for the Indian Ocean as a whole by a factor of ~4 from the previous historical record. Generally, the satellite data agreed with the float-derived chlorophyll concentration within the uncertainty of ±35%, for the band-difference (OCI) and band-ratio (OC3) algorithms, but not for a semianalytical ocean colour model (GSM) that exhibited significantly higher chlorophyll values (>100% mean difference). Our results indicate that autonomous float-based measurements provide substantial potential for improving regional validation of satellite ocean colour products in remote areas.
Parametrizing tidal creek morphology in mature saltmarshes using semi-automated extraction from lidar Remote Sens. Environ. (IF 6.265) Pub Date : 2018-03-19 C. Chirol, I.D. Haigh, N. Pontee, C.E. Thompson, S.L. Gallop
Development of Landsat-based annual US forest disturbance history maps (1986–2010) in support of the North American Carbon Program (NACP) Remote Sens. Environ. (IF 6.265) Pub Date : 2018-03-19 Feng Zhao, Chengquan Huang, Samuel N. Goward, Karen Schleeweis, Khaldoun Rishmawi, Mary A. Lindsey, Elaine Denning, Louis Keddell, Warren B. Cohen, Zhiqiang Yang, Jennifer L. Dungan, Andrew Michaelis
In Phase III of the North American Forest Dynamics (NAFD) study an automatic workflow has been developed for evaluating forest disturbance history using Landsat observations. It has four major components: an automated approach for image selection and preprocessing, the vegetation change tracker (VCT) forest disturbance analysis, postprocessing, and validation. This approach has been applied to the conterminous US (CONUS) to produce a comprehensive analysis of US forest disturbance history using the NASA Earth Exchange (NEX) cloud computing system. The resultant NAFD-NEX product includes 25 annual forest disturbance maps for 1986–2010 and two time-integrated maps to provide spatial-temporal synoptic view of disturbances over this time period. These maps were derived based on 24,000+ scenes selected from 350,000+ available Landsat images at 30-m resolution, and were validated using a visual assessment of Landsat time-series images in combination with high-resolution and other ancillary data sources over samples selected using a probability based sampling method. The validation revealed no major biases in the NAFD-NEX maps for disturbance events that resulted in at least 20% canopy cover loss. The average user's and producer's accuracies for the disturbance class were 53.6% and 53.3%, respectively, with the individual year's user's accuracy varying from 42.8% to 73.6% and producer's accuracy from 39.0% to 84.8% over the 25-year period. The NAFD-NEX disturbance maps are available from a web portal of the Oak Ridge National Laboratory Distributed Active Archive Center (ORNL-DAAC) at https://doi.org/10.3334/ORNLDAAC/1290.
Two-source energy balance modeling of evapotranspiration in Alpine grasslands Remote Sens. Environ. (IF 6.265) Pub Date : 2018-03-19 M. Castelli, M.C. Anderson, Y. Yang, G. Wohlfahrt, G. Bertoldi, G. Niedrist, A. Hammerle, P. Zhao, M. Zebisch, C. Notarnicola
This work aims to assess a diagnostic approach which links evapotranspiration (ET) to land surface temperature (LST) measured by thermal remote sensing in the Alps. We estimated gridded ET, from field (30 m) to regional (1 km) scales, and we performed a specific study on grassland ecosystems in the Alps in South Tyrol (Italy), to evaluate the model sensitivity to different types of land management. The energy balance model TSEB ALEXI (Two Source Energy Balance Atmosphere Land EXchange Inverse) was first applied to Meteosat satellite data. Then ET was estimated by the flux disaggregation procedure DisALEXI, driven by MODIS and Landsat LST retrievals, which has never been applied before in a mountain region. We evaluated the model against eddy-covariance (EC) measurements from established stations in the Alps, and analyzed the main limitations which affect the model performance in mountainous regions. The TSEB model, applied in plot-scale mode using tower-based meteorological and LST input data, performed well with errors in daytime (6–18 UTC+1) latent heat flux around 30–60 W m−2 in comparison with flux measurements corrected for the lack of closure in the energy balance. For landscape ET retrievals, while Landsat resolution (30 m) is preferable for capturing small-scale heterogeneity in landscape moisture conditions, and for direct comparison with tower fluxes, persistent cloud cover resulted in no clear Landsat scenes during the study period. MODIS-based retrievals at 1 km resolution are too coarse to resolve the flux tower footprint in this complex landscape, yielding discrepancies of 100 W m−2 in model-measurement comparisons. Still, MODIS DisALEXI partitioning of the energy budget was reasonable and enabled to detect evaporative stress at regional scale expressed as the ratio between actual and potential ET, fPET. We evaluated fPET in comparison with a crop stress index based on cumulative air temperature and precipitation at different stations in the study area, and investigated ability to capture differential responses between managed and unmanaged grasslands. Results show that in the Alps i) moderate resolution thermal data can be used to monitor evaporative stress at the regional scale; ii) the spatial-temporal evolution of ET can be characterized from MODIS and Landsat thermal data with limitations which are due to the low availability of clear-sky scenes and to the small-scale (∼10 m) changes in soil moisture, topography and canopy density, which control ET patterns in mountainous regions; iii) solar radiation and leaf area index are critical variables which strongly affect the accuracy of the modeled energy fluxes.
Evaluation of summer passive microwave sea ice concentrations in the Chukchi Sea based on KOMPSAT-5 SAR and numerical weather prediction data Remote Sens. Environ. (IF 6.265) Pub Date : 2018-03-19 Hyangsun Han, Hyun-cheol Kim
Satellite passive microwave (PM) sensors have observed sea ice in Polar Regions and provided sea ice concentration (SIC) data since the 1970s. SIC has been used as a primary data source for climate change prediction and ship navigation. However, the accuracy of PM SIC is typically low and biased in summer. To provide more accurate information for climatic research and ship navigation, it is necessary to evaluate quantitatively the accuracy of PM SIC and to account for its errors. In this research, we evaluated the SIC data derived from PM measurements using four representative sea ice algorithms: NASA Team (NT), Bootstrap (BT), Ocean and Sea Ice Satellite Application Facility (OSISAF) hybrid, and Arctic Radiation and Turbulence Interaction STudy (ARTIST) Sea Ice (ASI). Analyses were performed for the Chukchi Sea in summer using KOrean Multi-Purpose SATellite-5 (KOMPSAT-5) Enhanced Wide-swath synthetic aperture radar (SAR) images. Ice/water maps were generated by binary classification of texture features in the SAR images based on Random Forest, a rule-based machine learning approach. SIC values estimated from the sea ice algorithms showed good correlation with those calculated from the KOMPSAT-5 ice/water maps, but the root mean square error was larger than 10%. SIC values estimated from the algorithms showed different error trends according to the KOMPSAT-5 SIC range. All algorithms overestimated SIC values in open drift ice zones (KOMPSAT-5 SICs ranged from 0% to 15%). In marginal ice zones (SICs ranged from 15% to 80%), the OSISAF SIC values were the least biased compared to those from KOMPSAT-5. The NT algorithm largely underestimated SIC values in marginal ice zones, while the BT and ASI algorithms overestimated them considerably. All algorithms, except for BT, underestimated SIC in consolidated pack ice zones (SICs ranged from 80% to 100%). By analyzing the correlations of biases of SIC from the algorithms with the numerical weather prediction (NWP) data from the European Reanalysis Agency Interim reanalysis, it was found that the overestimation of NT and ASI SICs was largely influenced by atmospheric water vapor content, while the underestimation of NT and OSISAF SICs was owing to ice surface melting. The overestimation of BT SICs was not significantly correlated with the NWP data. The underestimated SIC from the BT and ASI algorithms for high SIC regions might be compensated by the atmospheric water vapor content. The differences in SIC values estimated from each algorithm were due to different sensitivities to atmospheric water vapor content in the regions with KOMPSAT-5 SIC lower than 40% and to ice surface melting in the regions with higher KOMPSAT-5 SIC.
Extracting the full value of the Landsat archive: Inter-sensor harmonization for the mapping of Minnesota forest canopy cover (1973–2015) Remote Sens. Environ. (IF 6.265) Pub Date : 2018-03-19 Jody C. Vogeler, Justin D. Braaten, Robert A. Slesak, Michael J. Falkowski
Remote sensing estimates of forest canopy cover have frequently been used to support a variety of applications including wildlife habitat modeling, monitoring of watershed health, change detection, and are also correlated to various aspects of forest structure and ecosystem function. Although data from the long running Landsat earth observation program (1972–present) have been previously utilized to characterize forest canopy cover, the variability in spatial and spectral resolutions between the Landsat sensors has generally limited analyses to readily comparable imagery from the TM and ETM+ sensors, which omits large portions of the full temporal record. In this study, we present an R package, LandsatLinkr, which automates the processes for harmonizing Landsat MSS and OLI imagery to the spatial and spectral qualities of TM and ETM+ imagery, allowing for the generation of annual cloud-free composites of tasseled cap spectral indices across the entire Landsat archive. We demonstrate the utility of LandsatLinkr products, further enhanced through the LandTrendr segmentation algorithm, for characterizing forest attributes through time by developing annual forest masks and maps of estimated canopy cover for the state of Minnesota from 1973 to 2015. The forest mask model had an overall accuracy of 87%, with omission and commission errors for the forest class of 17% and 10%, respectively, and 9% and 16% for non-forest classification. Our resulting maps depicted a significant positive trend in forest cover across all ecological provinces of Minnesota during the study period. A random forest model used to predict continuous canopy cover had a pseudo R2 of 0.75, with a cross validation RMSE of 5%. Our results are comparable to previous Landsat-based canopy cover mapping efforts, but expand the evaluation time period as we were able to utilize the entire Landsat archive for assessment.
Integrating satellite optical and thermal infrared observations for improving daily ecosystem functioning estimations during a drought episode Remote Sens. Environ. (IF 6.265) Pub Date : 2018-03-19 Bagher Bayat, Christiaan van der Tol, Wouter Verhoef
Satellite optical and thermal infrared (TIR) spectra are linked to vegetation properties and, therefore, carry valuable information needed for estimating vegetation functioning as expressed in canopy photosynthesis [gross primary production (GPP)] and evapotranspiration (ET). The joint effort is required to fully exploit this satellite spectral information and to demonstrate its capability to reveal ecosystem functioning in various environmental conditions. We investigated the relationship between Landsat (TM5 and ETM7) optical/thermal data and canopy daily functioning of annual C3 grasses at a Fluxnet site (US-Var) during a prolonged drought episode. By using the ‘Soil-Canopy Observation of Photosynthesis and Energy fluxes’ (SCOPE) model, reference GPP and ET were simulated via locally measured weather data, and then actual GPP and ET were simulated twice: first using the vegetation properties retrieved only from the optical bands, and second using information from both the optical and thermal bands. The outputs of last two simulations were compared to flux tower measurements. For the first simulation, we used the MODTRAN atmospheric model and the optical radiative transfer (RT) routine in SCOPE, RTMo, to perform atmospheric correction and retrieve vegetation properties [notably Leaf Area Index (LAI), leaf chlorophyll content (Cab), leaf water content (Cw), leaf dry matter content (Cdm), the leaf inclination distribution function (LIDF) and the senescent material content (Cs)] by model inversion through optimization. We used the optical bands of 20 Landsat images covering the period from January to August 2004. The model inversion performance was assessed by R2 (0.86) and RMSE (0.13) between the retrieved and ground-measured LAI. All the retrieved vegetation properties were linearly interpolated over time and were used, together with locally measured weather variables, to simulate GPP and ET at half-hourly time steps with SCOPE. For the second simulation, we additionally used TIR information to retrieve the maximum carboxylation capacity (Vcmax), the Ball-Berry stomatal conductance parameter (m) and soil surface and boundary resistances (rss and rbs) by inversion of the energy balance and thermal radiative transfer routines of SCOPE, RTMt, through separate look-up tables. The comparison between simulations and measurements shows that most drought effects on ET, GPP and transpiration are “visible” in the Landsat optical bands. However, the accurate simulation of soil evaporation requires TIR information. The results from this study indicate that the integration of optical and TIR information has a great potential to capture the drought effects on the grass canopy in terms of reductions in daily GPP and ET.
Quantifying CDOM and DOC in major Arctic rivers during ice-free conditions using Landsat TM and ETM+ data Remote Sens. Environ. (IF 6.265) Pub Date : 2018-03-19 C.G. Griffin, J.W. McClelland, K.E. Frey, G. Fiske, R.M. Holmes
As high-latitudes warm, permafrost thaws, and the hydrological cycle accelerates, ground-based monitoring of riverine organic matter may be supplemented by satellite remote sensing during ice-free conditions. Recent programs, namely the Arctic Great Rivers Observatory, have established methodologically consistent sampling across the hydrograph, and shared the resulting data publicly. However, these efforts are limited by frequency, funding, and length of record. Satellite remote sensing can be used to estimate chromophoric dissolved organic matter (CDOM) as a riverine constituent that influences optical properties in surface waters. In this study, daily CDOM absorption was first estimated using discharge-constituent regression-based models for 2000–2013. We then regressed these discharge-based CDOM estimates against Landsat TM and ETM+ surface reflectance data from Google Earth Engine for the six largest rivers draining the pan-Arctic watershed (the Kolyma, Lena, Mackenzie, Ob', Yenisey, and Yukon rivers). These CDOM results were converted to dissolved organic carbon (DOC), using the strong relationship (R2 = 0.88) between direct measurements of the two constituents. Using river-specific remote sensing models, R2 could be as high as 0.84. Grouping all rivers into a single “universal” regression reduced R2 and increased root mean square errors, such as in the Yenisey River where R2 dropped by 0.63, and RMSE rose by 1.1 m−1. Seasonally varying discharge drove much of the variation in satellite-derived CDOM and DOC, corroborating recent studies. Satellite imagery can increase the frequency of monitoring observations, particularly during summer and fall when riverine CDOM absorption may be most sensitive to thawing permafrost.
Assessing the potential of parametric models to correct directional effects on local to global remotely sensed LST Remote Sens. Environ. (IF 6.265) Pub Date : 2018-03-19 Sofia L. Ermida, Isabel F. Trigo, Carlos C. DaCamara, Jean-Louis Roujean
Land surface temperature (LST) values retrieved from satellite measurements in the thermal infrared (TIR) may be strongly affected by spatial anisotropy. Different parametric approaches have been proposed to simulate such effects. These are relatively simple models requiring few input data and therefore appropriate to simulate directional effects in satellite LST retrievals over large areas. The purpose of this study is to consistently evaluate the performance of two parametric models (the so-called Kernel and Hotspot models), and to assess their respective potential to correct directional effects on LST for a wide range of surface conditions, in terms of tree coverage, vegetation density, surface emissivity. We also propose an optimization of the correction of directional effects through a synergistic use of both models. The Kernel model allows an effective simulation of LST directionality associated with shadowing effects and emissivity anisotropy, but results show that it significantly underestimates the amplitude of the angular corrections. The Hotspot model performs better in simulating anisotropy related to shadowing effects. However, it is unable to account for emissivity anisotropy, showing lower performance than the Kernel model for nighttime data and for low tree coverage. The combined Kernel-Hotspot model provides corrections on LST directionality with reliable quality, with particularly improved performance during nighttime and for low tree densities.
On the detectability of adjacency effects in ocean color remote sensing of mid-latitude coastal environments by SeaWiFS, MODIS-A, MERIS, OLCI, OLI and MSI Remote Sens. Environ. (IF 6.265) Pub Date : 2018-03-19 Barbara Bulgarelli, Giuseppe Zibordi
The detectability of adjacency effects (AE) in ocean color remote sensing by SeaWiFS, MODIS-A, MERIS, OLCI, OLI and MSI is theoretically assessed for typical observation conditions up to 36 km offshore (20 km for MSI). The methodology detailed in Bulgarelli et al. (2014) is applied to expand previous investigations to the wide range of terrestrial land covers and water types usually encountered in mid-latitude coastal environments. Simulations fully account for multiple scattering within a stratified atmosphere bounded by a non-uniform reflecting surface, sea surface roughness, sun position and off-nadir sensor view. A harmonized comparison of AE is ensured by adjusting the radiometric sensitivity of each sensor to the same input radiance. Results show that average AE in data from MODIS-A, and from MERIS and OLCI in reduced spatial resolution, are still above the sensor noise level (NL) at 36 km offshore, except for AE caused by green vegetation at the red wavelengths. Conversely, in data from the less sensitive SeaWiFS, OLI and MSI sensors, as well as from MERIS and OLCI in full spatial resolution, sole AE caused by highly reflecting land covers (such as snow, dry vegetation, white sand and concrete) are above the sensor NL throughout the transect, while AE originated from green vegetation and bare soil at visible wavelengths may become lower than NL at close distance from the coast. Such a distance increases with the radiometric resolution of the sensor. It is finally observed that AE are slightly sensitive to the water type only at the blue wavelengths. Notably, for an atmospheric correction scheme inferring the aerosol properties from NIR data, perturbations induced by AE at NIR and visible wavelengths might compensate each other. As a consequence, biases induced by AE on radiometric products (e.g., the water-leaving radiance) are not strictly correlated to the intensity of the reflectance of the nearby land.
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