Vegetation dynamics in the African continent play an important role in the global terrestrial carbon cycle. Above-ground biomass carbon (AGC) stocks in Africa are sensitive to drought, fires and anthropogenic disturbances, and can be increased from forest restoration and tree plantation. However, there are large uncertainties in estimating changes that have occurred in AGC stocks in Africa over the

PROSPECT is the most widely used optical leaf model for a wide range of remote sensing applications on vegetation and has been developed and parameterised based on empirical data measured almost exclusively on terrestrial plant leaves. As aquatic plants differ substantially from terrestrial plants in leaf morphology and physiology, the validity of the relationships underlying PROSPECT in aquatic plants

Seamless hourly surface fine particulate matters (PM2.5) datasets are urgently needed for environmental and epidemiologic studies. Most existing models rely on satellite AOD with spatial gaps and the time-geolocation information not directly physically related to ground-level PM2.5 as input, leading to several problems, e.g., for daytime only, spatial discontinuity. By exploring spatiotemporal connection

Wide-area deformation measurements require multi-frame InSAR datasets for joint monitoring due to the limited swath width of SAR images. However, variations in the stability of reference points, error signal magnitude and distribution, and spatio-temporal filtering parameters result in different deformation rates between adjacent frames, leading to centimeter-level errors in the results. To obtain

Machine learning (ML) has proven to be a powerful tool for utilizing the rapidly increasing amounts of remote sensing data for environmental monitoring. Yet ML models often require a substantial amount of ground truth labels for training, and models trained using labeled data from one domain often demonstrate poor performance when directly applied to other domains. Transfer learning (TL) has emerged

Given the importance that melt ponds have on the energy balance of summer sea ice, there have been several efforts to develop pan-Arctic datasets using satellite data. Here we intercompare three melt pond data sets that rely on multi-frequency optical satellite data. Early in the melt season, the three data sets have similar spatial patterns in melt pond fraction, but this agreement weakens as the

Northern peatlands store a large amount of carbon in the form of partially decomposed organic matter. Because the majority of northern peatlands are located in remote areas, remote sensing serves as a suitable alternative to traditional surveys, enabling to enhance our understanding of peatland vegetation. Among various optical remote sensing signals, sun-induced fluorescence (SIF) is the most directly

‘Water town’, a city design idea featuring buildings sitting along rivers and their associated riparian vegetation, serves as natural refuges for citizens to escape the dual challenges of heatwaves and urban heat islands. We investigated the multi-scale cooling effect of river networks in Shanghai, a typical modern city widely known as an assemblage of water towns. At the neighborhood scale, we conducted

Global changes in climate and land use are threatening natural ecosystems, biodiversity, and the ecosystem services people rely on. This is why it is necessary to track and monitor spatiotemporal change at a level of detail that can inform science, management, and policy development. The current constellation of multiple Landsat and Sentinel-2 satellites collecting imagery at predominantly ≤30-m spatial

High-resolution daily snow cover estimation is challenging due to irregular satellite data availability. In this study, we create synthetic 30 m snow cover images for dates with no satellite data. It is based on the relationship between meteorological predictors and available clear sky 30 m Landsat/Sentinel-2 snow cover images. Our approach relies on the fact that while snow cover can vary in a matter

Lidar is a unique instrument for profiling aerosol and cloud layers. Detecting the boundaries of these layers is crucial because a missing layer will not be retrieved later. Many layer detection methods, which can be divided into slope-type and threshold-type methods, have been proposed and applied to a one-dimensional (1D) profile or a two-dimensional (2D) scene using numerous empirical settings.

Solar-induced chlorophyll fluorescence (SIF) has long been regarded as a proxy for photosynthesis and has shown superiority in estimating gross primary production (GPP) compared to traditional vegetation indices, especially in evergreen ecosystems. However, current SIF-based GPP estimations regard the canopy as a large leaf and seldom consider the impact of interactions among light, canopy structure

Airborne high spectral resolution lidar (HSRL) measurements of ocean particulate backscatter (bbp) offer dramatic improvements in spatiotemporal coverage over in situ techniques, filling observational “blind spots” that limit our ability to study ocean processes. However, the technique has been assessed in only a few limited cases, and uncertainties remain regarding its applicability across a diversity

A tailings dam failure can be catastrophic. One constantly dumps or removes ore-related materials behind or within a dam before its closure, creating substantial surface change or deformation. The deformation can be assessed with multiple temporal synthetic aperture radar (SAR) observations and interferometric SAR (InSAR) techniques. The surface deformation is one prerequisite causing the failure.

Imaging spectrometers provide important Earth surface data for terrestrial and aquatic ecology, hydrology, geology through retrieved surface reflectance spectra. Surface reflectance is generally recovered from measured radiance through model inversions of the surface and atmospheric system. Most retrieval approaches treat all pixels independently and ignore spatial correlations in the atmosphere for

Green area index (GAI), leaf chlorophyll content (LCC) and canopy chlorophyll content (CCC) are key variables that are closely related to crop growth. Concurrent and continuous monitoring of GAI, LCC and CCC is critical to keep consistency among variables and make decisions for field precision managements. Previous studies have developed several instruments and algorithms to monitor continuous GAI

Carbon cycle is influenced by agricultural soils, and accurately mapping the soil organic carbon (SOC) content of global Mollisols at a 30 m spatial resolution can contribute to clarifying the carbon sequestration capacity of each region, facilitate the quantification of agroecosystems and contribute to global food security. However, the high heterogeneity of environmental variables in global regions

Current and future vegetation imaging spectroscopy satellites will bring a new data stream of information, of high scientific value to refine existing remote sensing products, and develop new ones. The sensors on board ESA's Fluorescence Explorer (FLEX) will cover the entire 500–780 nm range, designed to track the photosynthetic energy partitioning based on the key pigment players in the light reactions

Stratified random sampling is often used to obtain reference data for assessing the accuracy of land cover maps created from remotely sensed data and for estimating area of land cover and land cover change. The sample size allocation to strata determines the precision of estimates of user's accuracy, producer's accuracy, and proportion of area. Different choices of nh (the sample size in stratum h)

Dryland ecosystems have complex vegetation communities, including subtle transitions between communities and heterogeneous coverage of key functional groups. This complexity challenges the capacity of remote sensing to represent land cover in a meaningful way. Many remote sensing methods to map vegetation in drylands simplify fractional cover into a small number of functional groups that may overlook

Two basic properties of soil moisture, namely water content and its phase status (freeze/thaw, referred to as F/T), strongly influence the pattern and efficiency of water and heat exchanges at the land-atmosphere interface. Therefore, accurate soil moisture and F/T retrievals are crucial to explore the impact of soil moisture and temperature on multi-sphere interactions, especially in the permafrost

Abstract not available

Solar-Induced chlorophyll Fluorescence (SIF) could be used as an indicator of photosynthetic status due to the close relationship between SIF and the photosynthetic apparatus. Terrestrial SIF is emitted throughout the red and near-infrared spectrum and is characterized by two peaks centered around 685 nm and 740 nm, respectively. In this study, we present a data-driven approach to reconstruct the terrestrial

The directional variation in upwelling thermal radiance (known as ‘thermal anisotropy’) affects our understanding of urban land surface temperature (LST) from remote sensing observations. Parametric models have been proposed to quantify and potentially correct the thermal anisotropy from satellite systems. The accurate specification of the coefficients is critical for broadening applications of parametric

Snow depth is highly variable across basins, yet most snow depth data in the western U.S. come from sparse in situ point measurements. The water resources community needs accurate snow depth data for improved basin-wide snow depth estimates. The NASA ICESat-2 mission has provided over four years of global satellite laser altimetry measurements since October 2018. Previous studies have shown that standard

Crop harvesting date is critical information for crop yield prediction, financial and logistic planning of grain market and downstream supply chain. Remote sensing has the potential to map harvesting date at regional scale. However, existing studies generally lack ground truth data, and have not fully utilized spectral and temporal information of satellite data. To address these gaps, we present a

The fate of the carbon stored in frozen peatlands in Canada's Hudson Bay Lowlands (HBL) depends strongly on water, with wetter conditions favoring long-term storage. Dynamic surface water products generated from historical Landsat data exist to inform surface water trends in the HBL based on binary classifications of land vs water at 30 m spatial resolution. However, the HBL contains many water features

Wide-swath and high-resolution Thermal infrared (TIR) data are limited by the mutual constraint between swath and resolution. In this paper, we propose a whisk-broom imaging method with the long-linear-array (LLA) 4-stage TDI detector (a total of 2048 pixels along splicing direction) and high-precision one-dimensional scanning mirror to achieve 30 m resolution and 300 km swath. And it is being implemented

Accurate mapping of crops with high spatiotemporal resolution plays a critical role in achieving the Sustainable Development Goals (SDGs), especially in the context of integrated crop-livestock systems (ICLS). Stakeholders can make informed decisions and implement targeted strategies to achieve multiple SDGs related to agriculture, rural development, and sustainable livelihoods by understanding the

The aim of the study was to evaluate, for the first time, the capability of different machine learning (ML) algorithms in classifying along-track lake surface conditions (open water and ice types) across ice seasons (freeze-up, ice growth and break-up periods) from Sentinel-3 A/B synthetic aperture radar altimeter (SRAL) data. To achieve this goal, over 107,500 radar waveforms extracted from 11 large

Oil spill accidents are one of the major problems causing marine pollution, and thus such accidents require rapid detection for early response. In recent years, deep learning algorithms for oil spill detection have been developed for analyzing SAR images. Nevertheless, to generation of deep learning training data using visual inspection is not only a time-consuming and labor-intensive, but also cause

Secchi Disk Depth (Zsd) is one of the most fundamental and widely used water-quality indicators quantifiable via optical remote sensing. Despite decades of research, development, and demonstrations, currently, there is no operational model that enables the retrieval of Zsd from the rich archive of Landsat, the long-standing civilian Earth-observation program (1972 – present). Devising a robust Zsd

Leaf flavonoid content (LFC) is a marked indicator of the protection signals from biotic and abiotic stresses, as well as the potential in the recovery of phenolic compounds from plants for producing potent antioxidants. LFC has been non-destructively retrieved from leaf reflectance spectra in recent studies. However, the LFC estimation from canopy-level spectra remains poorly understood and challenging

The land surface's thermal dynamics follows annual and diurnal cycles that are to a large extent controlled by solar geometry. Therefore, annual and diurnal variations of land surface temperature (LST) can be modelled with relatively simple functions controlled by a small number of parameters, typically from three to six. The parameter values of the models can be determined by fitting the respective

Sub-pixel mapping is the prevailing approach for dealing with the mixed pixel effect in urban land use/land cover classification, by reconstructing the sub-pixel-scale distribution inside each mixed-pixel based on spatial autocorrelation. However, 1) traditional spatial autocorrelation is limited to a local window, which cannot model the teleconnection between two locations or objects that are far

High quality independent ground measurements that are traceable to metrology standards, with a full uncertainty budget, are required for validation over the lifetime of ocean-colour satellite missions. In this paper, we used radiometric Fiducial Reference Measurements (FRM) collected during four Atlantic Meridional Transect (AMT) field campaigns from 2016 to 2019 to assess the performance of radiometric

Tree clearing and degradation inside and outside forest ecosystems in Africa are important contributors to global carbon budget and emissions. Part of the uncertainties in emission estimates, among other things, is related to the non-inclusion of disturbances across all tree-based systems and limitations in the capacity of existing methodologies to detect subtle changes or degradation. Here, we present

Retrievals of satellite aerosol optical depth (AOD) products are used for studies of air pollution, energy balance, and climate change. However, few studies have evaluated the diurnal time representation (DTR) of satellite AOD products. In this study, we proposed a statistically valid threshold method to investigate the DTR of Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared

Images of key phenological periods play a vital role in agricultural applications as they capture the unique spectral characteristics of crops. Unfortunately, acquiring high-spatial-resolution images of the key phenological period from a single satellite platform remains challenging due to its short duration and synchronization with the rainy season. Spatiotemporal fusion (STF) is an effective tool

The photon recollision probability (p) and spectral invariant theory (p-theory) have gained much attention in the vegetation remote sensing community over the past decades. A broad range of studies have been performed involving the derivation of theoretical principles, determination of the p value in field measurements and remote sensing studies, and application of the theory in various areas. This

Artificial intelligence (AI) approaches nowadays have gained remarkable success in single-modality-dominated remote sensing (RS) applications, especially with an emphasis on individual urban environments (e.g., single cities or regions). Yet these AI models tend to meet the performance bottleneck in the case studies across cities or regions, due to the lack of diverse RS information and cutting-edge

Process-based models are widely used to predict the agroecosystem dynamics, but such modeled results often contain considerable uncertainty due to the imperfect model structure, biased model parameters, and inaccurate or inaccessible model inputs. Data assimilation (DA) techniques are widely adopted to reduce prediction uncertainty by calibrating model parameters or dynamically updating the model state

Abstract Soil color is a key indicator of soil properties and conditions, exerting influence on both agronomic and environmental variables. Conventional methods for soil color determination have come under scrutiny due to their limited accuracy and reliability. In response to these concerns, we developed an innovative system that leverages 35 years of satellite imagery in conjunction with in-situ soil

Diurnal variations in planetary boundary layer height (PBLH) are essential for regulating the diffusion of pollutants and controlling heat and moisture exchange in the lower atmosphere. However, owing to the lack of continuous large-scale observations, the global behavior of diurnal variation in the PBLH remains poorly understood. This study seeks to bridge this knowledge gap by examining 33 months

Abstract not available

The ICESat-2 (Ice, Cloud, and land Elevation Satellite-2) photon-counting lidar is a revolutionary active remote sensing device and is extremely sensitive to both laser signal and solar background induced noise photons. The weak laser signals, such as aerosol and water subsurface backscattered laser photons, may be buried in noise in the daytime. A noise model for spaceborne photon-counting lidars

Satellite passive microwave (PMW) observations are important data sources for obtaining land surface temperature (LST) especially for cloudy conditions. Considering the generally poorer spatial representativeness of PMW observations, fusion of PMW-based and thermal infrared-(TIR-) based observations have been considered as an important approach to generate all-weather LST with appropriate resolution

Solar-induced chlorophyll fluorescence (SIF), as a direct probe of vegetation photosynthesis, has recently been an effective indicator for crop yield estimation in late-season. Spatio-temporal prediction of SIF (STP-SIF) from mid- to late-season could be a promising solution to forecast crop yields in mid-season. However, STP-SIF has not been well explored in assessing its applicability in crop yield

Timely knowledge of phenological development and crop growth is pivotal for evidence-based decision making in agriculture. We propose a near real-time approach combining radiative transfer model inversion with physiological and phenological priors from multi-year field phenotyping. Our approach allows to retrieve Green Leaf Area Index (GLAI), Canopy Chlorophyll Content (CCC) and hence Leaf Chlorophyll

Remote sensing aims to provide precise information on forest ecosystems under climate and land use changes, much of which is in the form of parameters estimated for biotic and abiotic variables for various official reporting instruments. Model-assisted estimation (MA) that harnesses remote sensing has demonstrated a surpassing ability to balance the tradeoff between robustness and efficiency. However

Observations of glacier albedo are sparse, despite being a first-order control on net solar radiation and melt rates. Recent and forthcoming airborne and satellite imaging spectrometer missions increase our ability to record changes in albedo over time and space but will require an understanding of uncertainties, especially in areas of rugged terrain. Some of these uncertainties arise from the timing

Active microwave measurements have the potential to estimate vegetation optical depth (VOD), an indicator related to vegetation water content and biomass. The Advanced SCATterometer (ASCAT) provides long-term C-band backscatter data at vertical-vertical (VV) polarization from 2007. So far, very few studies have considered retrieving VOD from this active sensor. This study presents a new publicly released