Climate warming in northern and Arctic regions drives vegetation growth and shifts species distribution. In northern Quebec's Boreal-Arctic transition (forest-tundra ecotone), this is seen in the replacement of lichen by shrubs, primarily dwarf birch. These changes impact surface albedo, contributing to climate forcings with broad consequences. This study measures vegetation changes in Tasiapik valley

This special issue explores the burgeoning field of deep learning for remote sensing time series analysis. The 20 contributed papers showcase diverse applications, including land cover mapping, change detection, atmospheric and biophysical/biochemical parameter retrieval, and disaster monitoring. The articles demonstrate a variety of approaches to address the challenges of irregular time series, such

Quantifying landslide susceptibility saves lives, especially in populous areas exposed to wet climates. However, available hydrological data sets such as precipitation and soil moisture are usually from reanalysis with a few to tens of kilometers' coarse resolution compared to the dimensions of landslides. Here we aim to seek substitutes to characterize hydrological features with finer spacing for

The authors regret an error in the second bullet point of the “Highlights” section in the published article. The original statement:

Knowledge of anisotropy-induced spatial and temporal variations of land surface temperature (LST) is crucial for enhancing the quality of remote sensing products, refining land surface process modeling, and optimizing climate models. However, the limited availability of simultaneous multi-angle LST observations from space has hindered the exploration of this topic. NASA's latest ECOSTRESS sensor deployed

Soil salinization, a critical form of global soil degradation, threatens agricultural productivity and ecosystem functions. Accurate mapping of soil salinity is essential for sustainable land management and informed decision-making. However, conventional optical or radar satellite sensors are often limited in detecting key salinity spectral signatures due to their insufficient thermal infrared (TIR)

Topography alters both the incident radiation and radiative transfer (RT) processes within the canopy, leading to changes in the canopy bidirectional reflectance factor (BRF). Most traditional semi-physical terrain correction (TC) methods for vegetation canopy BRFs rely on simplifying physically-based analytical RT models. However, these analytical RT models are not comprehensively parameterized for

Sun-induced chlorophyll fluorescence (SIF) is increasingly recognized as a non-destructive probe for tracking terrestrial photosynthesis. Emerging developments in spectral invariants theory provide an innovative and efficient approach for representing SIF radiative transfer processes at the canopy scale. However, modeling leaf-scale fluorescence based on the spectral invariants properties (SIP) remains

Characterizing sea surface temperature (SST) variability is a critical aspect of studying long-term changes in estuarine environments. However, the scales of estuarine variability and change can be quite small (10 m–10 km). In this study, we present the first combined analysis of an estuary using the 39-year-long SST evolution from the multi-satellite Landsat data (∼18 day average sampling), over a

Leaf chlorophyll content (LCC) is a crucial biochemical parameter for monitoring the plant's nutritional status and photosynthetic capacity. However, retrieving LCC from canopy reflectance is challenging due to the coupling influence of LCC and canopy structure, particularly leaf area index (LAI). The isolation of leaf-scale information from canopy signals is therefore essential to improve the LCC

A massive inversion of the Hapke model is carried out over the Asal-Ghoubbet rift (Republic of Djibouti) using high-resolution multiangular Pleiades images. This is the first time that such an inversion is performed on Earth over an entire image, previous studies having focused on planetary surfaces. This work addresses challenges such as atmospheric and geometrical corrections of these images to produce

Fire severity is closely linked to ecosystem responses. As climate change increases the frequency of severe fires, large-scale fire severity monitoring has become increasingly important. The traditional bitemporal approach, which compares single pre- and post-fire images to map fire severity, is effective at local scales but less efficient for larger-scale assessments. Recent advances in compositing

Satellite-based precipitation products have significantly advanced applications in hydrology, climate science, and related fields. Despite the significant role of the Global Precipitation Measurement (GPM) mission in monitoring global precipitation dynamics over the past decade, its performance on sub-daily time scales remains insufficiently explored on a global scale. This study provides a continental

Spatiotemporal fusion of multisource remote sensing data offers a viable way for precise and dynamic Earth monitoring. However, existing methods struggle with reliable spatiotemporal fusion in two commonly occurring yet complex scenarios: drastic surface changes, such as those caused by natural disasters and human activities, and poor image quality, which caused by thick cloud cover, cloud shadows

Close-range remote sensing technologies show great potential to support national forest inventories (NFIs). Mobile laser scanning (MLS) has performed especially well in recent studies, as it is capable of fast data acquisition while still delivering accurate information on NFI-relevant variables (e.g. tree position, diameter at breast height [DBH]). However, the performance of MLS acquisition under

The emission, transmission, and secondary generation of atmospheric pollutants occur not only in proximity to the ground but also at elevated altitudes. Vertical distribution plays a pivotal role in understanding the intricate mechanisms that govern atmospheric pollutants. Although ground-based remote sensing offers valuable insights into vertical pollutant profiles, it is limited to obtaining vertical

Radiative transfer (RT) theories formulate vegetation radiative transfer models (RTMs) that link the biophysical properties of vegetation with remote sensing signals. Compared to classical RT theories, the recollision probability theory (also known as p-theory) is distinctive as it predicts some optical properties of vegetation canopies using fewer spectral invariants and simpler mathematical functions

Unmixing optical properties (OP) of land covers from coarse spatial resolution images is crucial for microclimate and energy balance studies. We propose the Unmixing Spectral method using Discrete Anisotropic Radiative Transfer (DART) model (US-DART), a novel approach for unmixing endmember OP in the shortwave domain from mono- or multispectral remotely sensed images. US-DART comprises four modules:

Accurate retrieval of sea surface wind speed is crucial for ecological research and marine resource development. The advent of satellite technology provides a feasible approach for global wind speed retrieval. As a photon-counting lidar, ICESat-2 provides unparalleled details of the sea surface and has the potential for sea surface wind speed retrieval. To facilitate the retrieval of sea surface wind

New advancements in satellite technology enable more accurate observation of woody population dynamics, providing greater insights into the underlying processes that influence their change. In this study, we evaluate the use of PlanetScope NDVI time series to track the phenology of individual trees in the Sahel, where ground-based environmental surveys are scarce. Five-year NDVI time series were produced

Soil moisture (SM) is a key state variable in agricultural, hydrological, and ecological studies. Microwave remote sensing can retrieve soil moisture at regional or global scales, but is limited by coarse spatial resolution. In order to generate large-scale, spatiotemporally seamless soil moisture of high precision, we propose a two-stage downscaling and correction cascade learning framework by fusing

Climate change significantly compromises global water resources, particularly shallow aquifer systems, which are vulnerable to variations in precipitation and evapotranspiration. This study investigated the impacts of climate change on a shallow aquifer system in the Gniezno Lakeland, Poland, by analysing the relationship among ground motion, hydraulic head changes, surface water variations, and meteorological

The Sustainable Development Science Satellite 1 (SDGSAT-1) is the first satellite developed specifically for implementing the UN 2030 Agenda for Sustainable Development. The multispectral imager (MII) onboard SDGSAT-1 provides advanced capabilities for coastal and inland water environment analysis but requires comprehensive radiometric performance evaluation for effective water monitoring. In this

Standard ocean colour algorithms exploiting only shorter visible wavelengths (less than 560 nm) perform poorly in the Arctic Ocean (AO) due to the interference from colored detrital material (CDM). The incorporation of longer wavelengths, which are less susceptible to interference from CDM, could prove beneficial in retrieving water properties, particularly in Arctic waters with high CDM content. Similarly

The Landsat 1–5 Multispectral Scanner (MSS) acquired images in 1972–1992, but their usage is limited, particularly by low geometric accuracy. In the latest USGS Landsat Collection 2 processing, <1 % MSS archive could be geometrically processed to the highest-level Tier 1 category. We present a novel methodology to characterize and correct MSS geometric distortions using Landsat 8 and 9 OLI L1TP images

Several methods were developed in Ocean Colour remote sensing over the last 25 years to model the anisotropy of the upwelling radiant field with respect to observation and solar-illumination geometries, also denoted as bidirectional reflectance distribution function (BRDF). These methods are necessary to produce normalized, or “BRDF-corrected,” marine reflectance representative of the seawater's inherent

Global Navigation Satellite System-Reflectometry (GNSS-R) has considerable potential for large-scale soil moisture (SM) monitoring. With the Fengyun-3 (FY-3) E, F, and G satellites currently in orbit, the FY-3 satellite series has formed the GNSS-R constellation. A comprehensive analysis and validation of the SM retrieval capability of the FY-3 GNSS-R constellation observations are essential. This

Reservoir landslides are the focus of geohazards associated with mega hydropower projects and have been extensively studied by monitoring their post-impoundment deformation. However, how landslide deformation changes before, during, and after impoundment is rarely known. Using satellite radar interferometry, we map 200 active landslides with their time-series deformation spanning the impoundment of

Non-destructive methods are widely used for field measurement of leaf area index (LAI). However, the above-ground woody components of trees and shrubs, i.e., trunks and branches, largely affect the measured gap fraction, thus hindering the accurate measurement of LAI. Many efforts have been made to correct for the woody component effect and estimate LAI, but there is a lack of research to systematically

Observations of mesoscale eddy structures rely heavily on satellite altimetry data. However, due to altimetry's coarse spatial resolution, the fine structure of eddy dynamics remains mysterious. Using high spatiotemporal resolution observations from the Geostationary Ocean Color Imager (GOCI), we reveal the fine structure and hourly dynamics of the eddy surface flow velocities, as well as the horizontal

Crop models are essential for evaluating the effects of climate change on crop yields, optimizing agronomic practices, and guiding policy decisions to enhance food security. However, using traditional crop models, including both process-based and statistical models, for regional applications presents significant challenges. Process-based crop models often require extensive, locally-sensed inputs to

NASA's Plankton, Aerosol, Clouds, ocean Ecosystem (PACE) mission, launched on 8th February 2024, carries a hyperspectral radiometer, Ocean Color Instrument (OCI) and two multi-angle polarimeters, Hyper Angular Rainbow Polarimeter (HARP2) and Spectro-Polarimeter for Planetary Exploration one (SPEX-one). The simultaneous deployment of these sensors offers an unprecedented opportunity to derive more accurate

Land surface phenology (LSP) has been widely generated using traditional methods of fitting satellite-based time series of vegetation indices over the past two decades. However, these methods are highly vulnerable to the presence of temporal gaps and the use of specific smoothing or gap-filling algorithms. Several attempts have recently used Convolutional Neural Networks (CNNs) and Recurrent Neural

Planting density and leaf angle are important factors related to rice growth and yield through interplant competition. Despite the necessity of understanding the dynamics of interplant competition according to planting density and leaf angle, detailed physiological changes throughout the growth cycle remain less clear due to the requirement for field surveys that are labor-intensive and time-consuming

InSAR and optical pixel offset tracking (POT) are two efficient tools for monitoring landslide displacements, but limitations in resolving 3D displacements constrain the full exploration of kinematic behaviors, especially for complex landslides exhibiting diverse movement types. In this study, we propose a technical route that combines SAR and optical images to reveal the spatiotemporal evolution of

Land surface temperature (LST) from satellite images contains meaningful signatures of geothermal heat flux (GHF) for geothermal exploration. However, the signal is mixed with solar radiation dominated features, making it difficult to identify GHF anomaly. Here we propose a novel method to tackle this problem that removes the time variant solar component based on principles of energy balance. Through

Tropical forests in the Amazon are characterized by a dry-season green-up, indicating a light-dominated regime in the seasonal variation of ecosystem functions. Southeast Asia, which hosts some of the most carbon-dense and diverse ecosystems in the world, is also expected to green up in dry seasons, however, recent in-situ evidence suggests otherwise. Here, we utilized high-frequency observations from

This study introduces a novel approach for mapping annual fractional vegetation cover in Sub-Saharan rangelands. We used Sentinel-2 time series data from October 2022 to October 2023 to derive phenological metrics, including the dry season integral and rate of greenness decline after peak season. Phenological metrics effectively separate woody vegetation from herbaceous plants based on their distinct

The objective of this study is to investigate the relationship between the Normalized Difference Vegetation Index (NDVI) and Synthetic Aperture Radar (SAR) data at multiple frequencies, focusing on S- and C-band data with additional analysis for X- and L-band. This is the foundation for the translation of SAR data into NDVI values, thereby enabling the filling of gaps in NDVI data due to cloud cover

While severe hurricanes continue to challenge the resilience of local communities, fine-scale knowledge of post-hurricane recovery remains scarce. Existing recovery tracking approaches mainly rely on aggregated metrics that would disguise the spatial heterogeneity in recovery patterns. Here, we present a spatiotemporally explicit investigation into the recovery of human activity after 10 recent severe

Recent advances in remote sensing technology have facilitated the emergence of high-quality hyperspectral satellite sensors with spatial resolutions comparable to well-established multispectral platforms like Landsat series and Sentinel-2. However, most hyperspectral satellite datasets suffer from limited temporal resolution, hindering the effective monitoring of rapid changes on the Earth's surface

Spatial variability has been one of the major challenges for large-area crop monitoring and classification with remote sensing. Recent works on deep learning have introduced spatial transformation methods to automatically partition a heterogeneous region into multiple homogeneous sub-regions during the training process. However, the framework is only designed for deep learning and is not available

Mapping tree crops is essential for resource management and supporting local livelihoods and ecosystem services. However, tree crops are often overlooked or misclassified in regional and global cropland maps. Employing multi-sensor imagery presents new opportunities for mapping tree crops by providing additional observations and distinct characteristics. Nevertheless, challenges regarding the scarcity

The aerosol optical thickness (AOT) and fine-mode fraction (FMF) are crucial to understanding the radiative and environmental effects of aerosols. However, accurately retrieving these properties simultaneously from monodirectional multispectral satellite data remains challenging. Inversion algorithms based on lookup tables typically leverage information from only two or three channels, resulting in

Land surface temperature (LST) data are crucial for global climate change research. While remote sensing data serve as a key source for LST, single-source sensor data often lack spatiotemporal continuity due to long satellite revisit intervals and cloud cover. Spatiotemporal fusion, which combines the strengths of multiple sources, can increase the available information. However, most current spatiotemporal

The Multiple Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) technology is capable of effectively generating ground deformation information derived from high-precision and continuous observation by satellites. However, due to the limited operational lifespan of a single SAR satellite, the derived ground deformation result of the study area cannot be ensured long-term (several decades)

Due to the great advancements in land surface models (LSMs), integrating data from thermal infrared (TIR) and LSMs is a promising way for obtaining gapless all-weather land surface temperature (LST). However, the differences of spatial resolution and discrepancy of data acquisition ways between TIR LST and model-simulated LST usually brought great challenges to traditional methods in terms of accuracy

The sensitivity of microwave data in soil moisture is attributed to radar wave penetration depth and signal attenuation. However, current soil moisture models rarely consider the simultaneous effects of amplitude and phase induced by soil moisture. This study proposes an innovative InSAR Bias Soil Moisture Model (IBSMM) that jointly exploits closure phase and closure amplitude. Compared with traditional

Coastal farmlands in the eastern United States of America (USA) are increasingly suffering from rising soil salinity, rendering them unsuitable for economically productive agriculture. Saltwater intrusion (SWI) into the groundwater reservoir or soil salinization can result in land cover modification (e.g. reduced plant growth) or land cover conversion. Two primary examples of such land cover conversion

Vegetation Optical Depth (VOD), obtained from passive microwave sensors, quantifies Vegetation Water Content (VWC) and complements conventional vegetation indices. Recent studies on Soil Moisture (SM) and VOD retrieval algorithms identified that VOD is more susceptible to errors due to the Radiative Transfer Model (RTM) and its parameterization than SM. The present work aims to address this limitation

Remotely sensed top-of-the-canopy (TOC) SIF is highly impacted by non-physiological structural and environmental factors that are confounding the photosystems' emitted SIF signal. Our proposed method for scaling TOC SIF down to photosystems' (PSI and PSII) level uses a three-dimensional (3D) modeling approach, capable of accounting physically for the main confounding factors, i.e., SIF scattering and

Optical remote sensing of soil and vegetation moisture index is widely recognized as a vital indicator for monitoring soil moisture and drought stress. Nevertheless, the traditional soil and vegetation moisture index does not adequately capture enough higher-order relations between spectral channels, leading to limited sensitivity to soil moisture variations in certain value ranges and difficulties

Gross primary productivity (GPP) is more accurately estimated by total canopy solar-induced chlorophyll fluorescence (SIFtotal) compared to raw sensor observed SIF signals (SIFobs). The use of two-leaf strategy, which distinguishes between SIF from sunlit (SIFsunlit) and shaded (SIFshaded) leaves, further improves GPP estimates. However, the two-leaf strategy, along with SIF corrections for bidirectional