Accurate retrieval of land surface temperature (LST) and land surface emissivity (LSE) from hyperspectral thermal infrared (TIR) observations remains challenging due to the ill-posed nature of temperature–emissivity separation (TES) and its sensitivity to atmospheric uncertainties. This study proposes a TES retrieval method that integrates a refined water vapor scaling (WVS) strategy with an adaptive continuous piecewise linear (ACPL) emissivity model. The WVS component reduces residual atmospheric bias by adaptively scaling water-vapor-related parameters within the radiative transfer model, while the ACPL model introduces adaptive spectral segmentation with explicit continuity constraints to preserve diagnostic emissivity features without oversmoothing. Controlled simulation experiments demonstrate the effectiveness and robustness of the proposed method. Under water vapor profile perturbations within ±30%, LST and LSE RMSEs remain stable at 0.57–1.13 K and 0.0160–0.0247, respectively. Even when additional instrument noise (0.1–0.5 K) is introduced, the method maintains stable performance, with LST RMSE within 0.74–1.52 K and LSE RMSE below 0.0306. Application to airborne LWIR hyperspectral data further demonstrates the practical applicability of the proposed method. Retrieved emissivity spectra at a quarry site agree well with in situ measurements (RMSE < 0.014), and class-wise analyses across diverse land-cover types show stable intra-class variability and clear inter-class separability. These results indicate that integrating adaptive emissivity modeling with atmospheric scaling improves the reliability of hyperspectral LST and LSE retrieval under both controlled and real observational conditions.

Timely and transparent agricultural statistics are essential for safeguarding global food security. When the war in Ukraine disrupted agricultural reporting - particularly in Russian-held territories - complementary and/or alternative approaches were needed to produce reliable statistics. We developed a fully remote sensing-based framework to estimate areas of two major crops, wheat and rapeseed, from 2022 to 2025. Using Planet and Sentinel-1/2 imagery, we applied clustering techniques to generate in-season crop type maps that supported stratified random sampling in sample-based area estimation, using remotely interpreted reference data. Our estimates closely matched official statistics in Government-controlled areas (RMSE = 0.138 and 0.32 million hectares (Mha) for wheat and rapeseed, respectively), while filling critical data gaps in Russian-controlled regions. Between 2022 and 2025, wheat area declined from 5.14 +/- 0.52 to 4.84 +/- 0.25 Mha in Government-controlled areas and from 2.06 +/- 0.16 to 1.55 +/- 0.09 Mha in Russian-held regions. Rapeseed expanded from 1.16 +/- 0.17 to 1.47 +/- 0.23 Mha (2022-2025) in Government-controlled territories but collapsed in Russian-held areas, from 0.17 +/- 0.01 to 0.05 +/- 0.01 Mha. Our findings underscore the critical role of remote sensing in providing timely, transparent, and independent agricultural statistics to support informed food security and market-stabilizing decision-making.

Land surface temperature (LST) is of fundamental importance to many aspects of geosciences, such as, net radiation budget, evaluation and monitoring of forest and crops. The LST is also a key-parameter to derive the Surface Urban Heat Island (SUHI) or health related indices, for example, the Discomfort Index (DI). It is therefore an essential prerequisite to understand the urban climate and to support the definition of mitigation strategies, health risk management plans, public policies among other initiatives to effectively address the adverse effects of heat. While the LST is commonly retrieved from data acquired in the TIR (Thermal InfraRed) spectral domain by remote multispectral sensors with about 1K accuracy for natural surfaces, its retrieval over urban areas is not trivial. Urban landscapes possess tremendous challenges in LST estimates due to its high heterogeneity of surfaces and materials, and the three-dimensional (3D) configuration of the elements that are present on urban areas. The Thermal InfraRed Imaging Satellite for High-resolution Natural resource Assessment (TRISHNA) is planned for launch in 2025 and features a TIR instrument that will image the Earth every three days, at 57 m resolution, providing the research community with critical information to understand the radiative interactions and impacts al local level. This new satellite mission will offer an unprecedented opportunity to support urban microclimate studies. Based on extensive radiative transfer simulations using the Discrete Anisotropic Radiative Transfer Model (DART) and sensitivity analysis, this work investigates the impacts of 3D urban structures (e.g., road width, building height, building density) and materials with different optical properties on LST estimation at the TRISHNA spatial resolution. In fine, the idea is to develop a method to minimize these impacts on LST estimated from the TRISHNA data. First, a processing chain has been set up to simulate TRISHNA LST with DART, by using as inputs i) the configuration of the sensor and ii) 3D urban forms with different geometric and optical properties. Second, the radiative transfer modeling for simulation of the TIR remote sensing signal is performed. Finally, by correlating the simulated TRISHNA LST and the surface characteristics for each scene, the main parameters impacting the LST in urban environments have been identified. From these results, a correction method at satellite scale to minimize the impacts of urban 3D variables on LST will be formulated.

River discharge plays an indispensable role in maintaining the stability of the hydrosphere system and eco-environment. Previous methods that utilize satellite imagery to estimate discharge over poorly gauged basins are generally tailored for large rivers and heavily reliant on ground-based measurements. Consequently, uncertainties often escalate when these methods are applied to medium-sized rivers. Based on Landsat 5 Thematic Mapper (TM) and unmanned aerial vehicle (UAV) images, this study proposed a framework for estimating the discharge of large and medium rivers with limited ground observations. It comprises (1) a modified C/M method, which considers the spatial heterogeneity of rivers using single-site observation data, and (2) a newly developed method for estimating river bathymetry with zero discharge measurements (RIBA-zero). Results show that, utilizing the modified C/M method, rivers wider than three times the satellite resolution (i.e., 90 m) exhibit a relative root mean square error (rRMSE) of 0.23 in the velocity estimation. Narrower rivers display a slight increase in the rRMSE (0.41), which is still within an encouraging range. For both types of river widths, the accuracy of flow velocity estimation is higher during high-flow periods compared with the low-flow counterparts. In terms of the flow area estimation, the RIBA-zero method is much more suited for parabola-shaped cross-sections (rRMSE = 0.22) and flood seasons (rRMSE = 0.35). Additionally, when replacing 30-m Landsat 5 TM with 10 m-resolution Sentinel-2 imageries, the approaches make a significant improvement in velocity estimation for rivers narrower than 90 m across all periods, exhibiting great potential to estimate discharge in medium rivers with finer resolution satellite imageries. The framework requires a few ground observations for discharge estimates with the Nash-Sutcliffe efficiency coefficient (NSE) reaching similar to 0.9, thereby greatly facilitating hydrology-related studies with profound implications for sustainable water resources management worldwide.

The intensification of the urban thermal environment has brought attention to urban land surface temperature (ULST). Complex building geometry and manmade material lead to significant thermal radiation directionality (TRD) of the urban canopy, and the TRD effect directly influences the accuracy of ULST retrieval algorithms. Therefore, it is essential to understand and eliminate the TRD effect to achieve high-accuracy ULST. In this context, the hemispherical brightness temperature maximum–minimum discrepancy (BTD) was quantitatively analyzed via different spectral bands, component temperature thresholds, urban geometries, and component temperature differences. Meanwhile, the DART simulations database was used to systematically evaluate 1 single-kernel- and 30 dual-kernel-driven models (KDMs), which were combined from 5 base-shape kernels (RossThick, Vinnikov, uea, RossThin, and LSF) and 6 hotspot kernels (RL, Roujean, Vinnikov, LiSparseR, LiDense, and Chen). Results show that the BTD discrepancy (ΔBTD) can reach up to 0.91 K with different band emissivities, whereas the ΔBTD is over 10 K with different component temperature differences. The building density and ratio between building heights and road widths (H/W) also exhibit their importance over urban regions. In addition, the RossThick–/Vinnikov–Roujean dual-kernel KDMs demonstrate better performance with an overall RMSE of 1.12 K. The RL-series KDMs can describe the hotspot distribution well, but the uea-series KDMs outperform at the solar principal plane (SPP) and cross-solar principal plane (CSPP). Specifically, the performance of all KDMs is sensitive to the H/W and component temperature thresholds, and urban geometry can affect the TRD RMSE with increasing H/W and a depletion of high building density. The quantitative TRD analysis and comparison provide a comprehensive reference for understanding the distribution of thermal radiation, which is also a reliable basis for developing the new TRD model over urban regions.

A series of empirical analytical tools have been adopted to investigate the driving mechanisms of surface urban heat islands (SUHI) on a global scale, among which spatial heterogeneity is yet to be fully elucidated. In this study, we investigated the spatial non-stationarity of the driving factors concerning surface properties, climate conditions, and urbanization processes for global long-term SUHI. First, the potential impact on SUHI was explored using global ordinary least squares regression. Geographically weighted regression (GWR) and multi-scale GWR (MGWR) from local perspectives were employed for comparison. The results show that the MGWR has the highest goodness of fit at 0.87, 0.73, 0.90, 0.74, 0.85, and 0.76 for annual day/night (AD/AN), summer day/night (SD/SN), and winter day/night (WD/WN) scales, respectively. Although both global and local schemes exhibit similar influencing magnitudes and signs on the SUHI, the MGWR is better at capturing spatial non-stationarity. Globally, for AD, AN, SD, SN, WD, and WN, the coefficients of the urban-rural vegetation index difference (Delta EVI) and surface albedo difference (Delta WSA), urban mean precipitation (MAP), wind speed (WS), population density (PD), and urban area (UA) are -0.50, +0.30, +0.16, +1.31, -0.03, and +0.03, respectively, at daytime, and -0.38, -0.33, -0.39, -0.10, +0.18, and +0.08, respectively, at night-time. Given the spatial heterogeneity of multiple factors, Delta EVI exhibits a strong mitigation effect on the SD SUHI especially in arid zones. The negative influence of Delta WSA on night-time SUHI demonstrates a strong latitudinal disparity and greater sensitivity in the equatorial zone. The positive correlations between MAP and AD/SD SUHIs have evident latitudinal and longitudinal variations. The mitigation effect of WS displayed distinct coastal amplification, especially in WD. In contrast, the PD and UA presented prominent positive impacts on night-time SUHI with less seasonal contrast.

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Leaf area (LA) parameters are crucial in ecosystem studies. As ecophysiological models advance toward finer detail, accurately estimating LA at various scales becomes essential, particularly for diverse units like urban individual trees. Several algorithms based on terrestrial laser scanning (TLS) data have been developed to obtain the LA of individual trees. However, their use at the stand level needs further research. In this study, the comparative shortest-path algorithm (CSP) is introduced for the automatic individual tree segmentation, thereby facilitating the application of the path length distribution method (PATH) for LA estimation at the stand level. Using high-density TLS data, we presented a bottom-up estimation of stand LA index (LAI) from 50 individual tree measurements and validated the results at different scales. At the tree scale, the LA derived from TLS and the allometric model were highly correlated, with an $R$ -value of 0.83. At the stand scale, the proposed method provides consistent results with the allometric and TRAC instrument measurements, performing better than vertical upward photography. Generally, 23 shared stations under the forest are enough to accurately obtain the LA of 50 trees and the LAI in an urban forest stand. Sensitivity analysis shows that the method is not sensitive to TLS scan resolution and parameters used in tree crown envelope reconstruction. The proposed bottom-up approach provides a new way of estimating the LAI at stand level using TLS and has the advantage of providing multilevel LA information and avoiding the scale effect.

The urban–rural temperature difference is widely used in measuring surface urban heat island intensity (SUHII), where the accurate determination of rural background is crucial. However, traditionally, the entire permeable rural surface has been selected to represent the background temperature, leaving uncertainty about the impact of non-uniform rural surfaces with multiple land covers on the accuracy of SUHII quantification. In this study, we proposed two quantifications of SUHII derived from the primary (SUHII1) and secondary (SUHII2) land types, respectively, which successively occupy over 40–50% of whole rural regions. The spatial integration and temporal variation of SUHII1 and SUHII2 were compared with the result from whole rural regions (SUHII) within 34 urban agglomerations (UAs) in China. The results showed that the SUHII1 and SUHII2 differed slightly with SUHII, and the correlation coefficients of SUHII and SUHII1/SUHII2 are generally above 0.9 in most (32) UAs. Regarding the long-term SUHII between 2003 and 2019, the three methods demonstrated similar seasonal patterns, although SUHII1 (or SUHII2) tended to overestimate or underestimate compared to SUHII. As for the multi-year integration at the regional scale, the day–night cycle and monthly variations of SUHII1 and SUHII were found to be identical for each geographical division separately, indicating that the spatiotemporal pattern revealed by SUHII is minimally affected by the diversity of rural landcover types. The findings confirmed the viability of the urban–rural LST difference method for measuring long-term regional SUHII patterns under non-uniform rural land cover types.

Land surface temperature (LST) is a crucial parameter needed to study the thermal environment in urban areas. Currently, it can be restored from thermal infrared (TIR) measurements based on various LST retrieval algorithms. However, the expected urban LST retrieval accuracy of < 1 K is difficult to achieve because knowledge is lacking on how to correct the impact from the surface of 3-D structures and the sunlit-shadow temperature contrast. Although an analytical TIR radiative transfer model over urban area (ATIMOU) has been proposed, the temperature contrast between sunlit and shadowed areas has not been well-managed yet, thus leading to its inapplicability in daytime TIR observations. This study develops an extended ATIMOU (E_ATIMOU) that considers the impact from both 3-D structures and sunlit-shadow temperature contrast. According to the simulations based on E_ATIMOU, if such impact is not properly accounted for, a 4.43-K bias can be potentially introduced to the ground brightness temperature of a street canyon under the condition of wavelength of $10 similar to\mu \text{m}$ , ratio "sunlit-road area/total-road area" of 0.5, shadowed wall and road temperature of 300 K, and the sunlit-shadow temperature contrast of 5 K, which emphasizes the necessity of addressing this impact during the LST retrieval in urban areas. Moreover, E_ATIMOU has also been validated by intercomparing with the discrete anisotropic radiative transfer (DART) model. The discrepancy between the two models for the calculated ground brightness temperatures is found to be < 0.1 K for various urban scenarios, indicating that the E_ATIMOU is in good agreement with DART.

The CAMCATT-AI4GEO extensive field experiment took place in Toulouse, a city in the southwest of France, from 14th to 25th June 2021 (with complementary measurements performed on the 6 September 2021). Its main objective was the acquisition of a new reference dataset on an urban site to support the development and validation of data products from the future thermal infrared (TIR) satellite missions such as TRISHNA (CNES/ISRO), LSTM (ESA) and SBG (NASA). With their high spatial (between 30-60m) and temporal (2-3 days) resolutions, the future TIR satellite data will allow a better investigation of the urban climate at the neighbourhood scale. However, in order to validate the future products of these missions such as LST, air temperature, comfort index and Urban Heat Island (UHI), there is a need to accurately characterise the organisation of the city in terms of 3D geometry, spectral optical properties and both land surface temperature and emissivity (LST and LSE) at several scales. In this context, the CAMCATT-AI4GEO field campaign provides a set of airborne VISNIR-SWIR (Visible Near InfraRed - ShortWave InfraRed) hyperspectral imagery, multispectral thermal infrared (TIR) imagery and 3D LiDAR acquisitions, together with a variety of ground data collected, for some of them, simultaneously to the flight. The ground dataset includes surface reflectance measured spectrally with ASD spectroradiometers and in six spectral bands spreading from shortwave to thermal infrared and for two viewing angles with a SOC410-DHR handheld reflectometer. It is completed with LST and LSE retrieved from thermal infrared radiance acquired in six spectral bands with CIMEL radiometers. It also includes meteorological data coming from four radio soundings (one of which was taken during the flight), data routinely collected at the Blagnac airport reference station as well as air temperature and humidity acquired using instrumented cars following two different itineraries. In addition, a link is provided to access the data routinely collected by the network of weather stations set up by Toulouse Metropole in the city and its surroundings. This data paper describes this new reference urban dataset which can be useful for many applications such as calibration/validation of at-surface radiance, LST and LSE data products as well as higher level products such as air temperature or comfort index. It also provides valuable opportunities for other applications in urban climate studies, such as supporting the validation of microclimate models.

The invasion of Ukraine by Russian forces was expected to have global impact on food trade and security, since Ukraine is a breadbasket cereals and oil seeds producer. The NASA Harvest « Rapid Agricultural Assessment for Policy Support » (RAAPS) team was triggered early in the conflict to provide answers to the following questions : (i) How much winter cereals, winter oil seeds and summer crops were planted in Ukraine during the 2021-2022 cropping season?(ii) What proportion of those crops fell under the Russian occupied area? (iii) How much cropland was left unplanted in 2022 due to the war?As insights had to be produced within season, the NASA Harvest RAAPS team produced the first ever, Ukraine scale in-season crop type map based on Planet Labs 3 meter spatial -, 4 bands spectral -, and daily temporal – resolution data. Since no labeled datasets were available early enough in-season for applying supervised machine learning techniques, cropland was progressively mapped into four classes (winter cereals, rapeseed, summer crops and barren/non cultivated plots), using semi-supervised clustering techniques and heuristical thresholdings. Expert domain knowledge allowed to cope with missing ground truth training data. First, active cropland was separated into winter crops and potential summer crops. K-means clustering of April and May Planet images, followed by visual cluster assignment, allowed to efficiently separate green crops (winter crops) from barren soils (potential summer crops). Then, another K-means clustering allowed to split winter crops into winter cereals and rapeseed as of end of May, based on the intense yellow flowering signal of the latter. Finally a set of NDVI based heuristics was applied on potential summer crops in order to assess if green-up happened or not. Crops which did not green up as of the 11th of July 2022 were considered barren/non-planted. Road side ground surveyed crop type information collected in free Ukraine has been provided by Kussul & al. (2022) in August 2022. Validation against this data provided an overall accuracy of 94 % and a mean F1-score of 91 % for winter cereals, rapeseed and summer crops. No unplanted fields were collected as part of the ground campaign. Several assessments of proportional area per crop type and occupation status were performed throughout the growing season, as occupation boundaries kept moving. As of the 11th of July 2022, 23.03 % of Ukraines cropland was occupied. 55.29 % of all detected barren fields were located within occupied territories, mainly scattered around the front line. 33.9 % of all winter crops were under occupied territory when harvest ready (mid July). This crop type map was used for computing harvested area, estimating yield and for production computation. Following NASA EarthObservatory articles were published, providing information to the public and private sector : (i) https://earthobservatory.nasa.gov/images/150025/measuring-wars-effect-on-a-global-breadbasket (ii) https://earthobservatory.nasa.gov/images/150590/larger-wheat-harvest-in-ukraine-than-expected

In a context of more frequent heat waves, urban overheating is a major environmental and public health issue. The Land Surface Temperature (LST) is a key variable to study this phenomenon and its estimation at district or city scale can help to diagnose the thermal behaviour of existing or future infrastructures. This paper compares in-situ sensors, 3D physical models and remote sensing observations to investigate the potential and accuracy of each approach to monitor LST at canyon scale. This comparison is performed based on the datasets acquired during the CAMCATT-AI4GEO experiment led in Toulouse city in June 2021 and a side experiment evaluating iButtons data using KT19 measurements as reference. This work shows that iButtons provide LST within +/- 1.25°C (over white walls) if the sensor is protected from direct sun. They offer a good spatial coverage over a small scene, providing direct LST measurement, but they seem sensitive to dark colour walls and sun exposition. On-ground TIR cameras allow for fine scale monitoring of the spatial and temporal variation of the LST, which provides information on the thermal behaviour of the surfaces over limited portion of the scene. On the other hand, airborne cameras allow to retrieve LST over horizontal surfaces at flight overpass time, which give an instant picture of the LST spatial variability at district or city scale. In both cases, retrieving LST from the thermal infrared images can be challenging. Finally, micro-climat models allow to simulate LST at high spatial and temporal resolutions up to district scale, provided that meteorological data are available and that the scene parametrization is correct. Each approach has advantages and drawbacks in terms of accuracy, spatial and temporal coverage, but they can also benefits from a combined used.

In this study, the global surface urban heat island (SUHI) for 1711 cities during 2003–2019 was quantified by the dynamic urban-extent (DUE) scheme with the land surface temperature datasets from Moderate Resolution Imaging Spectroradiometer Terra and Aqua through the Google Earth Engine platform. The global pattern and regional contrasts of SUHI intensity (SUHII), and the interannual changing rate of SUHII (δSUHII) were revealed at the annual, summer, and winter scales. Further, the associated driving factors for long-term SUHII were explored from a temporal perspective. The main findings are as follows: (1) Globally, the global mean SUHII over 2003–2019 for annual daytime (1.32 °C) and annual nighttime (1.09 °C) by DUE are generally higher than that by previous simplified urban-extent (SUE) scheme. Accordingly, the summer daytime and nighttime SUHIIs are 1.98 °C, 1.05 ℃, while the winter daytime and nighttime SUHIIs are 0.76 ℃, and 1.10 ℃. (2) The annual, summer, and winter δSUHIIs are 0.11 °C/decade, 0.27 °C/decade, and −0.06 °C/decade, respectively, at daytime, and 0.07 °C/decade, 0.09 °C/decade, and 0.10 °C/decade, respectively, at nighttime. (3) The global SUHII and δSUHII demonstrates evident regional contrast. The warm temperate and snow zones show distinct seasonal variations from summer to winter for daytime SUHII. Specifically, the negative daytime SUHII is detected for the arid zone, which exhibits the highest day-night variation and shows decreasing trend. (4) The global SUHII and δSUHII indicate distinct latitudinal variations, and an additional flip-flop (daytime SUHII < nighttime SUHII) region is detected between 10 °S and 20 °S. (5) The long-term daytime SUHII are negatively regulated by the urban–rural difference on evaporative cooling of vegetation; while at nighttime, it is negatively affected by the urban–rural difference on surface thermophysical properties. It implies the urban greening and surface properties should be specifically concerned to increase the evaporation cooling and reduce the heat retention in SUHII mitigation.

Land surface temperature (LST) is an essential input for modeling the processes of energy exchange and balance of the earth's surface. Thermal infrared (TIR) remote sensing is considered to be the most efficient way to obtain accurate LST, both regionally and globally. Currently, many LST retrieval algorithms have been developed, including the up-to-date SW-TES (SW: split window; TES: temperature-emissivity separation) method, which is claimed to be able to accurately derive LST without the need for atmospheric information and land surface emissivity (LSE) based on the selected multiple TIR channel configuration. However, this hybrid method is actually not applicable to observations with large viewing angles and was only preliminarily evaluated in Australia. In this study, this method was extended for application to global TIR measurements with different viewing angles. Additionally, the performance of this extended SW-TES method was assessed globally for different seasons by using the MODIS LST product as a reference, and was also validated using in-situ LST measurements from the SURFRAD (SURFace RADiation budget network) sites. The results showed that the LST retrievals using the extended SW-TES method were comparable to the MODIS LST product, with discrepancies of <2.7 K and < 1.8 K for global daytime and nighttime observations, respectively. Validations based on the SURFRAD in-situ LST measurements indicated that the extended method could be used to retrieve LST accurately with a root-mean-square error (RMSE) of approximately 3.6 K during the daytime and 2.4 K during the nighttime. However, special attention should be paid when applying the extended method to daytime observations on grasslands and shrublands during hot seasons, considering the relatively large discrepancy when using this method compared with that obtained with the MODIS LST product (>4.0 K). Overall, in this study, the SW-TES method was extended, and the performance was comprehensively evaluated at the global scale, which may help in facilitating its potential applications.

In preparation of the micro-bolometer-based MIcro Satellite for Thermal Infrared GRound surface Imaging (MISTIGRI) mission, we study the error budget of the Temperature-Emissivity Separation (TES) method using several spectral configurations that differ in channel numbers, locations, and widths. The error budget quantifies the contribution of 1) the TES underlying assumption about emissivity spectral contrast, 2) the errors on atmospheric corrections, and 3) the instrumental noise. When dealing with atmospheric corrections, we consider errors in atmospheric temperature, water vapor content, and concentrations of CO 2 and O 3 . To that end, we design an end-to-end simulator of MISTIGRI measurements in order to simulate the radiative and biophysical quantities involved in the data processing. We conduct numerous simulations over a wide range of realistic setups that include cavity effect, i.e., radiance trapping within vegetation canopy. In the case of micro-bolometer-based sensing, the current study highlights that atmospheric and instrumental noises have similar impacts on the TES retrievals, with resulting errors twice as large as those due to the TES intrinsic assumption about spectral contrast, where the latter contributes to the TES error budget within the [0.005–0.009] interval for emissivity, and within the [0.3–0.4 K] interval for land surface temperature (LST). Also, we show that retrieval performance of surface temperature is very similar across all considered MISTIGRI spectral configurations, with RMSE variation within 0.2 K. Eventually, our study permits us to select a 4-channels spectral configuration as the most suited for the MISTIGRI instrument, notably because it enables a moderately better capture of the emissivity contrast than a 3-channels one.