Remote Sensing of Environment最新文献

{"title":"A multi-parameter optimized sub-waveform retracker for monitoring river water levels using SAR altimetry","authors":"Xianwen Gao ,&nbsp;Taoyong Jin ,&nbsp;Xiaoli Deng ,&nbsp;Weiping Jiang ,&nbsp;Jiancheng Li","doi":"10.1016/j.rse.2025.114838","DOIUrl":"10.1016/j.rse.2025.114838","url":null,"abstract":"<div><div>Synthetic Aperture Radar (SAR) altimetry has been widely used for monitoring river water levels, especially over large and medium-sized rivers. However, challenges still remain in obtaining continuous and high-precision water levels over small rivers due to the altimeter's sparse along-track sampling, distorted waveforms, and river slopes. This study presents a new multi-parameter optimized sub-waveform (MulPOS) retracker, which retracks the waveforms across all cycles through a quantitatively considered integration of the spatial consistency and temporal continuity of water levels, river slopes, and the strong reflectivity of the river surface. Firstly, along-track sampling is increased by searching for off-nadir observations within half the sampling resolution from nadir water bodies to retrieve continuous river water levels. Secondly, waveform preprocessing, including interpolation and filtering is used to determine more accurate retracking points, and then all possible sub-waveform sets that correspond to river reflections are formed. The most likely sub-waveform sets are determined by their four-parameter weighting function, which considers spatial consistency, temporal continuity of water level variations, and the high reflectivity of the river water surface. Finally, slope corrections are computed using the robust Helmert variance component estimation method by combining the differences between water levels in adjacent cycles and along the track. The MulPOS has been applied to 290 virtual stations formed by Sentinel-3A/3B and Sentinel-6 MF over rivers in the United States (52 % of which are narrower than 100 m). For comparison purposes, six other retrackers have been used, including OCOG, ICE1, threshold, NPPTR, SAMOSA+, and MWaPP+. The results have been validated against the in-situ measurements from the United States Geological Survey, indicating that the water levels derived by MulPOS are superior to other retrackers with a median RMSE of 17.9 cm, a median relative RMSE of 7.2 %, a median correlation coefficient of 0.96, and an abnormal water level occurrence rate of 0.60 %, whereas the corresponding metrics for other retrackers are &gt;24.2 cm, &gt;9.8 %, &lt;0.94, and &gt; 2.36 %. Moreover, MulPOS achieves steady and high-precision water levels across most small rivers under varying river widths (e.g., RMSE for MulPOS is 20.9 cm vs. &gt;29.5 cm for other retrackers over rivers narrower than 50 m), varying angles between satellite ground tracks and rivers, and complex river morphologies. MulPOS is expected to generate a dataset with continuous, high-precision water level data for more small and medium-sized rivers, and this will expand the application of altimetry to inland water monitoring.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"327 ","pages":"Article 114838"},"PeriodicalIF":11.1,"publicationDate":"2025-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144178334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Observing carbon monoxide and volatile organic compounds from Canadian wildfires in 2023 from FengYun-3E/HIRAS-II in a dawn-dusk sun-synchronous orbit","authors":"Jiancong Hua ,&nbsp;Shangyi Liu ,&nbsp;Chengli Qi ,&nbsp;Sirui Wu ,&nbsp;Lu Lee ,&nbsp;Xiuqing Hu ,&nbsp;Xiaoyi Zhao ,&nbsp;Kimberly Strong ,&nbsp;Victoria Flood ,&nbsp;Bruno Franco ,&nbsp;Lieven Clarisse ,&nbsp;Cathy Clerbaux ,&nbsp;Debra Wunch ,&nbsp;Coleen Roehl ,&nbsp;Paul Wennberg ,&nbsp;Zhao-Cheng Zeng","doi":"10.1016/j.rse.2025.114829","DOIUrl":"10.1016/j.rse.2025.114829","url":null,"abstract":"<div><div>This study presents the first attempt to observe wildfire enhancements of carbon monoxide (CO) and volatile organic compounds (VOCs) around sunrise and sunset from a hyperspectral infrared sounder in a dawn-dusk sun-synchronous orbit. The 2nd generation of High Spectral Infrared Atmospheric Sounder (HIRAS-II) on board FengYun-3E (FY-3E), the world's first civilian dawn-dusk orbit meteorological satellite, provides global observations in the thermal infrared spectral range with equatorial overpass times of 5:30 am/pm local solar time (LST). The spectral observations are used to retrieve CO, formic acid (HCOOH) and peroxyacetyl nitrate (PAN) emitted from three major Canadian wildfire events from June to August 2023. Extreme enhancements of CO, HCOOH and PAN were detected in the 2023 Canadian wildfires which are unprecedented in time and spatial scales and intensity. The HIRAS-II successfully captured the strong signals of CO, HCOOH, and PAN. The averaging kernel (AK) matrix, indicative of detection vertical sensitivity, peaks mostly in the free troposphere where extensive transport typically takes place. Comparison with the Infrared Atmospheric Sounding Interferometer (IASI) and the Cross-track Infrared Sounder (CrIS) reveals that the spatial distribution patterns of the total columns extracted from HIRAS-II are in good agreement. Validation with the CAMS model and ground-based observations from TCCON and NDACC confirms that HIRAS-II retrievals are consistent. The HCOOH-to-CO and the PAN-to-CO column enhancement ratios derived from HIRAS-II are close to those derived from IASI. This paper exhibits the capability of FY-3E/HIRAS-II in observing wildfire emissions during dawn and dusk hours, which will potentially enhance the climate-monitoring capability of low-orbit meteorological satellites.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"327 ","pages":"Article 114829"},"PeriodicalIF":11.1,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144137754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Retrieval of terrain surface elevation in mountainous areas with ICESat-2/ATLAS","authors":"Yanli Zhang ,&nbsp;Pan Zhao ,&nbsp;Xin Li ,&nbsp;Bisheng Yang ,&nbsp;Jun Zhao ,&nbsp;Jiazheng Hu ,&nbsp;Qi Wei ,&nbsp;Kegong Li ,&nbsp;Mingliang He","doi":"10.1016/j.rse.2025.114823","DOIUrl":"10.1016/j.rse.2025.114823","url":null,"abstract":"<div><div>Land elevation data are indispensable for topographic mapping and geological disaster monitoring. However, the existing ICESat-2/ATL08 (V04) product has a coarse resolution (≥100 m) and is characterized by high uncertainty in mountainous areas; thus, it cannot be used to describe terrain relief characteristics accurately. In this study, a new method for extracting terrain surface elevation is proposed, which uses a local statistical denoising algorithm for mountainous areas (LSDAMA) based on the raw georeferenced photon product ICESat-2/ATL03. Coarse denoising is based on performing histogram thresholding, and refined denoising is based on local slope fitting; the process of performing coarse denoising twice and then refined denoising not only improves the removal effect for noise photons but also increases the signal photon retention rate in mountainous areas. Additionally, the estimation accuracy of the terrain surface elevation can be improved by setting rectangular dynamic windows along the fitted slope direction. Using the Babao River Basin in the Qilian Mountains as the research area, a total of 137 validation points from 777 GPS CORS in 40 quadrats and UAV LiDAR measurements were used to verify the accuracy. The results showed that the terrain surface elevations estimated by the LSDAMA are more accurate than those estimated by the ATL08 official products, especially in mountainous areas with slopes greater than 20°. The root mean square error (<em>RMSE</em>) of the LSDAMA decreased from 2.72 m for the ATL08 product to 0.60 m, and the mean deviation (<em>MBE</em>) decreased from −1.27 to 0.04 m. Additionally, the LSDAMA greatly improved the signal photon retention rate and reduced the interval between adjacent elevation points from 100 m for the ATL08 product to 2–7 m; this reduced interval can be used to describe the terrain fluctuation characteristics in detail, thus providing reliable basic data for monitoring terrain surface elevation changes in mountainous areas.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"327 ","pages":"Article 114823"},"PeriodicalIF":11.1,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144137753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling 3D radiative transfer for maize traits retrieval: A growth stage-dependent study on hyperspectral sensitivity to field geometry, soil moisture, and leaf biochemistry","authors":"Romain Démoulin ,&nbsp;Jean-Philippe Gastellu-Etchegorry ,&nbsp;Sidonie Lefebvre ,&nbsp;Xavier Briottet ,&nbsp;Zhijun Zhen ,&nbsp;Karine Adeline ,&nbsp;Matthieu Marionneau ,&nbsp;Valérie Le Dantec","doi":"10.1016/j.rse.2025.114784","DOIUrl":"10.1016/j.rse.2025.114784","url":null,"abstract":"<div><div>This study integrates a dynamic plant growth model with a three-dimensional (3D) radiative transfer model (RTM) for maize traits retrieval using high spatial–spectral resolution airborne data. The research combines the Discrete Anisotropic Radiative Transfer (DART) model with the Dynamic L-System-based Architectural maize (DLAmaize) growth model to simulate field reflectance. Comparison with the 1D RTM SAIL revealed limitations in representing row structure effects, field slope, and complex light–canopy interactions. Novel Global Sensitivity Analyses (GSA) were carried out using dependence-based methods to overcome limitations of traditional variance-based approaches, enabling better characterization of hyperspectral sensitivity to changes in leaf biochemistry, canopy architecture, and soil moisture. GSA provided complementary results to assess estimation uncertainties of the proposed traits retrieval method across growth stages. A hybrid inversion framework combining DART simulations with an active learning strategy using Kernel Ridge Regression was implemented for traits estimation. The approach was validated using ground data and HyPlant-DUAL airborne hyperspectral images from two field campaigns in 2018 and achieved high retrieval accuracy of key maize traits: leaf area index (LAI, R²=0.91, RMSE=0.42 m²/m²), leaf chlorophyll content (LCC, R²=0.61, RMSE=3.89 <span><math><mi>μ</mi></math></span>g/cm²), leaf nitrogen content (LNC, R²=0.86, RMSE=1.13 × 10⁻² mg/cm²), leaf dry matter content (LMA, R²=0.84, RMSE=0.15 mg/cm²), and leaf water content (LWC, R²=0.78, RMSE=0.88 mg/cm²). The validated models were used to generate two-date 10 m resolution maps, showing good spatial consistency and traits dynamics. The findings demonstrate that integrating 3D RTMs with dynamic growth models is suited for maize trait mapping from hyperspectral data in varying growing conditions.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"327 ","pages":"Article 114784"},"PeriodicalIF":11.1,"publicationDate":"2025-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144130112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimating crop biophysical parameters from satellite-based SAR and optical observations using self-supervised learning with geospatial foundation models","authors":"Mahya G.Z. Hashemi ,&nbsp;Hamed Alemohammad ,&nbsp;Ehsan Jalilvand ,&nbsp;Pang-Ning Tan ,&nbsp;Jasmeet Judge ,&nbsp;Michael Cosh ,&nbsp;Narendra N. Das","doi":"10.1016/j.rse.2025.114825","DOIUrl":"10.1016/j.rse.2025.114825","url":null,"abstract":"<div><div>Accurate knowledge of vegetation water content (VWC) and crop height is crucial for agricultural management, environmental monitoring, and for satellite-based retrieval algorithms for geophysical variables. Traditional methods to estimate VWC, primarily rely on optical indices, which has limitations of biomass saturation, and sensitivity to atmospheric conditions. This study introduces a novel application of geospatial foundation models (GFMs), leveraging extensive, unlabeled datasets through self-supervised learning to enhance the skill of VWC and crop height estimation. We developed a comprehensive model integrating Sentinel-1 A C-band SAR and Sentinel-2 A/B indices with weather parameters to estimate soybean and corn VWC and crop height.</div><div>Our research study area spans a variety of climatic zones and management practices, from the humid continental climate of Iowa and Michigan to the subtropical environment of Florida, encompassing both irrigated and non-irrigated fields as well as diverse tillage practices. We compared the performance of Single-Task Learning GFM (STL-GFM), Multi-Task Learning GFM (MTL-GFM), and machine learning techniques including Random Forest (RF), and XGBoost (XGB) to evaluate their effectiveness in estimating VWC and crop height.</div><div>Results demonstrated that STL-GFM outperforms other methods in accuracy and generalizability. For VWC estimation, STL-GFM achieved R² values of 0.90 and 0.89 for soybean and corn, respectively. For crop height, R² values reached 0.95 for soybean and 0.98 for corn. The integration of SAR, optical, and climate data provided more reliable estimations than using individual data sources.</div><div>Feature importance analysis identified NDVI, NDWI, VH backscatter, and precipitation as key drivers for accurate VWC and height estimations. The red-edge band emerged as significant for VWC estimation but showed limited importance for height prediction. Notably, surface roughness demonstrated a noticeable impact on corn VWC and height estimations, while soil moisture exhibited less influence than initially anticipated. Notably, without directly incorporating soil moisture and surface roughness data, but by including diverse field conditions in training and validation, the STL-GFM model demonstrated strong generalization capabilities. This study highlights the potential of GFMs in advancing crop monitoring techniques, offering more reliable data for precision agriculture, and supporting sustainable farming practices across diverse agricultural landscapes.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"327 ","pages":"Article 114825"},"PeriodicalIF":11.1,"publicationDate":"2025-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144130113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel correlation-hypothesis based single channel method for land surface temperature retrieval with reduced atmospheric dependency","authors":"Xiu-Juan Li ,&nbsp;Hua Wu ,&nbsp;Zhao-Liang Li ,&nbsp;José Antonio Sobrino ,&nbsp;Xing-Xing Zhang ,&nbsp;Yuan-Liang Cheng","doi":"10.1016/j.rse.2025.114817","DOIUrl":"10.1016/j.rse.2025.114817","url":null,"abstract":"<div><div>As one of the critical parameters in the land-atmosphere exchange processes, land surface temperature (LST) plays an essential role in various domains, such as climate change, urban heat island effect, disaster monitoring, and evaporation retrieval. Thermal infrared (TIR) remote sensing is one of the main approaches to obtaining LST on a large scale. For the sensors with only one TIR channel, the single-channel (SC) methods are commonly and effectively used to retrieve LST, as they are most suitable under such limitations. However, atmospheric correction is essential for the SC methods, which involves significant uncertainty and complexity. To reduce the atmospheric dependency of SC methods, this study proposes a Correlation-Hypothesis based Single-Channel (CH-SC) method to retrieve LST. In this method, the LST can be retrieved using the top-of-atmosphere (TOA) brightness temperature from a single TIR channel and the LSEs from two virtual adjacent channels, while atmospheric water vapor content (WVC) is used solely to assess atmospheric conditions. Consequently, the CH-SC method exhibits the least sensitive in atmospheric parameter errors compared to existing SC methods. This inherent robustness results in superior stability of retrieval accuracy, enhancing its practicality for real applications. Subsequently, the CH-SC method was applied to Landsat 7 data, alongside the mono-window (MW) method and generalized single-channel (GSC) method. The retrieved LSTs were compared with in-situ measurements for validation. As a result, the CH-SC method exhibited strong performance compared to in-situ measurements, with an RMSE of 2.83 K in SURFRAD sites and 4.02 K in BSRN sites, which was comparable to Landsat 7 LST product (3.15 K at SURFRAD and 4.26 K at BSRN) and outperforming other SC methods. Generally, compared to the existing methods, the proposed method exhibits minimal dependence on atmospheric information while ensuring superior accuracy and stability, even under high water vapor conditions. That holds significant application value, especially for the sensors with limited TIR channels to enable real-time on-orbit computation of LST.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"327 ","pages":"Article 114817"},"PeriodicalIF":11.1,"publicationDate":"2025-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144125264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Canopy BRDF differentiation on LAI based on Monte Carlo Ray Tracing","authors":"Abdelaziz Kallel ,&nbsp;Yingjie Wang ,&nbsp;Johan Hedman ,&nbsp;Jean Philippe Gastellu-Etchegorry","doi":"10.1016/j.rse.2025.114785","DOIUrl":"10.1016/j.rse.2025.114785","url":null,"abstract":"<div><div>Radiative transfer models (RTM) enable the simulation of remote sensing observations and can therefore be useful for sensitivity analyses and model inversions, for example to determine the biophysical properties of vegetation. For this purpose, the calculation of observation derivatives is crucial. In this study, we propose to differentiate vegetation RTM based on Monte Carlo Ray tracing, PolVRT, as a function of the leaf area index (LAI). The variation of LAI is done considering the average leaf area (<span><math><mi>u</mi></math></span>). The difference between simulations corresponding to two areas close to each other (<span><math><mi>u</mi></math></span> and <span><math><msup><mrow><mi>u</mi></mrow><mrow><mo>′</mo></mrow></msup></math></span>) is used to calculate the derivative as the limit when <span><math><msup><mrow><mi>u</mi></mrow><mrow><mo>′</mo></mrow></msup></math></span> approaches <span><math><mi>u</mi></math></span>. We propose in this work to adjust traced paths for <span><math><mi>u</mi></math></span> to simulate (<span><math><msup><mrow><mi>u</mi></mrow><mrow><mo>′</mo></mrow></msup></math></span>), but such paths are weighted to correct their bias. This weighting is based on the technique of Importance Sampling. It increases the weighting of likely paths and conversely decreases it of unlikely ones. We have made a correction for the hot spot effect, since there is a strong dependency between the incident and backscattered paths. Our approach performances are verified using some discrete ROMC scene parameters and compared with the standard finite difference technique.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"327 ","pages":"Article 114785"},"PeriodicalIF":11.1,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144115339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using sub-diurnal surface-air temperature difference anomaly derived from Himawari-8 geostationary satellite and meteorological grids for early detection of vegetation drought stress: Application to Australia's 2017–2019 Tinderbox Drought","authors":"Dejun Cai ,&nbsp;Tim R. McVicar ,&nbsp;Thomas G. Van Niel ,&nbsp;Randall J. Donohue ,&nbsp;Yuhei Yamamoto ,&nbsp;Stephen B. Stewart ,&nbsp;Kazuhito Ichii ,&nbsp;Matthew P. Stenson","doi":"10.1016/j.rse.2025.114768","DOIUrl":"10.1016/j.rse.2025.114768","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Satellite land surface temperature (&lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mi&gt;T&lt;/mi&gt;&lt;mi&gt;s&lt;/mi&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt;) provides valuable information on vegetation drought stress via its physical linkage to plant stomatal activity and transpiration. New-generation geostationary satellites offer opportunities to monitor sub-diurnal variations in &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mi&gt;T&lt;/mi&gt;&lt;mi&gt;s&lt;/mi&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; and thus track plant physiological stress response occurring at sub-daily timescales. Nevertheless, the potential of satellite &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mi&gt;T&lt;/mi&gt;&lt;mi&gt;s&lt;/mi&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; and its derived metrics for early detection of vegetation drought stress before visible canopy changes occur has not been widely assessed. Here, we developed a parsimonious Surface-Air Temperature Difference Anomaly (SATDA) method for tracking vegetation drought stress using the cumulative sub-diurnal difference from late-morning to early-afternoon between &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mi&gt;T&lt;/mi&gt;&lt;mi&gt;s&lt;/mi&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; from the Himawari-8 geostationary satellite and hourly air temperature (&lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mi&gt;T&lt;/mi&gt;&lt;mi&gt;a&lt;/mi&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt;) from meteorological grids. SATDA utilised &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mi&gt;T&lt;/mi&gt;&lt;mi&gt;s&lt;/mi&gt;&lt;/msub&gt;&lt;mo&gt;−&lt;/mo&gt;&lt;msub&gt;&lt;mi&gt;T&lt;/mi&gt;&lt;mi&gt;a&lt;/mi&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; as the physical driving gradient for sensible heat flux (&lt;em&gt;H&lt;/em&gt;) to capture anomalous sensible heating due to reduced plant transpiration. We used SATDA to monitor the spatio-temporal patterns of the 2017–2019 Tinderbox Drought in southeast Australia. We benchmarked the skill of SATDA in forecasting visible drought-induced vegetation greenness decline against both conventional water availability-based indices (i.e., precipitation and soil moisture anomalies) and existing satellite &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mi&gt;T&lt;/mi&gt;&lt;mi&gt;s&lt;/mi&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; indices (i.e., Temperature Condition Index and Temperature Rise Index) across diverse climates and land covers. SATDA effectively captured a rapidly intensifying flash drought event at multi-week timescales (Jul to Sep 2019) embedded within the multi-year Tinderbox Drought, which contributed to detrimental impacts on agricultural production and increased wildfire risk. SATDA showed the best vegetation greenness forecast skill in the transitional semi-arid and sub-humid climates, with forecast correlation &gt;0.5 at 32-day lead time. The advantage over water availability-based indices was more evident in woody-dominated ecosystems than herbaceous-dominated ecosystems, likely due to the importance of physiological regulations by trees during droughts such as deeper roots and stronger stomatal control. SATDA, based on &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mi&gt;T&lt;/mi&gt;&lt;mi&gt;s&lt;/mi&gt;&lt;/msub&gt;&lt;mo&gt;−&lt;/mo&gt;&lt;msub&gt;&lt;mi&gt;T&lt;/mi&gt;&lt;mi&gt;a&lt;/mi&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt;, showed overall better vegetation greenness forecasts than two &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mi&gt;T&lt;/mi&gt;&lt;mi&gt;s&lt;/mi&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt;-only indices, especially in woody vegetation. Finally, SATDA showed consistently greater advantage over water availabil","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"327 ","pages":"Article 114768"},"PeriodicalIF":11.1,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144105807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A scalable, annual aboveground biomass product for monitoring carbon impacts of ecosystem restoration projects","authors":"Clement Atzberger ,&nbsp;Markus Immitzer ,&nbsp;Kyle S. Hemes ,&nbsp;Mathias Kästenbauer ,&nbsp;Josué López ,&nbsp;Talita Terra ,&nbsp;Clara Rajadel-Lambistos ,&nbsp;Saulo Franco de Souza ,&nbsp;Kleber Trabaquini ,&nbsp;Nathan Wolff","doi":"10.1016/j.rse.2025.114774","DOIUrl":"10.1016/j.rse.2025.114774","url":null,"abstract":"<div><div>Restoring natural ecosystems has the potential to remove billions of tons of CO₂ annually through the end of the century, but rigorously measuring the climate impacts of restoration activities on the ground remains elusive. Ecosystem restoration interventions across hundreds or thousands of smallholder properties require robust above-ground biomass (AGB) products at high spatial (deca-metric: 10–30 m) resolution for annual monitoring, reporting, and verification (MRV). In addition to ongoing monitoring, historical AGB time series across the region are also necessary. Historical maps are for example needed for eligibility checks and the selection of appropriate counterfactuals, i.e., to establish a dynamic performance benchmark. We present a novel AGB product based on a recently developed foundation model leveraging progress in self-supervised learning (SSL) techniques from multi-spectral Earth Observation (EO) time series. The foundation model is non-contrastive and condenses all available spectral observations acquired within a year into a few, orthogonal and highly informative representations at 10 m (for Sentinel-2) and 30 m (for Landsat 7/8). Combined with spatially sparse Global Ecosystem Dynamics Investigation (GEDI) full-waveform measurements at two relative heights (RH₉₅ and RH₁₀), but otherwise without any further fine-tuning, we are able to estimate forest biomass with an RMSE of &lt;25 Mg/ha, when validated against 38 in-situ AGB measurement sites across a range of agroforestry (cacao and oil palm) and restoration age classes. Compared to five openly available datasets – most of them not available at annual time steps – our approach reduces the RMSE by 15–55%. We demonstrate the scalability of our approach, by producing annual AGB maps covering the entire state of Para, Brazil, for the years 2013 to 2024. The approach is computationally efficient, fully self-supervised without relying on contrastive samples, and can therefore be scaled to global coverage, even under conditions of high cloudiness.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"327 ","pages":"Article 114774"},"PeriodicalIF":11.1,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144105806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mapping the first dataset of global urban land uses with Sentinel-2 imagery and POI prompt","authors":"Shuping Xiong ,&nbsp;Xiuyuan Zhang ,&nbsp;Haoyu Wang ,&nbsp;Yichen Lei ,&nbsp;Ge Tan ,&nbsp;Shihong Du","doi":"10.1016/j.rse.2025.114824","DOIUrl":"10.1016/j.rse.2025.114824","url":null,"abstract":"<div><div>An up-to-date, detailed global urban land use map is essential for disclosing urban structures and dynamics as well as their differences across different regions. However, generating an accurate global urban land use map remains challenging due to the complex diversity of land use types and the uneven availability of data. Existing methods, which either rely solely on remote sensing imagery or treat supplementary data like point-of-interest (POI) as mandatory inputs, fail to account for regional data disparities and the complex relationships between different data modalities. In this study, we propose an urban land use mapping framework optimized with POI prompts. First, we acquire global urban Sentinel-2 imagery, POI data, and labeled LU samples from Google Earth Engine (GEE) and OpenStreetMap (OSM), and then propose a POI-Prompt Urban Land-use Mapping Network (PPUL-Net), which utilizes POI prompts to enhance classification accuracy and produce reliable predictions in those regions lacking POI data. Consequently, a global land use dataset (GULU) with a 10 m resolution in 2020 has been produced at the first time. Experimental results show that GULU has an overall accuracy of 84.59 %, demonstrating robust performance across continents. Incorporating POI data improved the accuracies for some challenging categories, such as commercial and institutional lands, by 7.32 % and 17.66 %, respectively. Additionally, using POI as prompts instead of direct pixel-level fusion with imagery increased accuracy by 2.92 %. Finally, analysis of the GULU dataset reveals that Europe and North America exhibit high land use diversity, implying mature urban structures, whereas sub-Saharan Africa and South America are predominantly characterized by residential and undeveloped areas. This dataset provides invaluable insights for urban planning, development monitoring, and sustainable development assessments.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"327 ","pages":"Article 114824"},"PeriodicalIF":11.1,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144099323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}