Remote Sensing of Environment最新文献

{"title":"Multi-Modal Vision Transformer for high-resolution soil texture prediction of German agricultural soils using remote sensing imagery","authors":"Lucas Wittstruck,&nbsp;Björn Waske,&nbsp;Thomas Jarmer","doi":"10.1016/j.rse.2025.114985","DOIUrl":"10.1016/j.rse.2025.114985","url":null,"abstract":"<div><div>The quantification and mapping of important soil properties, such as soil texture, are vital for effective crop management and the assessment of overall soil health in agricultural systems. In this study, we propose a multi-modal Visual Transformer (MMVT) architecture to predict and map the soil particle size distribution of agricultural topsoils in Germany at a high spatial resolution of 10 meters. Our modeling utilized multi-source bare soil satellite image composites with terrain and soil-related covariates. To optimize the model’s ability to capture spatial soil context, various image sizes were evaluated. The study findings highlighted the effectiveness of our MMVT model, demonstrating improved estimation accuracies compared to a two-dimensional Convolutional Neural Network (2D CNN) and a Random Forest (RF) model. Specifically, the proposed transformer network achieved the highest averaged validated accuracy in predicting the soil texture when incorporating a contextual image surrounding of 320 × 320 m around the soil sampling positions (Sand: <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> = 0.74, RMSE = 14.78%, and RPIQ = 3.52, Silt: <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> = 0.73, RMSE = 12.36%, and RPIQ = 3.50, Clay: <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> = 0.52, RMSE = 6.30%, and RPIQ = 1.95). This integrated approach underscores the potential of advanced deep learning techniques and multi-modal learning in providing comprehensive insights into soil characteristics with high resolution and at a large scale.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"331 ","pages":"Article 114985"},"PeriodicalIF":11.4,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144987795","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":"Remote sensing for crop mapping: A perspective on current and future crop-specific land cover data products","authors":"Chen Zhang ,&nbsp;Hannah Kerner ,&nbsp;Sherrie Wang ,&nbsp;Pengyu Hao ,&nbsp;Zhe Li ,&nbsp;Kevin A. Hunt ,&nbsp;Jonathon Abernethy ,&nbsp;Haoteng Zhao ,&nbsp;Feng Gao ,&nbsp;Liping Di ,&nbsp;Claire Guo ,&nbsp;Ziao Liu ,&nbsp;Zhengwei Yang ,&nbsp;Rick Mueller ,&nbsp;Claire Boryan ,&nbsp;Qi Chen ,&nbsp;Peter C. Beeson ,&nbsp;Hankui K. Zhang ,&nbsp;Yu Shen","doi":"10.1016/j.rse.2025.114995","DOIUrl":"10.1016/j.rse.2025.114995","url":null,"abstract":"<div><div>Crop mapping is an indispensable application in agricultural and environmental remote sensing. Over the last few decades, the exponential growth of open Earth Observation (EO) data has significantly enhanced crop mapping and enabled the production of detailed crop-specific land cover data at national and regional scales. These data have served multiple purposes across a wide range of applications and research initiatives. However, there is currently no comprehensive summary of the crop mapping data products, nor is there a detailed discussion of their uses in remote sensing studies. This paper provides the first in-depth review of remote sensing for crop mapping from the perspective of crop-specific land cover data by evaluating over 60 open-access operational products, archival crop type map datasets, single-crop extent map datasets, cropping pattern datasets, and crop mapping platforms and systems. Using the Cropland Data Layer (CDL) – one of the most widely used products with over 25 years of continuous monitoring of U.S. croplands – as a case study, we also conduct a systematic literature review on the application of crop type maps in remote sensing science. Our analysis synthesizes 129 research articles through three core research questions: (1) What EO data are used with CDL; (2) What scientific problems and technologies are explored using CDL; and (3) What role does CDL play in remote sensing applications. Furthermore, we delve into the implications of our vision for new data products and propose emerging research topics, ranging from extending the spatiotemporal coverage of current data products to improving global mapping reliability and developing operational in-season crop mapping systems. This review paper not only serves as a reference for stakeholders seeking to utilize crop-specific land cover data in their work, but also outlines the directions for future geospatial data product development.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"330 ","pages":"Article 114995"},"PeriodicalIF":11.4,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144930626","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":"Wide-area coastal deformation extraction using multi-path/frame InSAR: A case study of the Bohai Rim","authors":"Zhiqiang Gong ,&nbsp;Mingsheng Liao ,&nbsp;Jie Dong ,&nbsp;Qianye Lan ,&nbsp;Ru Wang ,&nbsp;Shangjing Lai","doi":"10.1016/j.rse.2025.114988","DOIUrl":"10.1016/j.rse.2025.114988","url":null,"abstract":"<div><div>Coastal areas worldwide experience serious ground subsidence and rising sea levels, leading to frequent flooding and continued elevation loss. Reliable estimation of coastal subsidence is critical for effective risk assessment and management. Interferometric Synthetic Aperture Radar (InSAR) enables millimeter-scale deformation monitoring, but generating seamless wide-area results remains challenging due to limited swath width and inconsistencies between adjacent frames. This study proposes an adaptive gridded adjustment model for merging multi-path/frame InSAR results into a wide-area high-precision deformation map. We employ a quadtree-based adaptive grid with dynamically optimized sizes determined by the deformation gradient. The gridded corrections are combined with GNSS-constrained global corrections to eliminate inter-frame biases. Applied to the coastal area of the Bohai Rim, this method reduces the Sentinel-1 discrepancies by 38 %, and outperforms the fixed-grid method in achieving an optimal balance between precision and efficiency. This method is useful for wide-area high-precision deformation monitoring to support risk assessments of relative sea level rise in the context of global climate change.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"330 ","pages":"Article 114988"},"PeriodicalIF":11.4,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144925589","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":"Remote sensing of vegetation phenology in the northern hemisphere from multi-channel passive microwave measurements of Chinese FengYun-3D satellite","authors":"Yipu Wang ,&nbsp;Qingyang Liu ,&nbsp;Rui Li ,&nbsp;Jiheng Hu ,&nbsp;Peng Zhang ,&nbsp;Binbin Song","doi":"10.1016/j.rse.2025.114997","DOIUrl":"10.1016/j.rse.2025.114997","url":null,"abstract":"<div><div>Vegetation phenology regulates the inter-annual variations in carbon and water dynamics in terrestrial ecosystems, and serves as a key indicator of vegetation-climate interaction. Chinese FengYun-3D satellite multi-channel passive microwave measurements are responsive to seasonal changes of vegetation structure and internal water status, and have a daily global coverage under both clear and cloudy skies, offering valuable and complementary phenological information to optical-infrared measurements. However, no studies have yet explored their potential for global phenology extraction. Here we evaluated the capability of Normalized Emissivity Difference Vegetation index (NEDVI), which was derived from FengYun-3D X- and Ka- band microwaves, to extract forest and grass phenological dates in the Northern Hemisphere, including the start, end and length of the growing season (SOS, EOS and LOS). By testing three phenology models and two extraction methods at 31 flux sites from 2020 to 2022, NEDVI-derived SOS, EOS and LOS were found to be significantly correlated with those derived from in-situ gross primary production (GPP). No single model or extraction method can produce an absolutely superior accuracy in extracting phenological dates, while use of multi-model mean may greatly reduce the uncertainties. NEDVI-based relative threshold extraction method showed an overall bias of less than 2 days in the phenological dates when considering the multi-model average, outperforming the maximum rate of curvature method. Performances of the time series of NEDVI are overall better for the extraction of SOS than EOS, and are also comparable to those of MODIS and VIIRS global phenology products. Spatial patterns and latitudinal variations in NEDVI-derived phenology align with the two optical phenology products in the Northern Hemisphere. In evergreen forest types, NEDVI-derived SOS, EOS and LOS tend to present earlier, later and longer than those from MODIS and VIIRS, respectively. In addition, the product of NEDVI-derived growing season length and maximum carbon uptake capacity can better explain annual GPP variation across forest and grass sites (R² =0.43) compared to that of MODIS (R² =0.19) and VIIRS (R² =0.37). These findings demonstrate that FengYun-3D microwave NEDVI is promising for global retrievals of phenological dates. We also highlight that using the multi-model mean, instead of relying on a single model, can greatly enhance the robustness of microwave-based phenology extraction with lower uncertainty.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"330 ","pages":"Article 114997"},"PeriodicalIF":11.4,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144925594","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":"Transformation of aquatic vegetation in Chinese lakes from 1988 to 2023","authors":"Jinying Liu ,&nbsp;Xuejiao Hou ,&nbsp;Huabing Huang ,&nbsp;Peimin Chen ,&nbsp;Lian Feng ,&nbsp;Ronghua Ma","doi":"10.1016/j.rse.2025.114999","DOIUrl":"10.1016/j.rse.2025.114999","url":null,"abstract":"<div><div>Aquatic vegetation (AV) is crucial for maintaining lake ecosystem's stability. Transformation of certain types of AV (e.g., submerged aquatic vegetation (SAV)) into other lake features (e.g., algal bloom) may cause a shift in lake's stable state. However, such transformations in Chinese lakes remain largely unknown. Using 0.23 million Landsat images from 1988 to 2023, a comprehensive investigation was conducted to reveal the long-term conversions between SAV, floating and emergent AV (FEAV), and non-AV (e.g., water or algal bloom) across 4375 Chinese lakes (with area &gt; 1 km²). Results show that from 1988–1993 to 2018–2023, the total lake AV area experienced a reduction of 0.7 × 10³ km²_. Despite a countrywide net increase in FEAV (+0.9 × 10³ km²), it could not offset the sharp decline in SAV (−1.6 × 10³ km²). The SAV decline was mainly observed in Eastern Plain lake region. Nationwide, 3.6 × 10³ km² of SAV have transitioned into non-AV, with 78.3% of this area transformation occurring in Eastern Plain. In contrast, many non-AV areas on the Tibetan Plateau saw an emergence of SAV, expanding at +17.9 km² per year. However, mutual conversions between SAV and FEAV were minimal. National FEAV gains were primarily from non-AV conversions (+1.3 × 10³ km²), occurring mainly in Eastern Plain, while SAV-to-FEAV transitions accounted for only 23.8% of total FEAV gains. Driving forces analysis shows that lake eutrophication dominated SAV reduction, whereas FEAV variations were primarily influenced by eutrophication, temperature, and lake water area changes. These findings could provide important insights for lake restoration management in China.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"330 ","pages":"Article 114999"},"PeriodicalIF":11.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144922124","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":"Mitigating the black-soil problem in the reflectance-to-fluorescence (R2F) relationship: A soil-adjusted reflectance-based approach for downscaling SIF","authors":"Peiqi Yang ,&nbsp;Zhigang Liu ,&nbsp;Dalei Han ,&nbsp;Runfei Zhang ,&nbsp;Bastian Siegmann ,&nbsp;Jing Liu ,&nbsp;Huarong Zhao ,&nbsp;Uwe Rascher ,&nbsp;Jing M. Chen ,&nbsp;Christiaan van der Tol","doi":"10.1016/j.rse.2025.114998","DOIUrl":"10.1016/j.rse.2025.114998","url":null,"abstract":"<div><div>Solar-induced chlorophyll fluorescence (SIF) is an effective probe for photosynthesis, but this remote sensing signal is affected by multiple factors, including radiation intensity, canopy structure, sun-observer geometry, and leaf physiological status. The complex interplay among these factors causes substantial discrepancies among top-of-canopy (TOC) SIF, leaf-level average SIF and actual photosynthetic activity. Downscaling TOC SIF to the leaf-level and decoupling structural and physiological information remain major challenges in the use of SIF signals for remote sensing of photosynthesis. To address these challenges, the R2F (reflectance-to-fluorescence) theory was developed, grounded in the similarity in radiative transfer processes governing SIF and reflectance. This theory establishes a physical relationship between near-infrared reflectance (<span><math><msub><mi>R</mi><mi>nir</mi></msub></math></span>) and the far-red SIF scattering coefficient (<span><math><msub><mi>σ</mi><mi>F</mi></msub></math></span>). On this basis, SIF signals can be scaled from the canopy to the leaf level by normalizing <span><math><msub><mi>σ</mi><mi>F</mi></msub></math></span>, estimated from reflectance as <span><math><mspace></mspace><msub><mi>σ</mi><mi>F</mi></msub><mo>=</mo><msub><mi>R</mi><mi>nir</mi></msub><mo>/</mo><msub><mi>i</mi><mn>0</mn></msub></math></span>, where <span><math><msub><mi>i</mi><mn>0</mn></msub></math></span> denotes canopy interceptance. However, the original R2F formulation assumes a non-reflective soil. This simplification breaks down in sparse canopies, where soil contributions are non-negligible—an issue referred to as the “black-soil problem”. Soil enhances both <span><math><msub><mi>R</mi><mi>nir</mi></msub></math></span> and <span><math><msub><mi>σ</mi><mi>F</mi></msub></math></span>, distorting their intrinsic relationship. In this study, we show that soil effects manifest through two main mechanisms: (1) direct soil reflection, which significantly increases <span><math><msub><mi>R</mi><mi>nir</mi></msub></math></span> but has minimal impact on <span><math><msub><mi>σ</mi><mi>F</mi></msub></math></span>, and (2) soil–vegetation multiple scattering, which affects both <span><math><msub><mi>R</mi><mi>nir</mi></msub></math></span> and <span><math><msub><mi>σ</mi><mi>F</mi></msub></math></span> but tends to have compensatory effects. Consequently, the dominant source of bias in the original R2F relationship is direct soil reflection that contributes to <span><math><msub><mi>R</mi><mi>nir</mi></msub></math></span>—a mechanism that had not been explicitly isolated in previous studies. This finding allows us to narrow down the “black-soil problem” in the R2F framework to the specific impact of soil single scattering on <span><math><msub><mi>R</mi><mi>nir</mi></msub></math></span>. To mitigate this bias, we propose a soil-adjusted R2F (saR2F) method, which estimates the direct soil contribution of <span><math><msub><mi>R</m","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"330 ","pages":"Article 114998"},"PeriodicalIF":11.4,"publicationDate":"2025-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144920255","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":"Hyperspectral surface reflectance improves GPP estimation in terrestrial biosphere modeling using model-data fusion","authors":"Haoran Liu ,&nbsp;Fa Li ,&nbsp;Hamid Dashti ,&nbsp;Min Chen","doi":"10.1016/j.rse.2025.114989","DOIUrl":"10.1016/j.rse.2025.114989","url":null,"abstract":"<div><div>Gross Primary Productivity (GPP) estimates from terrestrial biosphere models (TBMs) are often uncertain due to limited constraints on vegetation biochemical and biophysical properties. Remote sensing offers promising opportunities to reduce these uncertainties, yet its full potential remains understudied. Here, we conducted model-data fusion experiments, including Observing System Simulation Experiments (OSSEs), and Observing System Experiments (OSEs) at the Harvard Forest site, using the Terrestrial Ecosystem Carbon cycle simulator (TECs) with an embedded spectral invariant theory-based radiative transfer model. In OSSEs, we assimilated synthetic hyperspectral reflectance, multispectral reflectance, and Leaf Area Index (LAI) into TECs to evaluate their effect under the ideal conditions. In OSEs, we assimilated PRecursore IperSpettrale della Missione Applicativa (PRISMA) hyperspectral reflectance (620–1000 nm), MODerate resolution Imaging Spectroradiometer (MODIS) multispectral reflectance (broadband red and near-infrared), and MODIS-derived LAI to optimize model parameters, including several key vegetation traits such as leaf chlorophyll content (Cab), maximum carboxylation rate at 25 °C (V_cmax25), and LAI. Results show that hyperspectral reflectance consistently outperforms multispectral reflectance and LAI in improving GPP estimates and reducing uncertainties, with RMSE decreasing from 2.68 to 1.18 μmol CO₂ m⁻² s⁻¹ in OSSEs, and from 6.74 to 5.42 μmol CO₂ m⁻² s⁻¹ in OSEs. This is because hyperspectral information better constrains seasonal variations in canopy structure and Cab. Meanwhile, both hyperspectral and multispectral reflectance outperform LAI, with information from both canopy structural parameters and leaf biochemical properties, thus offering a joint constraint on GPP simulations. Our findings highlight that remotely sensed reflectance data, particularly hyperspectral reflectance, have great potential to improve photosynthesis modeling and reduce uncertainties in GPP estimates within TBMs.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"330 ","pages":"Article 114989"},"PeriodicalIF":11.4,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144908241","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":"Landsat and dual random forest modelling reveal sediment fining in the Yellow River shaped by ecological restoration on China's loess plateau","authors":"Zhiqiang Qiu ,&nbsp;Dong Liu ,&nbsp;Nuoxiao Yan ,&nbsp;Yao Yan ,&nbsp;Chen Yang ,&nbsp;Chenxue Zhang ,&nbsp;Hongtao Duan","doi":"10.1016/j.rse.2025.114994","DOIUrl":"10.1016/j.rse.2025.114994","url":null,"abstract":"<div><div>Monitoring coarse-grained sediment is essential for managing riverbed stability, flood capacity, and ecological resilience in the Yellow River, where high sediment loads originate from the erosion-prone Loess Plateau. Although large-scale ecological restoration has been implemented since the 1980s, its long-term impact on sediment grain-size dynamics remains unclear due to limited field observations. This study developed a dual-layer random forest model that synergizes Landsat satellites reflectance (1986–2022) with multi-scale watershed attributes (hydrological information, vegetation coverage, erosion susceptibility) to remotely quantify particle size distribution (PSD) of suspended sediment. The model achieved high precision (root mean square error: 2.94–4.82 %; mean absolute percentage difference: 13.44–19.87 %), enabling the first basin-wide PSD reconstruction. Key findings reveal: (1) Medium-sized particles (0.01–0.05 mm) dominated the mainstream (63.96 %), while coarse (&gt;0.05 mm, 67.80 %) and fine particles (&lt;0.01 mm, 25.70 %) were concentrated in the Fen and Wei Rivers, respectively; (2) Median grain size decreased by 7.25 % during the 1980s–2020s, reflecting the cumulative effects of ecological restoration, though localized coarsening (1.27, 2.39 and 2.61 %) occurred in the Huangshui, Wei, and Jing Rivers; and (3) Vegetation expansion (8.50–51.23 %) and urbanization (impervious surfaces (12.90–17.04 %)) drove particle fining, while increased wind/water erosion increased the proportion of coarse particle. This study fills a critical gap in monitoring suspended particle size dynamics and provides a scalable framework for evaluating ecological restoration outcomes and informing suspended sediment management in large, sediment-rich watersheds.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"330 ","pages":"Article 114994"},"PeriodicalIF":11.4,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144908240","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":"Microwave water vapor atmospheric motion vectors retrieval from polar-orbiting satellites","authors":"Zongru Yang ,&nbsp;Xuezhi Bai ,&nbsp;Gang Ma ,&nbsp;Peng Zhang ,&nbsp;Yangtian Yan ,&nbsp;Chunhong Zhou","doi":"10.1016/j.rse.2025.114983","DOIUrl":"10.1016/j.rse.2025.114983","url":null,"abstract":"<div><div>Atmospheric motion vectors (AMVs) constitute one of the most critical data sources assimilated in numerical weather prediction (NWP), yet current operational wind products fall short to meet forecast requirements. This study addresses a fundamental observational gap in satellite wind retrievals. Traditional polar-orbiting satellite retrievals are limited to high latitudes, and geostationary AMV products are restricted to mid-low latitudes. In the resulting gap regions, only morning-orbit Metop infrared AMVs currently provide limited coverage. This study introduces an optical flow-based atmospheric motion vector retrieval method employing spatiotemporal matching of 183.31 GHz microwave water vapor channel brightness temperatures from NOAA-20/21 Advanced Technology Microwave Sounders (ATMS), enabling highly vertically resolved wind retrievals with clear-sky pixels. Using a fixed 5° × 5° feature tracking regions (5° FTR), the wind speed bias ranges from 0.16 to 0.64 m·s⁻¹, the root mean square error (RMSE) ranges from 3.45 to 3.81 m·s⁻¹, and the wind direction bias was consistently constrained below 27.4°. The overall accuracy achieves the error levels of existing products.</div><div>For extremely wind speed conditions, a hybrid-scale FTR optimization model, 3° FTR for slow wind speed region and 10° FTR for those high wind speed region, is also proposed. It can expand the detectable wind speed range from 45 m·s⁻¹ to 70 m·s⁻¹ with a sample size increase of over 10 % per channel. The RMSE for 3° FTR reduces by 0.5 m·s⁻¹, while the 10° FTR achieves a 1.5° reduction in both angular deviations and their standard deviation (STD) at 500 and 450 hPa. For all the hybrid regions in all channels, the RMSE remains within 3.47–3.79 m·s⁻¹, the correlation coefficient is enhanced by about 10 % and the wind direction bias is almost the same as that of the fixed FTR. This hybrid-scale tracking strategy can effectively balance spatial resolution and statistical reliability, and thus provides a new technical paradigm for polar-orbiting microwave AMV retrieval. The resulting afternoon-orbit microwave AMVs deliver a novel wind data source for NWP assimilation systems.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"330 ","pages":"Article 114983"},"PeriodicalIF":11.4,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144908239","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":"Predicting below-average NDVI anomalies for agricultural drought impact forecasting","authors":"Koen De Vos ,&nbsp;Sarah Gebruers ,&nbsp;Jeroen Degerickx ,&nbsp;Marian-Daniel Iordache ,&nbsp;Jessica Keune ,&nbsp;Francesca Di Giuseppe ,&nbsp;Francisco Vilela Pereira ,&nbsp;Hendrik Wouters ,&nbsp;Else Swinnen ,&nbsp;Koen Van Rossum ,&nbsp;Laurent Tits","doi":"10.1016/j.rse.2025.114980","DOIUrl":"10.1016/j.rse.2025.114980","url":null,"abstract":"<div><div>Agricultural droughts, driven by deficits in root-zone soil moisture, pose challenges to food security and economic stability in Africa, which is simultaneously vulnerable to frequent droughts and strongly relies on rainfed agriculture. Current Earth observation (EO)-based monitoring systems rely on a near-real-time assessment of vegetation conditions — often through monitoring the Normalized Difference Vegetation Index (NDVI)- and are thereby allowing for reactive rather than proactive drought management. This study presents a machine learning-based forecasting system to predict below-average NDVI anomalies as a proxy for agricultural drought impact, focusing on recently drought-affected and crises-prone countries. By integrating EO data, meteorological forecasts, soil moisture, and static environmental descriptors, we developed a system that forecasts below-average NDVI anomalies up to three months in advance and explicitly considers ensemble uncertainty. The forecast shows an improved accuracy over using near-real-time NDVI anomalies and similar temporal patterns during the 2021–2022 growing seasons, which was used for independent validation. Our forecasted results are comparable to existing NDVI-based monitoring products such as the Agricultural Stress Index System developed by FAO. Despite these advancements, the modelling system struggles during transitions between rainy and dry seasons, often coinciding with the start and end of the growing season. Uncertainties in meteorological forecasts burden effective estimates of important phenological dates such as emergence or harvest up to three months in advance. This study complements existing soil moisture forecasting tools with impact on vegetation and presents a benchmark for the potential of integrating predictive models into anticipatory strategies in existing drought management frameworks.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"330 ","pages":"Article 114980"},"PeriodicalIF":11.4,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144902940","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}