Remote Sensing of Environment最新文献

{"title":"Remote sensing-based high-resolution reservoir drought index for identifying the occurrence and propagation of hydrological droughts in a large river basin","authors":"Liwei Chang ,&nbsp;Lei Cheng ,&nbsp;Lu Zhang ,&nbsp;Dongyang Han ,&nbsp;Jun Zhang ,&nbsp;Pan Liu","doi":"10.1016/j.rse.2025.114859","DOIUrl":"10.1016/j.rse.2025.114859","url":null,"abstract":"<div><div>Reservoir drought is a valuable indicator of regional hydrological drought severity; however, it has received limited attention because of the low quality of reservoir storage data. This study proposes a Remote Sensing-Based High-Resolution Reservoir Drought Index (RS-HRDI) that integrates recent high-resolution satellite observations with historical low-resolution records to construct a long-term reservoir storage dataset. Reservoir droughts are identified by periods of abnormally low reservoir storage using a time-variant threshold. The RS-HRDI was used to detect reservoir droughts in the Yangtze River Basin, one of the most reservoir-regulated and critical river systems globally, from 2018 to 2023, including a record-breaking drought in 2022. The results indicate that the multi-satellite combination significantly improved the reservoir observation frequency from the historical monthly scale to an average of 4.3 d, enabling the detection of rapid reservoir storage reductions within days. The RS-HRDI could effectively identify droughts across various reservoirs and accurately describe their characteristics. Through comprehensive assessments of widespread reservoir networks, the aggregated RS-HRDI effectively characterized basin-scale hydrological droughts, detailing their spatial extent, intensity, and duration. Furthermore, the RS-HRDI highlighted the influence of reservoir operations on the occurrence and propagation of hydrological droughts in a river system. Specifically, upstream reservoir interception advanced downstream droughts by 2–40 d. This study presents a novel reservoir drought assessment method based on remote sensing, highlighting its potential for use in large-scale and timely hydrological drought monitoring and water resource management.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"328 ","pages":"Article 114859"},"PeriodicalIF":11.1,"publicationDate":"2025-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144237511","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":"NeRF-LAI: A hybrid method combining neural radiance field and gap-fraction theory for deriving effective leaf area index of corn and soybean using multi-angle UAV images","authors":"Qi Yang ,&nbsp;Junxiong Zhou ,&nbsp;Liya Zhao ,&nbsp;Zhenong Jin","doi":"10.1016/j.rse.2025.114844","DOIUrl":"10.1016/j.rse.2025.114844","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Methods based on upward canopy gap fractions are widely employed to measure in-situ effective LAI (&lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mi&gt;L&lt;/mi&gt;&lt;mi&gt;e&lt;/mi&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt;) as an alternative to destructive sampling. However, these measurements are limited to point-level and are not practical for scaling up to larger areas. To address the point-to-landscape gap, this study introduces an innovative approach, named NeRF-LAI, for corn and soybean &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mi&gt;L&lt;/mi&gt;&lt;mi&gt;e&lt;/mi&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; estimation that combines gap-fraction theory with the neural radiance field (NeRF) technology, an emerging neural network-based method for implicitly representing 3D scenes using multi-angle 2D images. The trained NeRF-LAI can render downward photorealistic hemispherical depth images from an arbitrary viewpoint in the 3D scene, and then calculate gap fractions to estimate &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mi&gt;L&lt;/mi&gt;&lt;mi&gt;e&lt;/mi&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt;. To investigate the intrinsic difference between upward and downward gaps estimations, initial tests on virtual corn fields demonstrated that the downward &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mi&gt;L&lt;/mi&gt;&lt;mi&gt;e&lt;/mi&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; matches well with the upward &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mi&gt;L&lt;/mi&gt;&lt;mi&gt;e&lt;/mi&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt;, and the viewpoint height is insensitive to &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mi&gt;L&lt;/mi&gt;&lt;mi&gt;e&lt;/mi&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; estimation for a homogeneous field. Furthermore, we conducted intensive real-world experiments at controlled plots and farmer-managed fields to test the effectiveness and transferability of NeRF-LAI in real-world scenarios, where multi-angle UAV oblique images from different phenological stages were collected for corn and soybeans. Results showed the NeRF-LAI is able to render photorealistic synthetic images with an average peak signal-to-noise ratio (PSNR) of 18.94 for the controlled corn plots and 19.10 for the controlled soybean plots. We further explored three methods to estimate &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mi&gt;L&lt;/mi&gt;&lt;mi&gt;e&lt;/mi&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; from calculated gap fractions: the 57.5° method, the five-ring-based method, and the cell-based method. Among these, the cell-based method achieved the best performance, with the &lt;em&gt;r&lt;/em&gt;&lt;sup&gt;2&lt;/sup&gt; ranging from 0.674 to 0.780 and RRMSE ranging from 1.95 % to 5.58 %. The &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mi&gt;L&lt;/mi&gt;&lt;mi&gt;e&lt;/mi&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; estimates are sensitive to viewpoint height in heterogeneous fields due to the difference in the observable foliage volume, but they exhibit less sensitivity to relatively homogeneous fields. Additionally, the cross-site testing for pixel-level LAI mapping showed the NeRF-LAI significantly outperforms the VI-based models, with a small variation of RMSE (0.71 to 0.95 m&lt;sup&gt;2&lt;/sup&gt;/m&lt;sup&gt;2&lt;/sup&gt;) for spatial resolution from 0.5 m to 2.0 m. This study extends the application of gap fraction-based &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mi&gt;L&lt;/mi&gt;&lt;mi&gt;e&lt;/mi&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; estimation from a discrete point scale to a continuous field scale by leveraging implicit 3D neural represen","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"328 ","pages":"Article 114844"},"PeriodicalIF":11.1,"publicationDate":"2025-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144229941","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":"Validating the Carnegie-Ames-Stanford Approach for remote sensing of perennial grass net primary production","authors":"Shaohui Zhang ,&nbsp;Poul Erik Lærke ,&nbsp;Mathias Neumann Andersen ,&nbsp;Junxiang Peng ,&nbsp;Esben Øster Mortensen ,&nbsp;Johannes Wilhelmus Maria Pullens ,&nbsp;Sheng Wang ,&nbsp;Klaus Steenberg Larsen ,&nbsp;Davide Cammarano ,&nbsp;Uffe Jørgensen ,&nbsp;Kiril Manevski","doi":"10.1016/j.rse.2025.114857","DOIUrl":"10.1016/j.rse.2025.114857","url":null,"abstract":"<div><div>Under optimal growth conditions, net primary productivity (<em>NPP</em>) is a product of intercepted photosynthetic active radiation (<em>Ipar</em>) and maximum radiation use efficiency (<em>RUE</em>_<em>max</em>; conversion of <em>Ipar</em> to biomass). The objective of this study was to improve and validate the <em>RUE</em>_<em>max</em>-based Carnegie-Ames-Stanford Approach (<em>CASA</em>) for the determination of grassland <em>NPP</em> by canopy multispectral reflectance collected at field (handheld sensor) and airborne (<em>UAV</em>) scale considering environmental constraints. The analysis was based on multi-year field experiments on sandy loam soil in Denmark, measured shoot and estimated root biomass to calculate <em>NPP</em>, long-term meteorological data, and daily <em>NPP</em> estimated from <em>CO</em>_<em>2</em> flux chamber measurements for deriving environmental constraints.</div><div>The results derived from <em>CO</em>_<em>2</em> flux data showed that <em>NPP</em> and plant respiration were higher in the middle of the season before the second harvest when temperature was also high. The daily maximum air temperature optimal for grass biomass production was 16.5 °C. The improved <em>CASA</em> model built in this study was accurate for modeling <em>NPP</em> at both daily (<em>nRMSE</em> decrease of 9 %) and seasonal (<em>nRMSE</em> decrease of 8–34 %) scales when considering the best environmental constraints such as maximum air temperature, vapor pressure deficit, cloudiness, and water stress, compared to no constraints. Maximum air temperature and water stress were the most important environmental constraints to the grass <em>RUE</em>_<em>max</em>. Seasonal <em>RUE</em>_<em>max</em> for modeling <em>NPP</em> after considering best environmental constraints was 1.9–2.7 g C MJ⁻¹ for ryegrass and 1.9–2.2 g C MJ⁻¹ for grass-legume mixture using the two remote sensors for measuring spectral reflectance. Over the whole growing season, tall fescue (3.1 g C MJ⁻¹) and festulolium (2.9 g C MJ⁻¹) obtained higher <em>RUE</em>_<em>max</em> than perennial ryegrass (2.3 g C MJ⁻¹).</div><div>This study highlights the practical implications of using the <em>CASA</em> model improved by maximum temperature and water stress functions for real-time, remote sensing-based assessments of grassland productivity.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"328 ","pages":"Article 114857"},"PeriodicalIF":11.1,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144223055","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":"Hierarchy features attention network for tiny ship detection from SDGSAT-1 thermal infrared images","authors":"Zeyi Yan ,&nbsp;Xuming Shi ,&nbsp;Lingjia Gu ,&nbsp;Zhuoyue Hu ,&nbsp;Fansheng Chen ,&nbsp;Zhiping He ,&nbsp;Weida Hu ,&nbsp;Fang Wang","doi":"10.1016/j.rse.2025.114842","DOIUrl":"10.1016/j.rse.2025.114842","url":null,"abstract":"<div><div>Accurate and reliable ship target detection is of great significance for the sustainable development goals of ocean management. With the development of remote sensing technology, satellite imagery provides strong support for space-based tiny ship detection. However, remote sensing images have complex backgrounds, and it is challenging to separate and locate different numbers of small ship targets in different scenarios. The thermal infrared bands can capture the temperature differences between ships and the surrounding marine environment, enabling effective detection. Therefore, this study used the Sustainable Development Goals Science Satellite 1 (SDGSAT-1) thermal infrared spectrometer (TIS) to develop a three-channel infrared small target detection (IRSTD) dataset with pixel-level annotations for all bands (SDG-IRSTD). The dataset contains 329 images from the SDGSAT-1 TIS and 3492 targets. Then a hierarchy features attention network (HFA-Net) for space-based tiny ship detection was proposed. The network generates enhanced feature maps of different scales through the multi-level detail enhancement module (MLDEM), employs a multi-level large kernel attention module (MLLKAM) which integrates the multi-scale mechanism with large kernel attention (LKA) to effectively model long-range dependencies on feature maps with different scales, and finally achieves feature fusion and interaction of different scales through the multi-level feature fusion module (MLFFM). In addition, the HFA-Net model improved intersection over union (IoU) and probability of detection (P_d) by 2.35 % and 3.97 %, respectively, and reduced false alarm rate (F_a) by 3.29 × 10⁻⁶, outperforming the state-of-the-art (SOTA) IRSTD methods. It can achieve target localization while obtaining the overall shape of the ship, providing important support for sustainable marine safety.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"328 ","pages":"Article 114842"},"PeriodicalIF":11.1,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144229942","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":"Forward modelling of passive microwave emissivities over snow-covered areas at continental scale","authors":"Iris de Gélis ,&nbsp;Catherine Prigent ,&nbsp;Carlos Jimenez ,&nbsp;Melody Sandells","doi":"10.1016/j.rse.2025.114821","DOIUrl":"10.1016/j.rse.2025.114821","url":null,"abstract":"<div><div>To assimilate passive microwave data in numerical weather prediction, a comprehensive understanding of the components of the radiative transfer equation is essential. Given the significant variability of emissivity in snow-covered regions — affected by frequency, polarisation, and the macro- and microstructural properties of snow — attention must be paid to the design of a forward model. However, existing physical models are unsuitable for global-scale studies due to their reliance on numerous inputs, such as snow grain size across different layers, which are typically unavailable at larger scales. In this study, we propose a method that utilises geophysical properties accessible at the continental scale to derive accurate emissivity values for frequencies ranging from 1 GHz to 90 GHz, in both vertical and horizontal polarisations, with a focus on the incident angles of conical scanners (approximately 50°). Our approach employs neural networks to obtain a robust forward model using geophysical variables as input data. A training dataset was developed based on satellite-derived surface emissivity from the SMOS and AMSR2 instruments by subtracting atmospheric components and surface temperature modulation. The results, which accounts for the actual geophysical state of the surface and its temporal variability, outperform the emissivity climatologies. We achieved snow-covered surface emissivities at the continental scale with a correlation coefficient above 0.9 and a RMSE below 0.02 for frequencies up to 18.7 GHz, and around 0.03 for higher frequencies. Additionally, we demonstrate that, in a typical tundra snowpack where the macro- and microstructural properties of snow can be obtained, the emissivities retrieved by our neural network-based forward model are consistent with results from the physical model (SMRT). This proposed model will also support preparations for the CIMR mission.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"328 ","pages":"Article 114821"},"PeriodicalIF":11.1,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144223053","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 of sea ice in 1975 and 1976 using the NIMBUS-6 Scanning Microwave Spectrometer (SCAMS)","authors":"Wiebke Margitta Kolbe ,&nbsp;Rasmus T. Tonboe ,&nbsp;Julienne Stroeve","doi":"10.1016/j.rse.2025.114815","DOIUrl":"10.1016/j.rse.2025.114815","url":null,"abstract":"&lt;div&gt;&lt;div&gt;The Scanning Microwave Spectrometer (SCAMS) onboard the NIMBUS-6 satellite operated between 15 June 1975 and 1 June 1976. Its primary mission objective was to map tropospheric temperature profiles for improving weather predictions, measuring Brightness Temperature(s) (&lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;T&lt;/mi&gt;&lt;mi&gt;B&lt;/mi&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;s) at five different frequencies (22.235, 31.65, 52.85, 53.85 and 55.45 GHz). However, the top-of-the-atmosphere emission measured at the 22.235 and the 31.65 GHz radiometer channels on the satellite are dominated by surface emission over polar open water and sea ice and can therefore be used for mapping sea ice concentration (SIC) on large scale (&lt;span&gt;&lt;math&gt;&lt;mo&gt;∼&lt;/mo&gt;&lt;/math&gt;&lt;/span&gt;100 km).&lt;/div&gt;&lt;div&gt;Here we present a SIC and ice type data set, which is based on the &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;T&lt;/mi&gt;&lt;mi&gt;B&lt;/mi&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt; observations of the two lowest frequencies of SCAMS (center frequencies at 22.235 &amp; 31.65 GHz). While the SCAMS channels do not completely align with the usual frequencies for sea ice retrievals (19, 22 and 37 GHz) in modern processing methods, it is still possible to apply modern techniques to reduce noise in the data. The SIC dataset provides important insights into the sea ice concentration, extent and type of the mid 1970s, where other satellite datasets e.g. the NIMBUS-5 ESMR have gaps and irregular coverage. The SCAMS data has been processed following modern methods, including a regional noise reduction over open water using a simple radiative transfer model, land-spillover corrections and estimation of uncertainties, as well as dynamical tie-points to calibrate the algorithm. The data set has been resampled into daily files with EUMETSAT’s OSI-SAF and ESA CCI compatible daily grids and land masks, for easier comparison with other data sets, such as the modern multi-frequency period starting with NIMBUS-7 SMMR in October 1978 to present and the 1972–1977 period covered by the NIMBUS-5 ESMR with some interruptions. The SCAMS &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;T&lt;/mi&gt;&lt;mi&gt;B&lt;/mi&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;s were processed with a hybrid SIC algorithm, combining a one and a two-channel algorithm over open water and ice respectively.&lt;/div&gt;&lt;div&gt;We find that the SIC calculated by the two-channel algorithm has more noise over water and low SIC areas than the single-channel algorithm. However, the two-channel algorithm does not systematically underestimate SIC in regions covered by Multi Year Ice (MYI) as the single channel algorithm does. A classification of sea ice types for First Year Ice (FYI) and MYI in the Northern Hemisphere (NH) proved successful, while it was also possible to identify two surface types A and B for the Southern Hemisphere (SH) sea ice, with different radiometric signatures.&lt;/div&gt;&lt;div&gt;A comparison of monthly mean sea ice extent (SIE) with the NIMBUS-5 ESMR showed good alignment in the both hemispheres, where the SCAMS SIE is larger by 386 676 km&lt;span&gt;&lt;math&gt;&lt;msup&gt;&lt;mrow&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;2&lt;/mn","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"328 ","pages":"Article 114815"},"PeriodicalIF":11.1,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144223054","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":"Multi-grained estimation of nighttime light dynamics during the COVID-19 surge in Shanghai with SDGSAT-1 GIU imagery and point of interest data","authors":"Zheng Zhang ,&nbsp;Huadong Guo ,&nbsp;Dongmei Yan ,&nbsp;Zhiqiang Liu ,&nbsp;Weixiong Zhang ,&nbsp;Jun Yan ,&nbsp;Ping Tang","doi":"10.1016/j.rse.2025.114822","DOIUrl":"10.1016/j.rse.2025.114822","url":null,"abstract":"<div><div>Nighttime light (NTL) imagery remotely sensed from outer space has been suggested to be a suitable proxy to investigate socioeconomic dynamics. Since the outbreak of COVID-19, many studies have used NTL imagery to estimate the impacts of the pandemic. However, finer-grained analytics are rarely achieved limited by the spatial resolution of major NTL data sources. In November, 2021, the Sustainable Development Science Satellite-1 (SDGSAT-1) was launched and one of its payloads, Glimmer Imager for Urbanization (GIU) can provide 10m/40 m panchromatic and multispectral NTL images for public use. In this study, we estimate the fine-grained NTL dynamics before and after the COVID-19 surge in the city of Shanghai during the second quarter of 2022 using SDGSAT-1 GIU nighttime imagery. To distinguish the different behaviors among urban functional entities, categorized Point of Interest (POI) data are adopted. The estimation is conducted in three progressive levels: city-level, POI-class-level, and POI-object-level. To characterize each urban objects from multiple angles, two additional NTL indices, NTL and luminous area ratio composite index (NTL-CI) and NTL background relative activeness index (NTL-AI) are introduced and estimated. On the basis of raw NTL, NTL-CI further considers the change of luminous area and NTL-AI further considers the relative change to the average standard. Moreover, detailed visual observations at typical POI objects are conducted, for instance, the Shanghai Disney Resort, the Shanghai Tesla Gigafactory, and multiple cabin hospitals temporarily converted from large stadiums and exhibition centers. This study aims to present a comprehensive investigation of the socioeconomic influence of COVID-19 in Shanghai from the perspective of NTL changes in multiple granularities, and the utility of SDGSAT-1 GIU nighttime imagery in supporting SDG 3 (The Sustainable Development Goals 3: Good Health and Well-Being) is also demonstrated with this set of quantitative analytics.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"328 ","pages":"Article 114822"},"PeriodicalIF":11.1,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144203859","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 universal yet easy-to-use data-driven method for angular normalization of directional land surface temperatures acquired from polar orbiters across global cities","authors":"Huilin Du ,&nbsp;Wenfeng Zhan ,&nbsp;Zihan Liu ,&nbsp;Chenguang Wang ,&nbsp;Fan Huang","doi":"10.1016/j.rse.2025.114840","DOIUrl":"10.1016/j.rse.2025.114840","url":null,"abstract":"<div><div>Urban thermal anisotropy poses significant challenges for accurately retrieving land surface temperature (LST) in urban environments using wide-swath polar orbiters. Existing physical and kernel-driven models often require detailed urban structural and property information or rely on simultaneous multi-angle LST observations, limiting their applicability for normalizing directional LSTs across diverse urban settings worldwide. Here we propose a UNIversal, easy-To-usE Data-driven (UNITED) method for angular normalization of directional LSTs across global cities, integrating advanced machine learning techniques with multi-source remote sensing and reanalysis data. We applied this method to normalize directional urban LSTs from all available wide-swath polar orbiters (Aqua MODIS, Terra MODIS, Suomi-NPP VIIRS) on Google Earth Engine, leveraging their full archives of multi-angle observations (2003–2024 for MODIS and 2012–2024 for VIIRS). The method's high accuracy in normalizing these three products was rigorously validated using quasi-simultaneous, near-nadir LSTs from various satellite platforms (e.g., Landsat) across tens of millions of urban pixels worldwide under diverse spatial, temporal, and angular conditions. For example, for Aqua MODIS observations with viewing zenith angle exceeding ±55°, angular normalization reduces the root mean square error and bias relative to nadir VIIRS LSTs (used as the reference) from 5.71 °C and −4.92 °C to 2.43 °C and −0.40 °C, respectively, underscoring the effectiveness of the UNITED method in harmonizing directional urban LSTs. Our study holds significant implications for advancing urban thermal remote sensing.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"328 ","pages":"Article 114840"},"PeriodicalIF":11.1,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144203858","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":"Spatial and temporal dynamics of plant water source distribution in China","authors":"Hongjiang Chen,&nbsp;Genxu Wang,&nbsp;Juying Sun,&nbsp;Li Guo,&nbsp;Chunlin Song,&nbsp;Xiangyang Sun","doi":"10.1016/j.rse.2025.114843","DOIUrl":"10.1016/j.rse.2025.114843","url":null,"abstract":"<div><div>Plant water use strategies play a crucial role in regulating soil moisture, mediating plant-climate feedbacks, and influencing species competition and symbiotic relationships. However, the lack of long-term and large-scale studies on plant water sources has significantly limited comprehensive estimations of the spatiotemporal variations in plant water sources and their impacts on ecohydrological processes. To address this challenge, this study compiled literature data from 210 study regions across China and applied a multivariate random forest model to generate a 0.25° × 0.25° spatial resolution map of plant water source distributions in China from 2001 to 2022. The model estimated the proportions of water uptake by plants from different soil depths and groundwater, and analyzed the variations in water sources across different vegetation types, as well as the key factors influencing plant water sources. The model results aligned well with existing experimental studies, demonstrating their reliability in capturing the spatiotemporal distribution and trends of plant water sources. Nationally, plants derived on average 34.64 ± 6.45 % of their water from shallow soil (0–30 cm), 24.60 ± 3.42 % from middle soil (30–60 cm), 32.68 ± 6.74 % from deep soil (&gt;60 cm), and 8.08 ± 4.7 % from groundwater. Significant differences in plant water sources were observed between southern and semi-arid northern regions of China, with plants in the southern regions predominantly relying on shallow and middle depth soil water, while those in the northwestern regions showed a greater dependence on deep soil water and groundwater. The correlation between soil water content and precipitation with variations in plant water sources was found to be more pronounced. Specifically, shallow soil moisture content and precipitation were positively correlated with the proportion of water absorbed by plants from shallow soil, but negatively correlated with the proportion of water absorbed from deep soil. From 2001 to 2022, there was a nationwide trend of an increased proportion of plant water uptake from shallow and deep soil layers, while the proportion from middle and groundwater decreased. This study fills a critical gap in the large-scale integrated study of plant water sources in China, providing valuable data and methodological references for related scientific research. The results of this study also contribute to the understanding of the ability to enhance vegetation adaptation to environmental changes, provide important driving data for ecohydrological model simulations and important data to support a more accurate assessment of ecosystem adaptation and water resource changes in the context of current extreme climate change.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"328 ","pages":"Article 114843"},"PeriodicalIF":11.1,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144203857","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":"Global retrieval of canopy chlorophyll content from Sentinel-3 OLCI TOA data using a two-step upscaling method integrating physical and machine learning models","authors":"Dong Li ,&nbsp;Holly Croft ,&nbsp;Gregory Duveiller ,&nbsp;Adam P. Schreiner-McGraw ,&nbsp;Anirudh Belwalkar ,&nbsp;Tao Cheng ,&nbsp;Yan Zhu ,&nbsp;Weixing Cao ,&nbsp;Kang Yu","doi":"10.1016/j.rse.2025.114845","DOIUrl":"10.1016/j.rse.2025.114845","url":null,"abstract":"<div><div>Canopy chlorophyll content per ground area (CCC, g·m⁻²) is tightly related to vegetation photosynthesis and is a promising indicator of photosynthetic capacity. However, a global operational CCC product is not yet available. To fill this gap, we developed a two-step upscaling method to estimate global CCC from Sentinel-3 OLCI top-of-atmosphere (TOA) reflectance. In the first step, a physically-based PROSAIL-D inversion model produced accurate CCC maps from over 20,000 high-spatial resolution (1 m) airborne hyperspectral images collected across 50 sites within the National Ecological Observatory Network (NEON) between 2019 and 2021. The validation against ground CCC measurements showed an R² of 0.89 and an RMSE of 0.30 g·m⁻². In the second step, these high-resolution CCC maps were resampled or upscaled to a spatial resolution of 300 m, and combined with Sentinel-3 OLCI TOA reflectance images to train random forest (RF) models. The RF model demonstrated robust performance with leave-one-site-out cross-validation, yielding an R² of 0.92 and RMSE of 0.14 g·m⁻². The two-step method also showed minimal sensitivity to angular effects and land cover variations, underscoring its robustness. In comparison, the traditional direct inversion method (the one-step method) led to underestimation of CCC by 0.16 g·m⁻² and a moderate estimation accuracy (R² = 0.65, RMSE = 0.30 g·m⁻²). We generated a long-term global OLCI CCC product using Sentinel-3 OLCI TOA reflectance data from 2016 to 2024, which can also be continuously updated using current data. This global CCC product can provide important plant physiological information, for parameterizing terrestrial biosphere models and capturing spatiotemporal photosynthetic patterns, thereby advancing research on vegetation carbon dynamics cycles at the global scale.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"328 ","pages":"Article 114845"},"PeriodicalIF":11.1,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144196225","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}