Remote Sensing of Environment最新文献

{"title":"3DLCDM: Hybrid supervision for land cover discovery mapping of emerging urban structures in 3D remote sensing","authors":"Jing Du ,&nbsp;John Zelek ,&nbsp;Dedong Zhang ,&nbsp;Jonathan Li","doi":"10.1016/j.rse.2025.115018","DOIUrl":"10.1016/j.rse.2025.115018","url":null,"abstract":"<div><div>Urban environments are characterized by continuous transformation, with new buildings, innovative infrastructures, evolving landforms, and emerging vegetation constantly reshaping the urban fabric. These dynamic changes create previously unannotated land cover classes that modify surface albedo, alter drainage patterns, and influence carbon storage, thereby affecting local climates, resource flows, and ecosystem services. Therefore, traditional land cover mapping methods based on static semantic labels are inherently limited. Even the most meticulously annotated datasets cannot comprehensively account for the full spectrum of urban classes. As urban environments continue to evolve, these static methods fail to capture the continual appearance of previously unannotated classes. This limitation leads to maps that quickly become outdated, incomplete, and imprecise, thereby impeding accurate environmental monitoring. To address this critical challenge, we propose Land Cover Discovery Mapping (LCDM), which integrates novel class discovery with land cover mapping, and we present an innovative end-to-end hybrid supervision framework, 3DLCDM, to implement LCDM in 3D remote sensing. The system has been tested on two high-resolution 3D point cloud datasets: one acquired via airborne LiDAR in Canada and the other obtained primarily using UAV-based LiDAR in Germany. Experimental results reveal that our 3DLCDM framework increases the mIoU for novel classes by up to 16.95% on the DALES dataset and up to 24.43% on the H3D dataset compared to baseline methods, demonstrating effective discovery capabilities under evaluation conditions that are procedurally equivalent to encountering genuinely novel urban features in practice. The proposed 3DLCDM framework demonstrates the potential to enable the continuous generation of up-to-date land cover maps that capture dynamic changes in urban morphology, thereby significantly advancing land cover discovery mapping. Furthermore, strong generalization across multiple datasets and urban feature types demonstrates the robustness of the framework’s discovery mechanisms and its capability to deliver high-fidelity maps that scale across diverse urban environments.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"331 ","pages":"Article 115018"},"PeriodicalIF":11.4,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145083828","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":"An insight into long-term continuity in global land surface phenology: A comparative analysis of MODIS and VIIRS products","authors":"Khuong H. Tran ,&nbsp;Xiaoyang Zhang ,&nbsp;Yongchang Ye ,&nbsp;Geoffrey M. Henebry ,&nbsp;Mark A. Friedl ,&nbsp;Yu Shen ,&nbsp;Yuxia Liu ,&nbsp;Shuai An ,&nbsp;Shuai Gao","doi":"10.1016/j.rse.2025.115024","DOIUrl":"10.1016/j.rse.2025.115024","url":null,"abstract":"<div><div>The Visible Infrared Imaging Radiometer Suite (VIIRS) Global Land Surface Phenology (GLSP) product (VNP22Q2 C2) has been operationally produced since 2013, and is designed to provide annual measurements of the phenologies of vegetated land surfaces at the global scale, succeeding the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Dynamics (LCD) product (MCD12Q2 C61), which was first produced in 2001. Although separate validations have been conducted locally to ensure the reliability and accuracy of the detected phenometrics for each product, a comprehensive understanding of the differences between these two operational products is important for downstream applications. Therefore, this study conducted critical analyses and cross-comparisons of the land surface phenology (LSP) products to ensure long-term continuity in the global phenological dynamics. Specifically, we compared five LSP products at 500 m spatial resolution: two NASA operational products of MCD12Q2 C61 and VNP22Q2 C2, as well as three other LSP products that were generated by applying the VNP22Q2 C2 algorithm to different time series from NOAA-20 VIIRS only, both SNPP and NOAA-20 VIIRS, and MODIS on Aqua and Terra. First, we cross-validated the five products at 500 m using a high-quality reference LSP dataset at 30 m that was generated by fusing the Harmonized Landsat and Sentinel-2 (HLS) observations with near-surface PhenoCam time series across diverse ecosystems in North America, Europe, and Japan. Second, cross-comparisons were conducted between the five LSP products at 500 m across 12 golden tiles. Third, the long-term comparability and continuity between the MCD12Q2 C61 and the VNP22Q2 C2 products were assessed globally. These comprehensive evaluations demonstrated that the VNP22Q2 C2 product provides overall global continuity for the MCD12Q2 C61 record. Compared to independent reference data from the HLS-PhenoCam LSP product, the four LSP products derived from the VNP22Q2 C2 algorithm produced highly comparable results with a mean absolute difference (MAD) of ∼ 11 days and mean systematic bias (MSB) of ∼ 7 days. The cross-comparisons indicated a strong agreement among the five 500 m LSP products in the 12 selected golden tiles with MADs &lt; 7 days; however, their agreement with the MCD12Q2 C61 was slightly lower with MADs ∼ 9–10 days. The global-scale evidence of continuity between the MCD12Q2 C61 and VNP22Q2 C2 products was an absolute difference of &lt; 15 days, except in arid/semiarid, tropical, and high-latitude ecosystems. Finally, it is suggested that (1) the continuity from MODIS to VIIRS LSP products would be enhanced if the same phenological detection algorithm was applied to the MODIS data, and (2) the quality of the VIIRS GLSP product could be improved by integrating data from multiple VIIRS sensors.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"331 ","pages":"Article 115024"},"PeriodicalIF":11.4,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145083870","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":"Discovery of groundwater storage changes and zoning feature variations over the past two decades in the Mongolian Plateau for SDG 6.6","authors":"Kai Li ,&nbsp;Juanle Wang ,&nbsp;Ochir Altansukh ,&nbsp;Congrong Li ,&nbsp;Erdenebayar Bavuu ,&nbsp;Gan-Erdene Tsengel","doi":"10.1016/j.rse.2025.115026","DOIUrl":"10.1016/j.rse.2025.115026","url":null,"abstract":"<div><div>Groundwater is a critical resource for supporting Sustainable Development Goals (SDGs) in arid and semi-arid regions, such as the Mongolian Plateau (MP), which accounts for over 82 % of total water usage in Mongolia; however, its spatiotemporal dynamics remain underexplored. In this study, aiming to enhance the connection between the Groundwater Storage (GWS) and local SDG implementation, we established an integrated analytical framework encompassing groundwater change detection, driving force analysis, and groundwater sustainability assessment. This framework quantifies groundwater storage changes across the MP from April 2002 to December 2023. Results reveal an overall groundwater change rate of −2.96 mm/yr in the MP, which increases in the west and north and decreases in the east, center, and south. The Gobi area has shown increase trends in GWS. Attribution analysis reveals that areas with increasing GWS are more influenced by climatic conditions such as higher precipitation and lower evapotranspiration, while regions with significantly decreasing GWS are associated with insufficient natural recharge and stronger human activity intensity. By contrast, winter precipitation recharge played a crucial role in slowing down the decrease in groundwater storage. In the five major basins of the MP, the dominant factors leading to groundwater depletion varied and were driven by various social factors, such as population, livestock, agriculture, and mining. A dual-index classification combining precipitation minus evapotranspiration (P−ET) and GWS trends provides a convenient, interpretable and objective approach to assessing groundwater sustainability, offering strong correspondence with the Reliability–Resilience–Vulnerability (RRV) framework. This combined strategy not only captures groundwater trends but also enhances interpretability by linking local sustainability levels to both environmental and anthropogenic drivers, underscoring the need for targeted management to support SDG 6.6. The findings of the present study provide an accurate assessment framework and valuable scientific evidence for groundwater resource management in arid and semi-arid regions.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"331 ","pages":"Article 115026"},"PeriodicalIF":11.4,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145078394","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":"Segmenting and characterising ripple patterns on sand dunes using machine learning and 2D semi-variogram","authors":"Lucie A. Delobel ,&nbsp;David Moffat ,&nbsp;Emma Tebbs ,&nbsp;Andreas C.W. Baas","doi":"10.1016/j.rse.2025.115031","DOIUrl":"10.1016/j.rse.2025.115031","url":null,"abstract":"<div><div>Sand ripples, shaped by fluid flow like wind or water, are common on dunes on Earth and Mars. Their patterns reveal local transport conditions, offering insights into wind regimes where direct observations are lacking. Since manual mapping is slow and subjective, automated methods are essential for consistent large-scale analysis. This study presents two novel and complementary methods for mapping ripple patterns on Martian dunes using high-resolution imagery: a U-Net model for pattern classification and a 2D semi-variogram for measuring ripple spacing and orientation. Tested on 42 barchan dunes across six Martian regions, the U-Net showed reliable ripple classification (F1-score 79 %), while the variogram method achieved high accuracy for ripple spacing (R² = 0.78) and orientation (R² = 0.98). Together, these approaches enable efficient, large-scale analysis of ripples for sediment transport on any planetary surface and can be applied to other patterned features.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"331 ","pages":"Article 115031"},"PeriodicalIF":11.4,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145078393","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":"Impact of atmospheric effect and observation geometry on the directional distribution of blooming effect in VIIRS night-time light images","authors":"Xinyu Shi ,&nbsp;Miroslav Kocifaj ,&nbsp;Xi Li ,&nbsp;Deren Li ,&nbsp;Jinjin Li","doi":"10.1016/j.rse.2025.115017","DOIUrl":"10.1016/j.rse.2025.115017","url":null,"abstract":"<div><div>Blooming effect, a phenomenon that actual lit areas appear enlarged in nighttime light images acquired by satellites, has been widely studied in remote sensing community. However, the physical mechanisms behind its formation have not been quantitatively explored. In this study, we first reported a new and interesting phenomenon that the blooming effect exhibits an east-west elliptical shape at point light sources in VIIRS nighttime light images. Based on this observation we hypothesize that the blooming shape is influenced by the observation geometry (i.e., viewing zenith angle (VZA)) and atmospheric scattering. To test this hypothesis, the physical mechanism behind the formation of the blooming shape through numerical simulations using the Method of Successive Orders of Scattering (MSOS) model was revealed. More than 1000 numerical simulations were conducted using MSOS model under various aerosol conditions, observation geometries, and wavelengths, revealing a positive correlation between the eccentricity of the blooming shape and VZA, with R² values exceeding 0.9. To validate these findings with satellite observations, we analyzed VNP46A1 data of Black Marble product from 10 gas flaring regions worldwide. The results reconfirmed the positive correlation between the eccentricity of the blooming shape and VZA across all the selected regions, with R² values exceeding 0.5. Both numerical simulations and satellite observations supported the hypothesis. This study revealed the physical mechanism behind the blooming effect, suggesting that physical models are important to understand and advance nighttime light remote sensing.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"331 ","pages":"Article 115017"},"PeriodicalIF":11.4,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145067741","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":"Neural disaster simulation for transferable building damage assessment","authors":"Zhuo Zheng ,&nbsp;Yanfei Zhong ,&nbsp;Zijing Wan ,&nbsp;Liangpei Zhang ,&nbsp;Stefano Ermon","doi":"10.1016/j.rse.2025.114979","DOIUrl":"10.1016/j.rse.2025.114979","url":null,"abstract":"<div><div>Timely and reliable building damage assessment is essential for effective disaster response and humanitarian assistance. However, the diversity of disaster types, geographic regions, and data distributions poses significant challenges to transferring building damage assessment models to new disaster scenarios (i.e., target domains). In addition, limited availability of post-disaster training imagery in target domains further hinders progress. Recent approaches, such as single-temporal change adaptation, enable adaptation using only target pre-disaster images by constructing pseudo bitemporal damage samples at an unexplainable embedding level. While effective, these methods produce representations that are difficult for human experts to interpret, inspect for errors, or adjust categorical distributions to ensure reliable model performance. In this paper, we propose Neural Disaster Simulation (NeDS), a deep disaster generative model that synthesizes realistic post-disaster image from pre-event image and customizable disaster information (i.e., disaster types and disaster intensity). Thanks to damage data generation based solely on pre-event imagery, NeDS enables adaptation at any time, effectively bypassing the limitation of post-disaster training image availability. Furthermore, by explicitly modeling disaster effects at the image level, NeDS mitigates distribution shifts between historical training data and unseen disaster events, enhancing both model transferability and visual interpretability. Extensive experiments conducted on both global-scale and local-scale study areas demonstrate that NeDS adaptation outperforms the previous state-of-the-art, achieving a 4.3% improvement in average performance on a global dataset, as well as 3.6%, 7.9%, and 18.5% gains in damage classification performance for the 2025 Eaton fire, the 2025 Palisades fire in Los Angeles, and the 2025 Nigeria flooding, respectively.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"331 ","pages":"Article 114979"},"PeriodicalIF":11.4,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145067733","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":"Improvements of AI-driven emission estimation for point sources applied to high resolution 2-D methane-plume imagery","authors":"Thomas Plewa ,&nbsp;André Butz ,&nbsp;Christian Frankenberg ,&nbsp;Andrew K. Thorpe ,&nbsp;Julia Marshall","doi":"10.1016/j.rse.2025.115002","DOIUrl":"10.1016/j.rse.2025.115002","url":null,"abstract":"<div><div>Anthropogenic methane (CH₄) sources have had a considerable impact on the Earth’s changing radiation budget since pre-industrial times. Localized sources such as those resulting from the fossil fuel industry and waste treatment have been shown to make up a substantial fraction of the emission total, and CH₄ plumes from such sources are detectable through airborne and space-based hyperspectral imaging techniques. Here, we further develop a machine learning technique to estimate CH₄ emission rates from such plume images without the need for auxiliary data such as local wind speed information. We directly build upon the idea of previous research which used a convolutional neural network (CNN) called MethaNet and a library of large-eddy-simulations (LES) of turbulent CH₄ plumes as our synthetic data environment. Here we suggest appropriate error metrics and changes to the training procedure that reduce systematic biases present in previous studies. Our improved setup has a mean absolute percentage error (MAPE) of 10% for sources with flux rates above 40<!--> <!-->kg<!--> <!-->h⁻¹, a Pearson correlation coefficient of 98% and is capable of providing meaningful error estimates for its predictions. This is a significant improvement to MethaNet and other studies and can be used as an efficient method for point source quantification in the future.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"331 ","pages":"Article 115002"},"PeriodicalIF":11.4,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145043044","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":"Geospatial big data-driven roof greening priority and benefit assessment in Hong Kong","authors":"Jie Shao ,&nbsp;Wei Yao ,&nbsp;Lei Luo ,&nbsp;Linzhou Zeng ,&nbsp;Zhiyi He ,&nbsp;Puzuo Wang ,&nbsp;Xingjian Fu ,&nbsp;Jianbo Qi ,&nbsp;Huadong Guo","doi":"10.1016/j.rse.2025.115004","DOIUrl":"10.1016/j.rse.2025.115004","url":null,"abstract":"<div><div>Green roofs constitute a critical component in enhancing urban sustainability and resilience, making the systematic assessment of roof greening initiatives a pivotal focus in urban research and planning. A thorough understanding of implementation priorities and potential benefits is essential for effectively promoting green roof adoption. As a multifaceted urban intervention, roof greening involves complex stakeholder coordination and requires comprehensive assessments across various urban development dimensions. Nevertheless, significant research gaps remain regarding the multi-criteria evaluation of building-level roof greening priorities and their associated benefits at urban scales. Here, using geospatial big data, we conduct an urban-scale assessment of roof greening at a single building level in Hong Kong from a sustainable development perspective. We identify that 85.3 % of buildings reveal potential and urgent demand for roof greening (average priority&gt;0.6). We further find green roofs could increase greenspace coverage rate around buildings by 65.7 %, produce hundreds of millions (HK$) in economic benefits annually, and contribute to approximately 0.15 °C urban temperature reduction and 0.8 % annual carbon emission offsets. Our study offers a comprehensive assessment of roof greening, which could provide a reference for sustainable development in cities worldwide, from data utilization to solutions and findings.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"331 ","pages":"Article 115004"},"PeriodicalIF":11.4,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145043046","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":"The progress and potential directions in the remote sensing of farmland abandonment","authors":"Alexander V. Prishchepov ,&nbsp;Katharina Anders ,&nbsp;Jan Feranec ,&nbsp;Tomáš Goga ,&nbsp;Simona R. Gradinaru ,&nbsp;Jan Kolář ,&nbsp;Robert Pazur ,&nbsp;Markéta Potůčková ,&nbsp;Bogdan Zagajewski ,&nbsp;Lucie Kupková","doi":"10.1016/j.rse.2025.115019","DOIUrl":"10.1016/j.rse.2025.115019","url":null,"abstract":"<div><div>The world is facing increasing land scarcity due to growing demand for agricultural products and urban expansion. At the same time, farmland abandonment is emerging as a widespread global land-use change phenomenon. Remote sensing plays a critical role in identifying abandonment across diverse farming systems. Here, we synthesized current knowledge through a systematic literature review of 131 publications to assess progress in remote-sensing-based monitoring of farmland abandonment. Our review highlights the growing use of remote sensing techniques and the increasing utility of multisource satellite data. However, research remains primarily skewed toward publicly available optical Landsat and Sentinel-2 data, with limited integration of other sources and a lack of global-scale assessments. We propose research directions to guide future studies, focusing on underrepresented land-use types, such as grasslands, terraces, and plantations, as well as regions like Africa, Central and Southeast Asia, and South America. We emphasize the importance of diversifying data sources and integrating ancillary information, including cadastral data and historical land-use records, to better understand abandonment processes and associated vegetation changes. We advocate for multi-scale, temporally explicit analyses to improve scalability and enable the development of regional and continental products. As most studies focus on biophysical changes, future work should also consider socio-economic contexts and integrate remote-sensing-based proxies of land-use change. Finally, we recommend improving communication by clearly defining abandonment, providing visual examples, validating with diverse reference data, documenting uncertainty, and sharing data, code, and outputs.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"331 ","pages":"Article 115019"},"PeriodicalIF":11.4,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145043045","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 Normalized Harvest Phenology Index (NHPI) for corn and soybean harvesting date detection using Landsat and Sentinel-2 imagery on Google Earth Engine","authors":"Yin Liu ,&nbsp;Chunyuan Diao ,&nbsp;Zijun Yang ,&nbsp;Weiye Mei ,&nbsp;Tianci Guo","doi":"10.1016/j.rse.2025.115016","DOIUrl":"10.1016/j.rse.2025.115016","url":null,"abstract":"<div><div>The timing of harvesting is crucial for determining crop yield potential as it influences the final stages of the crop growth cycle and affects crop grain quality. Early harvesting can lead to yield losses from excessive moisture and insufficient dry matter, while delayed harvesting can degrade grain quality due to over-maturation and increased susceptibility to weather, pests, and diseases. Accurate monitoring of harvest timing is essential to assess yield gaps, support profitable and sustainable farming practices, and optimize agricultural supply chains. However, remote sensing-based harvesting date detection methods often suffer from biases due to the inconsistent relationship between end-of-season (EOS) metrics in vegetation index (VI) time series and actual harvesting dates. This inconsistency occurs because harvesting decisions are often influenced by human factors such as equipment availability, labor constraints, and fuel costs, rather than plant condition alone. In this study, we develop a novel Normalized Harvest Phenology Index (NHPI) that integrates the Normalized Difference Vegetation Index (NDVI) and the Near-Infrared (NIR) reflectance to accurately monitor whether fields of corn and soybean have been harvested. Leveraging the distinct separability of NIR reflectance for corn and soybean before harvesting (senescent plants) and after harvesting (crop residue), combined with the contrasting trends between NIR and NDVI during this transition, the NIR-to-NDVI ratio amplifies the harvesting signal in its time series, making it a robust indicator of harvesting events. As the first spectral index designed for scalable identification of crop harvesting stage, the developed NHPI is applied to map harvesting dates for corn and soybean fields across the U.S. Midwest from 2020 to 2023 using Landsat and Sentinel-2 imagery via Google Earth Engine (GEE). At the field level, the NHPI-based harvesting date estimation method achieves a mean absolute error (MAE) of 4 days and an R² of 0.85 when compared against field-recorded harvesting dates, significantly outperforming all advanced harvesting date estimation benchmarks (i.e., EOS phenometric-based method, shape model fitting method (SMF), and shape model fitting by the separate phenological stage method (SMF-S). The NHPI-based harvesting date mapping also shows strong alignment with the state-level cumulative distribution of harvesting dates of the USDA crop progress reports, achieving an average MAE of 3 days. Further analysis of NHPI values before and after harvesting events reveals its strong adaptability to diverse weather conditions at large scales, highlighting its efficiency and robustness.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"331 ","pages":"Article 115016"},"PeriodicalIF":11.4,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145027423","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}