ISPRS Journal of Photogrammetry and Remote Sensing最新文献

{"title":"Tidal flat topography mapping with Sentinel time series using cross-modal sample transfer and deep learning","authors":"Pengfei Tang , Shanchuan Guo , Lu Qie , Xingang Zhang , Hong Fang , Liang Wan , Jocelyn Chanussot , Peijun Du","doi":"10.1016/j.isprsjprs.2025.04.017","DOIUrl":"10.1016/j.isprsjprs.2025.04.017","url":null,"abstract":"<div><div>Tidal flats are crucial components of coastal geomorphic systems, where the ocean meets the land. Timely and accurate topographic maps of tidal flats are essential for sustainable coastal management and development. Although satellite imagery-based inversion methods offer a cost-effective solution for constructing large-scale intertidal topography, their accuracy remains heavily dependent on the availability and quality of satellite data. Frequent cloudy and rainy weather in coastal areas presents significant challenges for extracting waterlines from optical images. To address these challenges, this study developed an integrated framework that leverages the complementary strengths of optical and Synthetic Aperture Radar (SAR) imagery, providing an innovative solution to accurately map tidal flat topographies at high spatial resolution. By utilizing the high-precision spatiotemporal distribution results of tidal flats extracted from optical images and integrating tidal constraints and temporal conditions, a cross-modal sample transfer strategy for Optical-SAR imagery was designed, which automatically generates a pseudo-sample library for SAR images. To optimize the automatic extraction of tidal flats in complex SAR imagery environments, we constructed a hybrid semantic segmentation network, UCTCNet. UCTCNet combines the local feature extraction capabilities of convolutional neural networks with the global information focus provided by attention mechanisms. ICESat-2 data was used as altimetry input based on the relationship between tidal flat elevations and inundation frequencies, which was combined with derived inundation frequency maps, low-tide imagery, and spectral indices to accurately invert tidal flat elevations using a random forest algorithm. Experimental results showed that the UCTCNet model demonstrated high potential in processing single-channel, high-noise, weak-feature Sentinel-1 SAR imagery, achieving an IoU of over 0.90, indicating strong performance in extracting high-level semantic features of tidal flats. The elevation inversion framework was validated along the entire coastal region of Jiangsu, China, for multi-temporal and multi-scene analysis. Further validation using generated topographic maps from unmanned aerial vehicle photogrammetry showed superior performance (RMSE = 0.24 m) compared to existing public tidal flat elevation data. The framework was also applied to derive DEMs from 2019 to 2023, revealing significant spatial and elevation changes in the North Jiangsu Radial Sand Ridges. The results further demonstrated the influence of various features, including inundation frequency maps, low-tide imagery, and spectral indices, on elevation inversion accuracy. The integration of S1 SAR data not only improved inversion accuracy but also helped address the limitations associated with discrete frequency data. These findings demonstrate that our proposed framework offers novel insights into high-resolution, large-sca","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"225 ","pages":"Pages 69-87"},"PeriodicalIF":10.6,"publicationDate":"2025-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143876716","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":"Deep learning coupled with split window and temperature-emissivity separation (DL-SW-TES) method improves clear-sky high-resolution land surface temperature estimation","authors":"Huanyu Zhang ,&nbsp;Tian Hu ,&nbsp;Bo-Hui Tang ,&nbsp;Kanishka Mallick ,&nbsp;Xiaopo Zheng ,&nbsp;Mengmeng Wang ,&nbsp;Albert Olioso ,&nbsp;Vincent Rivalland ,&nbsp;Darren Ghent ,&nbsp;Agnieszka Soszynska ,&nbsp;Zoltan Szantoi ,&nbsp;Lluís Pérez-Planells ,&nbsp;Frank M. Göttsche ,&nbsp;Dražen Skoković ,&nbsp;José A. Sobrino","doi":"10.1016/j.isprsjprs.2025.04.016","DOIUrl":"10.1016/j.isprsjprs.2025.04.016","url":null,"abstract":"<div><div>Land surface temperature (LST) is a fundamental parameter in environmental and climatic studies. Over the past decades, various clear-sky LST retrieval methods have been developed, among which the temperature-emissivity separation (TES) algorithm prevails due to its good accuracy and the simultaneous retrieval of LST and land surface emissivity (LSE). However, TES relies on complete atmospheric profiles and radiative transfer calculations for atmospheric correction, which accumulates large uncertainties and requires intensive computation. In this study, we integrated the physical mechanisms of the split window (SW) and TES algorithms into the deep learning (DL) model, constructing the DL-SW-TES framework. This new framework directly retrieves LST from easily accessible parameters without requiring any prior knowledge of LSE information and atmospheric profiles. The DL-SW-TES framework was evaluated using both the simulation dataset and high-resolution ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) observations. The simulation analysis showed that the DL-SW-TES method achieved a root mean squared error (RMSE) of 1.05 K in LST retrieval and appeared robust across various uncertainty conditions. The evaluation of the ECOSTRESS LST estimates at the six radiometer sites revealed that the DL-SW-TES method achieved a better performance with an overall RMSE of 1.56 K and a bias of −0.06 K compared to the official ECO2LTSE product (with an RMSE of 1.94 K and a bias of −0.25 K). The nighttime ground measurements from the twelve pyrgeometer sites reaffirms the accuracy improvements achieved by the new model, with bias reduced by 0.7 K and RMSE reduced by approximately 0.3 K. LST estimates from DL-SW-TES and the ECO2LTSE product also present good consistency in terms of spatial patterns. The demonstrated advantage of the developed DL-SW-TES method over the traditional TES is attributed to its simplified input parameters and robustness to uncertainties in these parameters. We conclude that DL-SW-TES achieves improved accuracy compared to the traditional TES algorithm with significantly simplified input parameters and enhanced computational efficiency, standing as a promising approach for mapping clear-sky high-resolution LST at large scales from the future thermal missions. The source code and data are available at <span><span>https://github.com/cas222huan/DLSWTES</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"225 ","pages":"Pages 1-18"},"PeriodicalIF":10.6,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143864752","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":"SCAGAT: A scene-aware ensemble graph attention network for global PM2.5 pollution mapping via land–atmosphere interactions","authors":"Kaixu Bai ,&nbsp;Ke Li ,&nbsp;Songyun Qiu ,&nbsp;Zhe Zheng ,&nbsp;Penglong Jiao ,&nbsp;Yibing Sun ,&nbsp;Liuqing Shao ,&nbsp;Chaoshun Liu ,&nbsp;Xinran Li ,&nbsp;Zhengqiang Li ,&nbsp;Jianping Guo ,&nbsp;Ni-Bin Chang","doi":"10.1016/j.isprsjprs.2025.04.019","DOIUrl":"10.1016/j.isprsjprs.2025.04.019","url":null,"abstract":"<div><div>The sparse and uneven distribution of ground-based air quality monitoring stations poses significant challenges for large scale PM_2.5 pollution mapping. Spatially heterogenous land–atmosphere interactions often lead to large uncertainties in satellite-based PM_2.5 estimations from global modeling strategies. To enhance global PM_2.5 mapping accuracy, particularly in poorly monitored regions, we propose a novel ensemble learning framework called the SCene-Aware ensemble Graph ATtention network (SCAGAT), which integrates locally trained PM_2.5 prediction models across regions using a graph attention network and transfer learning concept. Unlike popular global modeling strategy, SCAGAT first constructs thousands of site-specific PM_2.5 estimation models at individual monitoring station using the random forest (RF) method. For each target grid, raw PM_2.5 estimates are predicted by the 32 site-specific RF models with the most similar geographic scene attributes, characterized by nine variables relevant to haze pollution levels, land cover, and climate characteristic. A graph attention network then aggregates these initial estimates to produce an optimal PM_2.5 prediction through ensemble learning. By taking advantage of the strength of SCAGAT, global daily gap-free PM_2.5 concentrations over land from 2000 to 2021 were finally mapped based on a long-term gap-filled aerosol optical depth dataset. Cross-validation shows that SCAGAT achieves high global PM_2.5 modeling accuracy, with a correlation coefficient of 0.909 and a root-mean-squared error of 9.87 μg m⁻³. Intercomparison results demonstrate SCAGAT’s superiority over other widely used global modeling methods, reducing PM_2.5 modeling bias by 44.2 %, 12.7 %, 32.4 %, 44.4 %, and 48.3 % in China, the USA, Europe, India, and a global product, respectively. Overall, SCAGAT provides a robust solution for large-scale air quality mapping and effectively resolves data imbalance related low accuracy in poorly monitored areas by accounting for geographic scene similarity. Furthermore, this method can be readily adapted to other data-driven Earth observing applications facing similar challenges.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"225 ","pages":"Pages 19-35"},"PeriodicalIF":10.6,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143864649","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":"Can satellite observations detect global ocean heat content change with high resolution by deep learning?","authors":"Hua Su ,&nbsp;Jianchen Teng ,&nbsp;Feiyan Zhang ,&nbsp;An Wang ,&nbsp;Zhanchao Huang","doi":"10.1016/j.isprsjprs.2025.04.018","DOIUrl":"10.1016/j.isprsjprs.2025.04.018","url":null,"abstract":"<div><div>The development of <em>in situ</em> observations has significantly improved ocean heat content (OHC) estimation. However, high-resolution OHC data remain limited, hindering detailed studies on mesoscale oceanic warming variability. This study used a deep learning method-Densely Deep Neural Network (DDNN) to reconstruct a high-resolution (0.25° × 0.25°) global OHC dataset for the upper 2000m ocean from 1993 to 2023, named the Ocean Projection and Extension Neural Network 0.25° (OPEN0.25°) product. This deep ocean remote sensing approach integrates multi-source remote sensing data, including Sea Surface Temperature (SST), Absolute Dynamic Topography (ADT), and Sea Surface Wind (SSW), alongside spatiotemporal coordinates and <em>in situ</em> observations. The DDNN model was trained using Argo-based gridded data and EN4-profile data, initially undergoing pre-training to assimilate large-scale oceanic features, followed by fine-tuning to enhance its accuracy in capturing mesoscale thermal structures. Our results demonstrate that the DDNN model achieves high accuracy across various depths. Particularly, OPEN0.25° can effectively capture detailed thermal variations in regions with complex dynamics, as well as the heat transfer processes within the ocean interior, outperforming traditional methods in resolution. The research highlights that, influenced by strong El Niño-Southern Oscillation (ENSO) events, OHC in the upper 700m of the Pacific Ocean potentially far exceeding expectations over the past decade. Through this study, OPEN0.25° has demonstrated its critical role in detecting and monitoring long-term changes in global OHC at high resolution.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"225 ","pages":"Pages 52-68"},"PeriodicalIF":10.6,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143864551","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":"Efficient large-scale vegetation mapping at the formation level using multi-source data: A case study in Beijing, China","authors":"Jiachen Xu ,&nbsp;Yongmei Huang ,&nbsp;Kai Cheng ,&nbsp;Yi Wang ,&nbsp;Tianyu Hu ,&nbsp;Hongcan Guan ,&nbsp;Yuling Chen ,&nbsp;Yu Ren ,&nbsp;Mengxi Chen ,&nbsp;Zekun Yang ,&nbsp;Jiarui Wang ,&nbsp;Qinghua Guo","doi":"10.1016/j.isprsjprs.2025.04.021","DOIUrl":"10.1016/j.isprsjprs.2025.04.021","url":null,"abstract":"<div><div>Formation-level vegetation mapping is pivotal for understanding ecological processes and mechanisms, as it reveals the distribution of dominant species that shape ecosystem structure and dynamics. However, fast and accurate formation-level mapping over large geographic areas is often hindered by the lack of robust mapping frameworks, limited field survey data, and unscientific or inefficient division of vegetation patches. To address these challenges, we proposed an automated mapping framework that integrates multi-source data for formation-level vegetation mapping. Our approach introduced an innovative strategy for automatically delineating vegetation patches based on slope units, improving mapping efficiency and ensuring results align more closely with actual vegetation distribution. Additionally, we developed a crowdsource-based vegetation survey system that aggregates data from diverse sensors, significantly increasing the sample size and diversity of vegetation formations. Using this framework, we successfully mapped 16 formations in Beijing with an overall accuracy of 65.7%, achieving F-scores exceeding 60% for major formations. The result indicates that Beijing’s vegetation is dominated by forests and shrublands, with the largest vegetation formation being <em>Vitex negundo</em> (deciduous broadleaf shrubland), covering 20% of the city in the southwestern mountains, followed by <em>Quercus mongolica</em> (deciduous broadleaf forest), occupying 10% in the northwestern mountains. This study provides a solid foundation for understanding Beijing’s vegetation distribution and its ecological functions. By integrating remote sensing and crowdsourced data, it demonstrates an effective approach for precise, large-scale formation-level vegetation mapping, offering valuable support for refined ecological management and interdisciplinary research.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"225 ","pages":"Pages 36-51"},"PeriodicalIF":10.6,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143864550","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":"AWDA: Adversarial and Weighted Domain Adaptation for cross-dataset change detection","authors":"Xueting Zhang,&nbsp;Xin Huang,&nbsp;Jiayi Li","doi":"10.1016/j.isprsjprs.2025.04.008","DOIUrl":"10.1016/j.isprsjprs.2025.04.008","url":null,"abstract":"<div><div>Recent advancements in change detection (CD) using fully-supervised methods have been significant; however, effectively applying CD in scenarios where labels are unavailable remains a challenge. To address this, our study introduces a new task, domain adaptive change detection (DACD), which transfers change knowledge from a labeled CD dataset (source domain) to an unlabeled CD dataset (target domain). In practice, two challenges hinder change knowledge transfer across domains: domain shifts, such as resolution differences and change semantic discrepancies, and imbalanced distribution between the minority change class and the dominant no-change class. To tackle these issues, we propose a novel Adversarial and Weighted Domain Adaptation (AWDA) framework for DACD. AWDA employs a Siamese encoder–decoder network shared between source and target domains to extract features and make predictions from bi-temporal remote sensing images. Moreover, AWDA incorporates three cross-domain learning strategies for learning domain-invariant CD representations: (1) supervised learning, which uses all the labeled data of the source domain to train the model to obtain initial CD capability, (2) domain adversarial training, which aligns the features between the source and target domains adversarially, and (3) class-weighted self-training, which dynamically computes and assigns class weights for the self-training on the unlabeled data of the target domain. The proposed AWDA effectively mitigates cross-domain shifts and preserves the integrity of the minor change class during knowledge transfer. To evaluate our method’s effectiveness, we conducted comprehensive experiments across four cross-domain CD scenarios using three well-known building CD datasets. The results demonstrate AWDA substantially enhances CD performance in the target domain, achieving IoU increase ranging from 13.64 to 34.73, and significantly surpassing several competing domain adaptation methods. Our code will be available at <span><span>https://github.com/zxt9/AWDA</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"224 ","pages":"Pages 398-409"},"PeriodicalIF":10.6,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143856115","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":"From rice planting area mapping to rice agricultural system mapping: A holistic remote sensing framework for understanding China's complex rice systems","authors":"Zizhang Zhao ,&nbsp;Jinwei Dong ,&nbsp;Jilin Yang ,&nbsp;Luo Liu ,&nbsp;Nanshan You ,&nbsp;Xiangming Xiao ,&nbsp;Geli Zhang","doi":"10.1016/j.isprsjprs.2025.03.026","DOIUrl":"10.1016/j.isprsjprs.2025.03.026","url":null,"abstract":"<div><div>Information on the rice agricultural system, including not only planting area but also phenology and cropping intensity, is critical for advancing our understanding of food and water security, methane emissions, carbon and nitrogen cycles, and avian influenza transmission. However, existing efforts primarily focus on mapping planting area and lack a comprehensive picture of the rice agricultural system. To address this gap, we propose a remote sensing-based comprehensive framework for mapping the rice agricultural system in China: First, we identified valid growth cycles of crop by using 30-m Sentinel-2 and Landsat fused data; Second, we applied a well-established phenology-based algorithm to map rice planting areas, by extracting the flooding and rapid growth signals in the transplanting and rapid growth temporal windows; Third, the rice-specific phenology phases (i.e., transplanting, tillering, heading, and mature) were identified using a phenology extraction method tailored to the physiological characteristics of rice; Finally, rice cropping intensity was determined based on detailed phenological phases and planting area data. Due to the accurate identification of crop cycles and pixel-level temporal windows at the national scale, the generated rice planting area maps exhibit a high accuracy across China, with both overall accuracy and F1 scores exceeding 0.8, based on validation with over 13,000 field samples. Improvements in the extraction method have addressed the lag in phenology detection caused by rice’s flooded environment, leading to more accurate phenological information. As a result, the phenological data shows reliable accuracy (<em>R²</em> of 0.6–0.8 and <em>RMSE</em> of 8–15 days), facilitated by the mutual enhancement of rice planting area and phenology mapping. The resultant rice phenology and cropping intensity maps are the first of their kind with 30 m resolution. Together, the resultant rice planting area, rice phenology, and cropping intensity maps provide, for the first time, a comprehensive picture of China's rice agricultural system, better supporting multiple targets related to Sustainable Development Goals.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"224 ","pages":"Pages 382-397"},"PeriodicalIF":10.6,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143848565","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":"New maps of mafic mineral abundances in global mare units on the Moon","authors":"Yuzhen Wang ,&nbsp;Haijun Cao ,&nbsp;Jian Chen ,&nbsp;Changqing Liu ,&nbsp;Xuejin Lu ,&nbsp;Chengxiang Yin ,&nbsp;Xiaohui Fu ,&nbsp;Le Qiao ,&nbsp;Guang Zhang ,&nbsp;Chengbao Liu ,&nbsp;Peng Zhang ,&nbsp;Zongcheng Ling","doi":"10.1016/j.isprsjprs.2025.04.010","DOIUrl":"10.1016/j.isprsjprs.2025.04.010","url":null,"abstract":"<div><div>Lunar surface mineralogy plays a crucial role in characterizing the distribution and abundance of silicate minerals, providing pivotal insights into the geological evolution of the Moon. Existing lunar mineral distribution maps are primarily derived from the calibration of Apollo and Luna sample datasets, which are predominantly older than 3.0 Ga. However, these maps lack critical constraints from younger mare basalt samples, limiting their ability to fully represent the mineralogical diversity of lunar mare units. In this work, we present updated global mineral abundance maps for olivine (OLV), high-Ca pyroxene (HCP), and low-Ca pyroxene (LCP) on the global mare units using the partial least squares regression (PLS) algorithm applied to Moon Mineralogy Mapper data, incorporated the distinctive 2.0-Ga Chang’e-5 lunar samples as a new calibration reference. The revised maps indicate mean HCP, LCP, and OLV abundances of 48.4 wt%, 38.2 wt%, and 13.2 wt%, respectively, among global mare units. Our results reveal a systematic trend of HCP enrichment and OLV depletion in younger mare basalts, which correlates with variations in TiO₂ content and model ages. HCP abundance follows a Gaussian distribution, increasing from low-Ti to high-Ti units, whereas LCP exhibits an inverse trend. OLV abundance only shows a slightly decrease from medium-Ti to low-Ti units. From a geochemical perspective, mare units become progressively enriched in HCP with increasing FeO content and decreasing Mg#, while less evolved mare units, characterized by higher LCP abundances, retain higher Mg# and lower FeO content. In the new maps, the localized variations in mineral abundances are possibly related to subsurface materials excavated by impact events, highland ejecta deposits, and space weathering.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"224 ","pages":"Pages 348-360"},"PeriodicalIF":10.6,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143844514","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 robust framework for mapping complex cropping patterns: The first national-scale 10 m map with 10 crops in China using Sentinel 1/2 images","authors":"Bingwen Qiu ,&nbsp;Fangzheng Wu ,&nbsp;Xiang Hu ,&nbsp;Peng Yang ,&nbsp;Wenbin Wu ,&nbsp;Jin Chen ,&nbsp;Xuehong Chen ,&nbsp;Liyin He ,&nbsp;Berry Joe ,&nbsp;Francesco N. Tubiello ,&nbsp;Jianping Qian ,&nbsp;Laigang Wang","doi":"10.1016/j.isprsjprs.2025.04.012","DOIUrl":"10.1016/j.isprsjprs.2025.04.012","url":null,"abstract":"<div><div>Complex cropping patterns with crop diversity are an underexploited treasure for global food security. However, significant methodological and dataset gaps in fully characterizing cropland cultivated with multiple crops and rotation sequences hinder our ability to understand and promote sustainable agricultural systems. Existing crop mapping models are challenged by the deficiency of ground reference data and the limited transferability capabilities across large spatial domains. This study aimed to fill these gaps by proposing a robust Complex Cropping Pattern Mapping framework (CCPM) capable of national-scale automatic applications using the Sentinel-1 SAR and Sentinel-2 MSI time series datasets. The CCPM framework addresses these challenges by integrating knowledge-based approaches &amp; data-driven algorithms (Dual-driven model) and Phenological Normalization. The CCPM framework was implemented over conterminous China with complex cropping systems dominated by smallholder farms, and the first national-scale 10-m Cropping pattern map with descriptions of cropping intensity and 10 crops in China (ChinaCP-T10) in 2020 was produced. The efficiency of the CCPM framework was validated when evaluated by 18,706 ground-truth reference datasets, with an overall accuracy of 91.47 %. Comparisons with existing crop data products revealed that the ChinaCP-T10 offered more comprehensive and consistent information on diverse cropping patterns. Dominant cropping patterns diversified from single maize in northern China, winter wheat-maize in North China Plain, single oilseeds in Western China, to single rice or double rice in Southern China. The key cropping patterns changed from double-grain cropping, single grain to single cash cropping with increasing altitudes. There were 151,744 km² planted areas of double grain cropping patterns in China, and multiple cropping accounted for 36.1 % of grain cultivated area nationally. Over 80 % of grain production was mainly implemented at lower altitudes as the Non-Grain Production (NGP) ratio enhanced from 32 % within elevations below 200 m to over 70 % among elevations above 700 m. Consistent datasets on complex cropping patterns are essential, given the significant roles of diversification and crop rotations in sustainable agriculture and the frequently observed inconsistencies in existing crop data products based on thematic mapping.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"224 ","pages":"Pages 361-381"},"PeriodicalIF":10.6,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143844515","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":"On the consistency and stability of vegetation biophysical variables retrievals from Landsat-8/9 and Sentinel-2","authors":"Najib Djamai ,&nbsp;Richard Fernandes ,&nbsp;Lixin Sun ,&nbsp;Gang Hong ,&nbsp;Luke A. Brown ,&nbsp;Harry Morris ,&nbsp;Jadu Dash","doi":"10.1016/j.isprsjprs.2025.04.006","DOIUrl":"10.1016/j.isprsjprs.2025.04.006","url":null,"abstract":"<div><div>Systematic decametric resolution global mapping of vegetation biophysical variables, including fraction of absorbed photosynthetically active radiation (fAPAR), fraction of vegetation cover (fCOVER), and leaf area index (LAI), is required to support various activities, including climate adaptation, crop management, biodiversity monitoring, and ecosystem assessments. The Canada Centre for Remote Sensing (CCRS) version of the Simplified Level 2 Prototype Processor (SL2P-CCRS) enables global mapping of these variables using freely available medium resolution multispectral satellite data from Sentinel-2 (S2) and Landsat-8/9 (LS) data. In this study, fiducial reference measurements (RMs) from the National Ecological Observatory Network (NEON) supplemented with regional measurements from CCRS were used to evaluate the consistency between SL2P-CCRS estimates of fAPAR, fCOVER and LAI from LS and S2 data and to quantify their temporal stability. SL2P-CCRS estimates of fCOVER (Accuracy (A) ∼ 0.03, Uncertainty (U) ∼ 0.13) and fAPAR (A ∼ −0.03, U ∼ 0.13) from LS and S2 were unbiased, and generally similar between sensors, based on 6569 LS-RMs and 4932 S2-RMs matchups. However, LAI estimates, especially for woody wetlands, deciduous forest, and mixed forest, were underestimated, with better estimates obtained using S2 (A ∼ −0.33, U ∼ 0.98) than LS (A ∼ −0.43, U ∼ 1.13). For all variables, SL2P-CCRS LS estimates were highly correlated to S2 estimates overall (R2 0.80 to 0.82) but up to 35 % lower for LAI over broadleaf and mixed forests and between lower 10 % and 20 % otherwise. The inter-annual stability of SL2P-CCRS estimates from both LS and S2 fell within the Global Climate Observing System (GCOS) requirements with the mean (standard deviation) values of −0.01 yr⁻¹ (0.06 yr⁻¹) for LS LAI, 0.02 yr⁻¹ (0.09 yr⁻¹) for S2 LAI, and 0 yr⁻¹ (0.01 yr⁻¹) for fCOVER and fAPAR from both LS and S2. The stability of both S2 and LS vegetation biophysical products indicate that are well suited for quantify the physical response of vegetation to climate variability, disturbances and regeneration.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"224 ","pages":"Pages 329-347"},"PeriodicalIF":10.6,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143839503","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}