Pub Date : 2023-12-06DOI: 10.1080/15481603.2023.2290337
Yulin Shangguan, Xiaoxiao Min, Nan Wang, Cheng Tong, Zhou Shi
Long-term, high-resolution soil moisture (SM) is a vital variable for understanding the water-energy cycle and the impacts of climate change on the Qinghai-Tibet Plateau (QTP). However, most existi...
长期、高分辨率土壤湿度是了解青藏高原水能循环和气候变化影响的重要变量。然而，大多数存在……
{"title":"A long-term, high-accuracy and seamless 1km soil moisture dataset over the Qinghai-Tibet Plateau during 2001–2020 based on a two-step downscaling method","authors":"Yulin Shangguan, Xiaoxiao Min, Nan Wang, Cheng Tong, Zhou Shi","doi":"10.1080/15481603.2023.2290337","DOIUrl":"https://doi.org/10.1080/15481603.2023.2290337","url":null,"abstract":"Long-term, high-resolution soil moisture (SM) is a vital variable for understanding the water-energy cycle and the impacts of climate change on the Qinghai-Tibet Plateau (QTP). However, most existi...","PeriodicalId":55091,"journal":{"name":"GIScience & Remote Sensing","volume":"25 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2023-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138544858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Despite the importance of the Amazonian rainforests in the global carbon cycle, their phenological responses measured by large field-of-view satellite sensors are still not completely understood. I...
尽管亚马逊雨林在全球碳循环中的重要性，但它们的物候响应仍未完全被大型视场卫星传感器所测量。我…
{"title":"Coupled effects of solar illumination and phenology on vegetation index determination: an analysis over the Amazonian forests using the SuperDove satellite constellation","authors":"Lênio Soares Galvão, Caio Arlanche Petri, Ricardo Dalagnol","doi":"10.1080/15481603.2023.2290354","DOIUrl":"https://doi.org/10.1080/15481603.2023.2290354","url":null,"abstract":"Despite the importance of the Amazonian rainforests in the global carbon cycle, their phenological responses measured by large field-of-view satellite sensors are still not completely understood. I...","PeriodicalId":55091,"journal":{"name":"GIScience & Remote Sensing","volume":"11 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2023-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138544888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-27DOI: 10.1080/15481603.2023.2286746
Han Liu, Tongkui Liao, Yu Wang, Xiaoming Qian, Xiaochen Liu, Chengming Li, Shiwei Li, Zhanlei Guan, Lijue Zhu, Xiaoyuan Zhou, Chong Liu, Tengyun Hu, Ming Luo
Timely and accurate wetland information is necessary for wetland resource management. Recent advances in machine learning and remote sensing have facilitated cost-effective monitoring of wetlands. ...
{"title":"Fine-grained wetland classification for national wetland reserves using multi-source remote sensing data and Pixel Information Expert Engine (PIE-Engine)","authors":"Han Liu, Tongkui Liao, Yu Wang, Xiaoming Qian, Xiaochen Liu, Chengming Li, Shiwei Li, Zhanlei Guan, Lijue Zhu, Xiaoyuan Zhou, Chong Liu, Tengyun Hu, Ming Luo","doi":"10.1080/15481603.2023.2286746","DOIUrl":"https://doi.org/10.1080/15481603.2023.2286746","url":null,"abstract":"Timely and accurate wetland information is necessary for wetland resource management. Recent advances in machine learning and remote sensing have facilitated cost-effective monitoring of wetlands. ...","PeriodicalId":55091,"journal":{"name":"GIScience & Remote Sensing","volume":" 4","pages":""},"PeriodicalIF":6.7,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138473570","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-16DOI: 10.1080/15481603.2023.2281142
Lijun Wang, Yang Bai, Jiayao Wang, Zheng Zhou, Fen Qin, Jiyuan Hu
Accurate and near-real-time crop mapping from satellite imagery is crucial for agricultural monitoring. However, the seasonal nature of crops makes it challenging to rely on traditional machine lea...
{"title":"Histogram matching-based semantic segmentation model for crop classification with Sentinel-2 satellite imagery","authors":"Lijun Wang, Yang Bai, Jiayao Wang, Zheng Zhou, Fen Qin, Jiyuan Hu","doi":"10.1080/15481603.2023.2281142","DOIUrl":"https://doi.org/10.1080/15481603.2023.2281142","url":null,"abstract":"Accurate and near-real-time crop mapping from satellite imagery is crucial for agricultural monitoring. However, the seasonal nature of crops makes it challenging to rely on traditional machine lea...","PeriodicalId":55091,"journal":{"name":"GIScience & Remote Sensing","volume":"141 33","pages":""},"PeriodicalIF":6.7,"publicationDate":"2023-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138085625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-16DOI: 10.1080/15481603.2023.2279802
Chanyang Sur, Won-Ho Nam, Xiang Zhang, T. Tadesse, B. Wardlow
ABSTRACT In this study, Eco-hydrometeorological Remote Sensing-based Penman-Monteith algorithm (Eh-RSPM) was developed by implementing the gross primary productivity into the revised Remote Sensing based Penman-Monteith algorithm (RS-PM). Evaluation of Eh-RSPM was conducted through comparison with in-situ measurements as well as model-based products (e.g. MODerate resolution Imaging Spectroradiometer (MODIS) 16 global ET products (MOD16 ET) and Surface Energy Balance System (SEBS)) during two years (2004 and 2012) in Northeast Asia. Comparison of ET from Eh-RSPM algorithm with five flux tower measurement agreed well with the flux tower datasets at the entire validation sites. Especially, Eh-RSPM showed advantages in improving the accuracy of ET at stations with relatively short canopy height (e.g. QHB and KBU site) as well as the forest site (e.g. SMK). Focusing on the forest site, Eh-RSPM exhibited slightly better statistical performance compared to MOD16. Specifically, the temporal mean bias and RMSD showed a slight improvement, decreasing from −15.40 W m−2 to −12.58 W m−2 and from 28.41 W m−2 to 25.26 W m−2, respectively. This is a key finding of this study, demonstrating the applicability of the improved ET algorithm to regions with significant forest cover. Similarly, spatial distribution of Eh-RSPM showed similar patterns with MOD16 and SEBS. Eh-RSPM strongly showed advantages over the land cover types with relatively shorter canopy height (e.g. grassland and alpine meadow) as well as the heterogeneous forest showed significant improvement in Eh-RSPM through considering the actual physiological behavior variation and influence of photosynthesis into ET calculation.
{"title":"Assessment of an evapotranspiration algorithm accounting for land cover types and photosynthetic perspectives using remote sensing images","authors":"Chanyang Sur, Won-Ho Nam, Xiang Zhang, T. Tadesse, B. Wardlow","doi":"10.1080/15481603.2023.2279802","DOIUrl":"https://doi.org/10.1080/15481603.2023.2279802","url":null,"abstract":"ABSTRACT In this study, Eco-hydrometeorological Remote Sensing-based Penman-Monteith algorithm (Eh-RSPM) was developed by implementing the gross primary productivity into the revised Remote Sensing based Penman-Monteith algorithm (RS-PM). Evaluation of Eh-RSPM was conducted through comparison with in-situ measurements as well as model-based products (e.g. MODerate resolution Imaging Spectroradiometer (MODIS) 16 global ET products (MOD16 ET) and Surface Energy Balance System (SEBS)) during two years (2004 and 2012) in Northeast Asia. Comparison of ET from Eh-RSPM algorithm with five flux tower measurement agreed well with the flux tower datasets at the entire validation sites. Especially, Eh-RSPM showed advantages in improving the accuracy of ET at stations with relatively short canopy height (e.g. QHB and KBU site) as well as the forest site (e.g. SMK). Focusing on the forest site, Eh-RSPM exhibited slightly better statistical performance compared to MOD16. Specifically, the temporal mean bias and RMSD showed a slight improvement, decreasing from −15.40 W m−2 to −12.58 W m−2 and from 28.41 W m−2 to 25.26 W m−2, respectively. This is a key finding of this study, demonstrating the applicability of the improved ET algorithm to regions with significant forest cover. Similarly, spatial distribution of Eh-RSPM showed similar patterns with MOD16 and SEBS. Eh-RSPM strongly showed advantages over the land cover types with relatively shorter canopy height (e.g. grassland and alpine meadow) as well as the heterogeneous forest showed significant improvement in Eh-RSPM through considering the actual physiological behavior variation and influence of photosynthesis into ET calculation.","PeriodicalId":55091,"journal":{"name":"GIScience & Remote Sensing","volume":"58 2","pages":""},"PeriodicalIF":6.7,"publicationDate":"2023-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139268840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As the most commonly used driven data for gross primary productivity (GPP) estimation, satellite remote sensing vegetation indexes (VI), such as the leaf area index (LAI), often seriously suffer from data quality problems induced by cloud contamination and noise. Although various filtering methods are applied to reconstruct the missing data and eliminate noises in the VI time series, the impacts of these data quality problems on GPP estimation are still not clear. In this study, the accuracy differences of the GPP estimations driven by different VI series are comprehensively analyzed based on two light use efficiency (LUE) models (the big-leaf MOD17 and the two-leaf RTL-LUE). Four VI filtering methods are applied for comparison, and GPP data across 169 eddy covariance (EC) sites are used for validation. The results demonstrate that all the filtering methods can improve the GPP simulation accuracy, and the SeasonL1 filtering method exhibits the best performance both for the MOD17 model (∆R2 = 0.06) and the RTL-LUE model (∆R2 = 0.07). The reconstruction of the key change points in the temporally continuous gaps may be the primary reason for the different performance of the four methods. Moreover, the effects of filtering processes on GPP estimation vary with latitudes and seasons due to the differences in the primary data quality. More significant improvements can be observed during the growing season and in the regions near the equator, where the data quality is relatively poor with lower primary GPP estimation accuracy. This study can guide the preprocessing of the VI data before GPP estimation.
卫星遥感植被指数(VI)，如叶面积指数(LAI)，作为估算总初级生产力(GPP)最常用的驱动数据，往往受到云污染和噪声的影响，导致数据质量问题严重。尽管使用了各种滤波方法来重建VI时间序列中的缺失数据和消除噪声，但这些数据质量问题对GPP估计的影响仍然不清楚。本文基于大叶MOD17和两叶RTL-LUE两种光利用效率(light use efficiency, LUE)模型，综合分析了不同VI序列驱动下的GPP估算精度差异。采用四种VI滤波方法进行比较，并使用169个涡动相关(EC)站点的GPP数据进行验证。结果表明，所有滤波方法均能提高GPP模拟精度，其中SeasonL1滤波方法对MOD17模型(∆R2 = 0.06)和RTL-LUE模型(∆R2 = 0.07)均表现出最好的滤波效果。在时间连续间隙中关键变化点的重建可能是导致四种方法性能不同的主要原因。此外，由于原始数据质量的差异，滤波过程对GPP估算的影响随纬度和季节而变化。在生长季节和赤道附近地区可以观察到更显著的改善，那里的数据质量相对较差，初级GPP估计精度较低。该研究可以指导GPP估计前VI数据的预处理。
{"title":"Impacts of the data quality of remote sensing vegetation index on gross primary productivity estimation","authors":"Yinghao Sun, Dan Peng, Xiaobin Guan, Dong Chu, Yongming Ma, Huanfeng Shen","doi":"10.1080/15481603.2023.2275421","DOIUrl":"https://doi.org/10.1080/15481603.2023.2275421","url":null,"abstract":"As the most commonly used driven data for gross primary productivity (GPP) estimation, satellite remote sensing vegetation indexes (VI), such as the leaf area index (LAI), often seriously suffer from data quality problems induced by cloud contamination and noise. Although various filtering methods are applied to reconstruct the missing data and eliminate noises in the VI time series, the impacts of these data quality problems on GPP estimation are still not clear. In this study, the accuracy differences of the GPP estimations driven by different VI series are comprehensively analyzed based on two light use efficiency (LUE) models (the big-leaf MOD17 and the two-leaf RTL-LUE). Four VI filtering methods are applied for comparison, and GPP data across 169 eddy covariance (EC) sites are used for validation. The results demonstrate that all the filtering methods can improve the GPP simulation accuracy, and the SeasonL1 filtering method exhibits the best performance both for the MOD17 model (∆R2 = 0.06) and the RTL-LUE model (∆R2 = 0.07). The reconstruction of the key change points in the temporally continuous gaps may be the primary reason for the different performance of the four methods. Moreover, the effects of filtering processes on GPP estimation vary with latitudes and seasons due to the differences in the primary data quality. More significant improvements can be observed during the growing season and in the regions near the equator, where the data quality is relatively poor with lower primary GPP estimation accuracy. This study can guide the preprocessing of the VI data before GPP estimation.","PeriodicalId":55091,"journal":{"name":"GIScience & Remote Sensing","volume":"123 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135138478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-10DOI: 10.1080/15481603.2023.2271246
Ye Ma, Zhen Zhen, Fengri Li, Fujuan Feng, Yinghui Zhao
Large-scale forest composition mapping and change monitoring are essential for regional and national forest resource management, monitoring, and carbon stock assessment. However, the existing large...
{"title":"An innovative lightweight 1D-CNN model for efficient monitoring of large-scale forest composition: a case study of Heilongjiang Province, China","authors":"Ye Ma, Zhen Zhen, Fengri Li, Fujuan Feng, Yinghui Zhao","doi":"10.1080/15481603.2023.2271246","DOIUrl":"https://doi.org/10.1080/15481603.2023.2271246","url":null,"abstract":"Large-scale forest composition mapping and change monitoring are essential for regional and national forest resource management, monitoring, and carbon stock assessment. However, the existing large...","PeriodicalId":55091,"journal":{"name":"GIScience & Remote Sensing","volume":"42 9","pages":""},"PeriodicalIF":6.7,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72365799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-09DOI: 10.1080/15481603.2023.2275424
Xiaohan Zhang, Wei Han, Jun Li, Lizhe Wang
Accurate bathymetric information is an important foundation for marine resource development and nearshore ecological protection. Existing empirical algorithms can estimate water depth from high res...
准确的水深信息是海洋资源开发和近岸生态保护的重要基础。现有的经验算法可以从高分辨率估计水深。。。
{"title":"Nearshore bathymetry estimation through dual-time phase satellite imagery in the absence of in-situ data","authors":"Xiaohan Zhang, Wei Han, Jun Li, Lizhe Wang","doi":"10.1080/15481603.2023.2275424","DOIUrl":"https://doi.org/10.1080/15481603.2023.2275424","url":null,"abstract":"Accurate bathymetric information is an important foundation for marine resource development and nearshore ecological protection. Existing empirical algorithms can estimate water depth from high res...","PeriodicalId":55091,"journal":{"name":"GIScience & Remote Sensing","volume":"64 14","pages":""},"PeriodicalIF":6.7,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71524933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-30DOI: 10.1080/15481603.2023.2275427
Baode Jiang, Shaofen Xu, Zhiwei Li
Polyline simplification is crucial for cartography and spatial database management. In recent decades, various rule-based algorithms for vector polyline simplification have been proposed. However, ...
{"title":"Polyline simplification using a region proposal network integrating raster and vector features","authors":"Baode Jiang, Shaofen Xu, Zhiwei Li","doi":"10.1080/15481603.2023.2275427","DOIUrl":"https://doi.org/10.1080/15481603.2023.2275427","url":null,"abstract":"Polyline simplification is crucial for cartography and spatial database management. In recent decades, various rule-based algorithms for vector polyline simplification have been proposed. However, ...","PeriodicalId":55091,"journal":{"name":"GIScience & Remote Sensing","volume":"66 4","pages":""},"PeriodicalIF":6.7,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71507431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-26DOI: 10.1080/15481603.2023.2270814
Binbin Fan, Geping Luo, Olaf Hellwich, Xuguo Shi, Friday U. Ochege
The Mountain-Oasis-Desert System (MODS) is the fundamental landscape component within the vast arid region of Central Asia. Human activities and natural processes cause surface displacement in the ...
山地绿洲沙漠系统（MODS）是中亚广大干旱地区的基本景观组成部分。人类活动和自然过程导致了。。。
{"title":"Surface deformation detection and attribution in the Mountain-Oasis-Desert Landscape in north Tianshan Mountains","authors":"Binbin Fan, Geping Luo, Olaf Hellwich, Xuguo Shi, Friday U. Ochege","doi":"10.1080/15481603.2023.2270814","DOIUrl":"https://doi.org/10.1080/15481603.2023.2270814","url":null,"abstract":"The Mountain-Oasis-Desert System (MODS) is the fundamental landscape component within the vast arid region of Central Asia. Human activities and natural processes cause surface displacement in the ...","PeriodicalId":55091,"journal":{"name":"GIScience & Remote Sensing","volume":"51 15","pages":""},"PeriodicalIF":6.7,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71514439","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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