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GIScience & Remote Sensing最新文献

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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 基于两步降尺度法的青藏高原1km长期高精度无缝土壤湿度数据集
IF 6.7 2区 地球科学
GIScience & Remote Sensing Pub Date : 2023-12-06 DOI: 10.1080/15481603.2023.2290337
Yulin Shangguan, Xiaoxiao Min, Nan Wang, Cheng Tong, Zhou Shi
{"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}
引用次数: 0
Coupled effects of solar illumination and phenology on vegetation index determination: an analysis over the Amazonian forests using the SuperDove satellite constellation 太阳光照和物候对植被指数测定的耦合影响:利用SuperDove卫星星座对亚马逊森林的分析
IF 6.7 2区 地球科学
GIScience & Remote Sensing Pub Date : 2023-12-06 DOI: 10.1080/15481603.2023.2290354
Lênio Soares Galvão, Caio Arlanche Petri, Ricardo Dalagnol
{"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}
引用次数: 0
Fine-grained wetland classification for national wetland reserves using multi-source remote sensing data and Pixel Information Expert Engine (PIE-Engine) 基于多源遥感数据和像元信息专家引擎(PIE-Engine)的国家湿地保护区细粒度湿地分类
IF 6.7 2区 地球科学
GIScience & Remote Sensing Pub Date : 2023-11-27 DOI: 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
{"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}
引用次数: 0
Histogram matching-based semantic segmentation model for crop classification with Sentinel-2 satellite imagery 基于直方图匹配的Sentinel-2卫星作物分类语义分割模型
IF 6.7 2区 地球科学
GIScience & Remote Sensing Pub Date : 2023-11-16 DOI: 10.1080/15481603.2023.2281142
Lijun Wang, Yang Bai, Jiayao Wang, Zheng Zhou, Fen Qin, Jiyuan Hu
{"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}
引用次数: 0
Assessment of an evapotranspiration algorithm accounting for land cover types and photosynthetic perspectives using remote sensing images 利用遥感图像评估考虑土地覆被类型和光合作用视角的蒸散算法
IF 6.7 2区 地球科学
GIScience & Remote Sensing Pub Date : 2023-11-16 DOI: 10.1080/15481603.2023.2279802
Chanyang Sur, Won-Ho Nam, Xiang Zhang, T. Tadesse, B. Wardlow
{"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}
引用次数: 0
Impacts of the data quality of remote sensing vegetation index on gross primary productivity estimation 遥感植被指数数据质量对总初级生产力估算的影响
2区 地球科学
GIScience & Remote Sensing Pub Date : 2023-11-10 DOI: 10.1080/15481603.2023.2275421
Yinghao Sun, Dan Peng, Xiaobin Guan, Dong Chu, Yongming Ma, Huanfeng Shen
{"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}
引用次数: 0
An innovative lightweight 1D-CNN model for efficient monitoring of large-scale forest composition: a case study of Heilongjiang Province, China 用于大规模森林成分有效监测的创新型轻量级1D-CNN模型——以黑龙江省为例
IF 6.7 2区 地球科学
GIScience & Remote Sensing Pub Date : 2023-11-10 DOI: 10.1080/15481603.2023.2271246
Ye Ma, Zhen Zhen, Fengri Li, Fujuan Feng, Yinghui Zhao
{"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}
引用次数: 0
Nearshore bathymetry estimation through dual-time phase satellite imagery in the absence of in-situ data 在缺乏现场数据的情况下，通过双时相卫星图像进行近岸水深测量估计
IF 6.7 2区 地球科学
GIScience & Remote Sensing Pub Date : 2023-11-09 DOI: 10.1080/15481603.2023.2275424
Xiaohan Zhang, Wei Han, Jun Li, Lizhe Wang
{"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}
引用次数: 0
Polyline simplification using a region proposal network integrating raster and vector features 使用集成光栅和矢量特征的区域建议网络简化多段线
IF 6.7 2区 地球科学
GIScience & Remote Sensing Pub Date : 2023-10-30 DOI: 10.1080/15481603.2023.2275427
Baode Jiang, Shaofen Xu, Zhiwei Li
{"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}
引用次数: 0
Surface deformation detection and attribution in the Mountain-Oasis-Desert Landscape in north Tianshan Mountains 北天山山地绿洲沙漠景观地表变形检测与归因
IF 6.7 2区 地球科学
GIScience & Remote Sensing Pub Date : 2023-10-26 DOI: 10.1080/15481603.2023.2270814
Binbin Fan, Geping Luo, Olaf Hellwich, Xuguo Shi, Friday U. Ochege
{"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}
引用次数: 0
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