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Quick Detection of Field-Scale Soil Comprehensive Attributes via the Integration of UAV and Sentinel-2B Remote Sensing Data

文献类型：期刊论文

作者Zhu, Wanxue1,4,5,6; Rezaei, Ehsan Eyshi1; Nouri, Hamideh6; Yang, Ting3,7; Li, Binbin4; Gong, Huarui3,7; Lyu, Yun2; Peng, Jinbang4,5; Sun, Zhigang3,4,5,7
刊名REMOTE SENSING
出版日期2021-11-01
卷号13期号:22页码:19
关键词unmanned aerial vehicle satellite remote sensing soil quality multispectral Sentinel-2B
DOI10.3390/rs13224716
通讯作者Sun, Zhigang(sun.zhigang@igsnrr.ac.cn)
英文摘要Satellite and unmanned aerial vehicle (UAV) remote sensing can be used to estimate soil properties; however, little is known regarding the effects of UAV and satellite remote sensing data integration on the estimation of soil comprehensive attributes, or how to estimate quickly and robustly. In this study, we tackled those gaps by employing UAV multispectral and Sentinel-2B data to estimate soil salinity and chemical properties over a large agricultural farm (400 ha) covered by different crops and harvest areas at the coastal saline-alkali land of the Yellow River Delta of China in 2019. Spatial information of soil salinity, organic matter, available/total nitrogen content, and pH at 0-10 cm and 10-20 cm layers were obtained via ground sampling (n = 195) and two-dimensional spatial interpolation, aiming to overlap the soil information with remote sensing information. The exploratory factor analysis was conducted to generate latent variables, which represented the salinity and chemical characteristics of the soil. A machine learning algorithm (random forest) was applied to estimate soil attributes. Our results indicated that the integration of UAV texture and Sentinel-2B spectral data as random forest model inputs improved the accuracy of latent soil variable estimation. The remote sensing-based information from cropland (crop-based) had a higher accuracy compared to estimations performed on bare soil (soil-based). Therefore, the crop-based approach, along with the integration of UAV texture and Sentinel-2B data, is recommended for the quick assessment of soil comprehensive attributes.
WOS关键词YELLOW-RIVER DELTA ; LEAF CHLOROPHYLL ; AREA INDEX ; SPATIAL-DISTRIBUTION ; VEGETATION INDEXES ; SPECTRAL INDEX ; SALINITY ; GROWTH ; WHEAT ; COMBINATION
资助项目Strategic Priority Research Program of the Chinese Academy of Sciences[XDA23050102] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA19040303] ; Key Projects of the Chinese Academy of Sciences[KJZD-SW-113] ; National Natural Science Foundation of China[31870421] ; National Natural Science Foundation of China[41771388] ; Program of Yellow River Delta Scholars (2020-2024)
WOS研究方向Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
出版者MDPI
WOS记录号WOS:000726988800001
资助机构Strategic Priority Research Program of the Chinese Academy of Sciences ; Key Projects of the Chinese Academy of Sciences ; National Natural Science Foundation of China ; Program of Yellow River Delta Scholars (2020-2024)
源URL[http://ir.igsnrr.ac.cn/handle/311030/168266]  
专题中国科学院地理科学与资源研究所
通讯作者Sun, Zhigang
作者单位1.Leibniz Ctr Agr Landscape Res ZALF, D-15374 Muncheberg, Germany
2.China Agr Univ, Dept Grassland Sci, Coll Grassland Sci & Technol, Beijing 100193, Peoples R China
3.Univ Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, CAS, Beijing 100101, Peoples R China
4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Beijing 100101, Peoples R China
5.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China
6.Univ Gottingen, Dept Crop Sci, D-37075 Gottingen, Germany
7.Shandong Dongying Inst Geog Sci, Dongying 257000, Peoples R China
推荐引用方式
GB/T 7714		 Zhu, Wanxue,Rezaei, Ehsan Eyshi,Nouri, Hamideh,et al. Quick Detection of Field-Scale Soil Comprehensive Attributes via the Integration of UAV and Sentinel-2B Remote Sensing Data[J]. REMOTE SENSING,2021,13(22):19.
APA Zhu, Wanxue.,Rezaei, Ehsan Eyshi.,Nouri, Hamideh.,Yang, Ting.,Li, Binbin.,...&Sun, Zhigang.(2021).Quick Detection of Field-Scale Soil Comprehensive Attributes via the Integration of UAV and Sentinel-2B Remote Sensing Data.REMOTE SENSING,13(22),19.
MLA Zhu, Wanxue,et al."Quick Detection of Field-Scale Soil Comprehensive Attributes via the Integration of UAV and Sentinel-2B Remote Sensing Data".REMOTE SENSING 13.22(2021):19.

入库方式： OAI收割

来源：地理科学与资源研究所

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