干旱区生态环境知识资源中心(Arid): A Global Meta-Analysis of Soil Salinity Prediction Integrating Satellite Remote Sensing, Soil Sampling, and Machine Learning

Arid

DOI	10.1109/TGRS.2021.3109819
	A Global Meta-Analysis of Soil Salinity Prediction Integrating Satellite Remote Sensing, Soil Sampling, and Machine Learning
	Shi, Haiyang; Hellwich, Olaf; Luo, Geping; Chen, Chunbo; He, Huili; Ochege, Friday Uchenna; Van de Voorde, Tim; Kurban, Alishir; de Maeyer, Philippe
通讯作者	Luo, GP (corresponding author), Chinese Acad Sci, Xinjiang Inst Ecol & Geog, State Key Lab Desert & Oasis Ecol, Beijing 100049, Peoples R China.
来源期刊	IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
ISSN	0196-2892
EISSN	1558-0644
出版年	2022
英文摘要	Despite the growing interest among researchers, satellite-based prediction of soil salinity remains highly uncertain. The improvements in prediction accuracy reported in previous studies are usually limited to a single area. We performed a meta-analysis of regional satellite-based soil salinity predictions combined with in situ soil sampling and machine learning. Based on R-2 and root-mean-square error (RMSE) collected, we evaluated the effects of various features on the model accuracy and established a Bayesian network to evaluate the joint causal effect of multifeatures. Most significant differences were found in soil sampling schemes and characteristics of the study area, including the mean and variability (averaged R-2 of 0.75 for soil sample sets with lower salinity variation and 0.62 for others) of the salinity, climate type (R-2 of 0.64 in arid areas and 0.74 in others), soil texture (R-2 of 0.66 in sandy areas and 0.57 in others), and the interval between sampling date and satellite data acquisition date (R-2 of 0.53 under the condition of over 15 days and 0.65 in others). Generally, using different satellite data has limited effects on model performance among which Sentinel-2 performed better (R-2 = 0.72) than Landsat (R-2 = 0.66). The sampling of subsamples for each sample should focus on their subpixel-scale spatial heterogeneity across satellite data rather than the number of subsamples. It is also necessary to select appropriate vegetation and salinity indices for different satellite data under different vegetation conditions. Among algorithms, random forests (R-2 = 0.70) and support vector machines (R-2 = 0.71) performed best.
英文关键词	Salinity (geophysical) Soil Vegetation mapping Satellites Predictive models Data models Geography Hyperspectral machine learning multispectral remote sensing satellite soil salinity
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000732758800001
WOS关键词	SENTINEL-1 ; IMPACT ; CHINA ; MODEL ; LAKE
WOS类目	Geochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/374584
作者单位	[Shi, Haiyang; Luo, Geping; He, Huili] Chinese Acad Sci, Xinjiang Inst Ecol & Geog, State Key Lab Desert & Oasis Ecol, Beijing 100049, Peoples R China; [Shi, Haiyang; Luo, Geping; He, Huili; de Maeyer, Philippe] Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China; [Shi, Haiyang; He, Huili; de Maeyer, Philippe] Univ Ghent, Sino Belgian Joint Lab Geoinformat, B-9000 Ghent, Belgium; [Shi, Haiyang; He, Huili; Van de Voorde, Tim; de Maeyer, Philippe] Univ Ghent, Dept Geog, B-9000 Ghent, Belgium; [Hellwich, Olaf] Tech Univ Berlin, Dept Comp Vis & Remote Sensing, D-10623 Berlin, Germany; [Chen, Chunbo; Ochege, Friday Uchenna; Kurban, Alishir; de Maeyer, Philippe] Chinese Acad Sci, Xinjiang Inst Ecol & Geog, Beijing 100049, Peoples R China
推荐引用方式 GB/T 7714	Shi, Haiyang,Hellwich, Olaf,Luo, Geping,et al. A Global Meta-Analysis of Soil Salinity Prediction Integrating Satellite Remote Sensing, Soil Sampling, and Machine Learning[J],2022.
APA	Shi, Haiyang.,Hellwich, Olaf.,Luo, Geping.,Chen, Chunbo.,He, Huili.,...&de Maeyer, Philippe.(2022).A Global Meta-Analysis of Soil Salinity Prediction Integrating Satellite Remote Sensing, Soil Sampling, and Machine Learning.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING.
MLA	Shi, Haiyang,et al."A Global Meta-Analysis of Soil Salinity Prediction Integrating Satellite Remote Sensing, Soil Sampling, and Machine Learning".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2022).