气候变化领域集成服务门户(CCPortal): Evaluation of SMAP-Enhanced Products Using Upscaled Soil Moisture Data Based on Random Forest Regression: A Case Study of the Qinghai-Tibet Plateau, China

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DOI	10.3390/ijgi12070281
	Evaluation of SMAP-Enhanced Products Using Upscaled Soil Moisture Data Based on Random Forest Regression: A Case Study of the Qinghai-Tibet Plateau, China
	Chen, Jia; Hu, Fengmin; Li, Junjie; Xie, Yijia; Zhang, Wen; Huang, Changqing; Meng, Lingkui
发表日期	2023
EISSN	2220-9964
卷号	12 期号:7
英文摘要	The evaluation of satellite soil moisture is a big challenge owing to the large spatial mismatch between pixel-based satellite soil moisture products and point-based in situ measurements. Upscaling in situ measurements to obtain the true value of soil moisture content at the satellite grid/footprint scale can make up for the scale difference and improve the validation. Many existing upscaling methods have strict requirements regarding the spatial distribution and quantity of soil moisture sensors. However, in reality, soil-moisture-monitoring networks are commonly sparse with low sensor density, which increases the difficulty of obtaining accurate upscaled soil moisture data and limits the validation of satellite products. For this reason, this paper proposes a scheme to upscale in situ measurements using five machine learning methods along with Landsat 8 datasets and DEM data to validate the accuracy of a SMAP-enhanced passive soil moisture product for a sparse network on the Qinghai-Tibet Plateau. The proposed scheme realizes the upscaling of in situ soil moisture data to the pixel scale (30 m x 30 m) and then to the coarse grid scale (9 km x 9 km) by using multi-source remote sensing data as the bridge of scale conversion. The long-time SMAP SM products since April 2015 on the Qinghai-Tibet Plateau were validated based on upscaled soil moisture data. The results show that (1) random forest regression performs the best, and the upscaled soil moisture data reflect the region-average soil moisture conditions that can be used for evaluating SMAP data; (2) the SMAP product meets its scientific measurement requirements; and (3) the SMAP product generally underestimates the soil moisture in the study area.
关键词	soil moisture SMAP evaluation sparse ground-based sites upscaling random forest regression
英文关键词	RETRIEVALS; SATELLITE; VALIDATION; NETWORKS; SMOS
WOS研究方向	Computer Science, Information Systems ; Geography, Physical ; Remote Sensing
WOS记录号	WOS:001035961900001
来源期刊	ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/283190
作者单位	Wuhan University; North China University of Water Resources & Electric Power
推荐引用方式 GB/T 7714	Chen, Jia,Hu, Fengmin,Li, Junjie,et al. Evaluation of SMAP-Enhanced Products Using Upscaled Soil Moisture Data Based on Random Forest Regression: A Case Study of the Qinghai-Tibet Plateau, China[J],2023,12(7).
APA	Chen, Jia.,Hu, Fengmin.,Li, Junjie.,Xie, Yijia.,Zhang, Wen.,...&Meng, Lingkui.(2023).Evaluation of SMAP-Enhanced Products Using Upscaled Soil Moisture Data Based on Random Forest Regression: A Case Study of the Qinghai-Tibet Plateau, China.ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION,12(7).
MLA	Chen, Jia,et al."Evaluation of SMAP-Enhanced Products Using Upscaled Soil Moisture Data Based on Random Forest Regression: A Case Study of the Qinghai-Tibet Plateau, China".ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 12.7(2023).