Temporal-Spatial Soil Moisture Estimation from CYGNSS Using Machine Learning Regression With a Preclassification Approach

Global navigation satellite system-reflectometry (GNSS-R) can retrieve Earth's surface parameters, such as soil moisture (SM) using the reflected signals from GNSS constellations with advantages of noncontact, all-weather, real-time, and continuity, particularly the space-borne cyclone GN...

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Autores principales:	Yan Jia, Shuanggen Jin, Haolin Chen, Qingyun Yan, Patrizia Savi, Yan Jin, Yuan Yuan
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	CYGNSS GNSS-Reflectometry preclassifica- tion SMAP soil moisture XGBoost Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809
Acceso en línea:	https://doaj.org/article/2e642fcfcf954df1b4ebd31b03e81dcd
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spelling	oai:doaj.org-article:2e642fcfcf954df1b4ebd31b03e81dcd2021-11-19T00:00:11ZTemporal-Spatial Soil Moisture Estimation from CYGNSS Using Machine Learning Regression With a Preclassification Approach2151-153510.1109/JSTARS.2021.3076470https://doaj.org/article/2e642fcfcf954df1b4ebd31b03e81dcd2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9419719/https://doaj.org/toc/2151-1535Global navigation satellite system-reflectometry (GNSS-R) can retrieve Earth's surface parameters, such as soil moisture (SM) using the reflected signals from GNSS constellations with advantages of noncontact, all-weather, real-time, and continuity, particularly the space-borne cyclone GNSS (CYGNSS) mission. However, the accuracy and efficiency of SM estimation from CYGNSS still need to improve. In this article, the global SM is estimated using machine learning (ML) regression aided by a preclassification strategy. The total observations are classified by land types and corresponding subsets are built for constructing ML regression submodels. Ten-fold cross-validation technique is adopted. The overall performance of SM estimation with/without preclassification is compared, and the results show that the SM estimations using different ML algorithms all have substantial improvement with the preclassification strategy. Then, the optimal XGBoost predicted model with root-mean-square error (RMSE) of 0.052 cm<sup>3</sup>/cm<sup>3</sup> is adopted. In addition, the satisfactory daily and seasonal SM prediction outcomes with an overall correlation coefficient value of 0.86 and an RMSE value of 0.056 cm<sup>3</sup>/cm<sup>3</sup> are achieved at a global scale, respectively. Furthermore, the extensive temporal and spatial variations of CYGNSS SM predictions are evaluated. It shows that the reflectivity plays a main role among the predictors in SM estimation, and the next is vegetation. In some extremely dry places, the roughness may become more important. The value of SM is positively correlated with RMSE and also another limit condition that will constrain the variation of predictors, thus affecting correlation coefficient R and RMSE. Also, we compare both SMAP and CYGNSS SM predictions against <italic>in situ</italic> SM measurements from 301 stations. Similar low-median unbiased RMSEs are obtained, and the daily averaged CYGNSS-based SM against the <italic>in situ</italic> networks is 0.049 cm<sup>3</sup>/cm<sup>3</sup>. The presented approach succeeds in providing SM estimation at a global scale with employing the least ancillary data with superior results and this article reveals the spatio-temporal heterogeneity for SM estimation using CYGNSS data.Yan JiaShuanggen JinHaolin ChenQingyun YanPatrizia SaviYan JinYuan YuanIEEEarticleCYGNSSGNSS-Reflectometrypreclassifica- tionSMAPsoil moistureXGBoostOcean engineeringTC1501-1800Geophysics. Cosmic physicsQC801-809ENIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 4879-4893 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	CYGNSS GNSS-Reflectometry preclassifica- tion SMAP soil moisture XGBoost Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809
spellingShingle	CYGNSS GNSS-Reflectometry preclassifica- tion SMAP soil moisture XGBoost Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809 Yan Jia Shuanggen Jin Haolin Chen Qingyun Yan Patrizia Savi Yan Jin Yuan Yuan Temporal-Spatial Soil Moisture Estimation from CYGNSS Using Machine Learning Regression With a Preclassification Approach
description	Global navigation satellite system-reflectometry (GNSS-R) can retrieve Earth's surface parameters, such as soil moisture (SM) using the reflected signals from GNSS constellations with advantages of noncontact, all-weather, real-time, and continuity, particularly the space-borne cyclone GNSS (CYGNSS) mission. However, the accuracy and efficiency of SM estimation from CYGNSS still need to improve. In this article, the global SM is estimated using machine learning (ML) regression aided by a preclassification strategy. The total observations are classified by land types and corresponding subsets are built for constructing ML regression submodels. Ten-fold cross-validation technique is adopted. The overall performance of SM estimation with/without preclassification is compared, and the results show that the SM estimations using different ML algorithms all have substantial improvement with the preclassification strategy. Then, the optimal XGBoost predicted model with root-mean-square error (RMSE) of 0.052 cm<sup>3</sup>/cm<sup>3</sup> is adopted. In addition, the satisfactory daily and seasonal SM prediction outcomes with an overall correlation coefficient value of 0.86 and an RMSE value of 0.056 cm<sup>3</sup>/cm<sup>3</sup> are achieved at a global scale, respectively. Furthermore, the extensive temporal and spatial variations of CYGNSS SM predictions are evaluated. It shows that the reflectivity plays a main role among the predictors in SM estimation, and the next is vegetation. In some extremely dry places, the roughness may become more important. The value of SM is positively correlated with RMSE and also another limit condition that will constrain the variation of predictors, thus affecting correlation coefficient R and RMSE. Also, we compare both SMAP and CYGNSS SM predictions against <italic>in situ</italic> SM measurements from 301 stations. Similar low-median unbiased RMSEs are obtained, and the daily averaged CYGNSS-based SM against the <italic>in situ</italic> networks is 0.049 cm<sup>3</sup>/cm<sup>3</sup>. The presented approach succeeds in providing SM estimation at a global scale with employing the least ancillary data with superior results and this article reveals the spatio-temporal heterogeneity for SM estimation using CYGNSS data.
format	article
author	Yan Jia Shuanggen Jin Haolin Chen Qingyun Yan Patrizia Savi Yan Jin Yuan Yuan
author_facet	Yan Jia Shuanggen Jin Haolin Chen Qingyun Yan Patrizia Savi Yan Jin Yuan Yuan
author_sort	Yan Jia
title	Temporal-Spatial Soil Moisture Estimation from CYGNSS Using Machine Learning Regression With a Preclassification Approach
title_short	Temporal-Spatial Soil Moisture Estimation from CYGNSS Using Machine Learning Regression With a Preclassification Approach
title_full	Temporal-Spatial Soil Moisture Estimation from CYGNSS Using Machine Learning Regression With a Preclassification Approach
title_fullStr	Temporal-Spatial Soil Moisture Estimation from CYGNSS Using Machine Learning Regression With a Preclassification Approach
title_full_unstemmed	Temporal-Spatial Soil Moisture Estimation from CYGNSS Using Machine Learning Regression With a Preclassification Approach
title_sort	temporal-spatial soil moisture estimation from cygnss using machine learning regression with a preclassification approach
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/2e642fcfcf954df1b4ebd31b03e81dcd
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Temporal-Spatial Soil Moisture Estimation from CYGNSS Using Machine Learning Regression With a Preclassification Approach

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