Comparison of two satellite-based soil moisture reconstruction algorithms: A case study in the state of Oklahoma, USA

Liu, Yangxiaoyue; Yao, Ling; Jing, Wenlong; Di, Liping; Yang, Ji; Li, Yong

doi:10.1016/j.jhydrol.2020.125406

Cited by 19 publications

(22 citation statements)

References 69 publications

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“…This confirmed the previous findings in [55] that soil moisture was closely related to the MODIS NDVI value over the Tibetan Plateau, and the interaction between the soil moisture and vegetation was a bidirectional process. This is in line with the use of vegetation index in optical remote sensing for soil moisture retrieval and reconstruction [21,50,56,57].…”

Section: A Inputs and Performances Of Random Forest Algorithmsupporting

confidence: 81%

Spatial Gap-Filling of SMAP Soil Moisture Pixels Over Tibetan Plateau via Machine Learning Versus Geostatistics

Tong¹,

Wang²,

Magagi³

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Soil moisture is a key variable in ecology, environment, agriculture, and hydrology. The Soil Moisture Active Passive (SMAP) satellite provides global soil moisture products with reliable accuracy since 2015. However, significant gaps of SMAP soil moisture appeared over Tibetan Plateau. Considering the important role of the Tibetan Plateau in global climate and environment, it is essential to develop methods to infill the gaps to generate seamless SMAP soil moisture data. To address this issue, we proposed two methods, machine learning and geostatistics technique. For the machine learning technique, we train a Random Forest algorithm which aims to match the output of available SMAP L3 soil moisture using a series of input variables such as SMAP brightness temperature (TBH, TBV) in ascending orbits (6:00 PM local time), surface temperature, MODIS NDVI, land cover, DEM and other auxiliary data. Then, the established RF estimators were applied to the SMAP brightness temperature from descending orbits (6:00 AM local time) to reconstruct complete soil moisture data over the Tibetan Plateau. For the geostatistics technique, the Ordinary Kriging was applied to the available SMAP L3 soil moisture pixels to interpolate complete soil moisture data. To cross-validate the performances of the algorithms, we assume certain areas with available SMAP SM values as missing, and then compared the gap-filling results with the actual ones. The cross-validations show that the gap-filling results from two algorithms were highly correlated to the official SMAP SM products with high coefficients of determination (R 2 RF = 0.97, R 2 OK = 0.85) and low RMSE (RMSERF = 0.015 cm 3 /cm 3 , RMSEOK = 0.036 cm 3 /cm 3 ). Furthermore, the gap-filling soil moisture data present a better correlation with the SMOS soil moisture data (R = 0.55 ~ 0.7) than the GLDAS simulations (R = 0.18 ~ 0.62). The reconstructed soil moisture from RF (R = 0.71) and OK (R = 0.55) algorithms are well related to the Maqu network measurements. Thus, the machine learning and geostatistics algorithms have the potential to reproduce the missing SMAP soil moisture products over the Tibetan Plateau.

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Section: A Inputs and Performances Of Random Forest Algorithmsupporting

confidence: 81%

Spatial Gap-Filling of SMAP Soil Moisture Pixels Over Tibetan Plateau via Machine Learning Versus Geostatistics

Tong¹,

Wang²,

Magagi³

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Firstly, the precision of environmental data can directly affect SM estimation accuracy. These environmental data with the quality control could avoid inferior samples (Liu et al, 2020a). Secondly, representative samples can effectively reflect the spatial variability of SM in a given region.…”

Section: Dependence Of Models' Performance On the Quality Of Environm...mentioning

confidence: 99%

Reconstruction of ESA CCI satellite-derived soil moisture using an artificial neural network technology

Zhang

Liu

Ren

et al. 2021

Science of The Total Environment

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“…An SM data assimilation scheme is to simulate dynamic SM at spatiotemporal scales using estimated soil parameters and weather forcing based on a hydrological model [46,47]. The machine learning technique is computationally intensive (e.g., random forest (RF) [48,49], artificial neural network (ANN) [50][51][52][53], support vector regression (SVR) [54][55][56], and regression trees (RT) [57,58]) and used to build mathematical models based on training sets and covariates to extract SM information from the available data [59,60].…”

Section: Introductionmentioning

confidence: 99%

Modeling Soil Moisture from Multisource Data by Stepwise Multilinear Regression: An Application to the Chinese Loess Plateau

Yuan

Zhang

et al. 2021

IJGI

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This study aims to integrate multisource data to model the relative soil moisture (RSM) over the Chinese Loess Plateau in 2017 by stepwise multilinear regression (SMLR) in order to improve the spatial coverage of our previously published RSM. First, 34 candidate variables (12 quantitative and 22 dummy variables) from the Moderate Resolution Imaging Spectroradiometer (MODIS) and topographic, soil properties, and meteorological data were preprocessed. Then, SMLR was applied to variables without multicollinearity to select statistically significant (p-value < 0.05) variables. After the accuracy assessment, monthly, seasonal, and annual spatial patterns of RSM were mapped at 500 m resolution and evaluated. The results indicate that there was a high potential of SMLR to model RSM with the desired accuracy (best fit of the model with Pearson’s r = 0.969, root mean square error = 0.761%, and mean absolute error = 0.576%) over the Chinese Loess Plateau. The variables of elevation (0–500 m and 2000–2500 m), precipitation, soil texture of loam, and nighttime land surface temperature can continuously be used in the regression models for all seasons. Including dummy variables improved the model fit both in calibration and validation. Moreover, the SMLR-modeled RSM achieved better spatial coverage than that of the reference RSM for almost all periods. This is a significant finding as the SMLR method supports the use of multisource data to complement and/or replace coarse resolution satellite imagery in the estimation of RSM.

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Comparison of two satellite-based soil moisture reconstruction algorithms: A case study in the state of Oklahoma, USA

Cited by 19 publications

References 69 publications

Spatial Gap-Filling of SMAP Soil Moisture Pixels Over Tibetan Plateau via Machine Learning Versus Geostatistics

Spatial Gap-Filling of SMAP Soil Moisture Pixels Over Tibetan Plateau via Machine Learning Versus Geostatistics

Reconstruction of ESA CCI satellite-derived soil moisture using an artificial neural network technology

Modeling Soil Moisture from Multisource Data by Stepwise Multilinear Regression: An Application to the Chinese Loess Plateau

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