2021
DOI: 10.3390/rs13234893
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In Situ Observation-Constrained Global Surface Soil Moisture Using Random Forest Model

Abstract: The inherent biases of different long-term gridded surface soil moisture (SSM) products, unconstrained by the in situ observations, implies different spatio-temporal patterns. In this study, the Random Forest (RF) model was trained to predict SSM from relevant land surface feature variables (i.e., land surface temperature, vegetation indices, soil texture, and geographical information) and precipitation, based on the in situ soil moisture data of the International Soil Moisture Network (ISMN.). The results of … Show more

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Cited by 23 publications
(16 citation statements)
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“…The other studies which outperformed ours used alternative model calibration and validation strategies ( Table S1 ). For example, random division of calibration and validation datasets was adopted by studies that reported more promising model fits (R 2 > 0.8) ( Liu et al, 2020 ; Greifeneder, Notarnicola & Wagner, 2021 ; Zhang et al, 2021 ). It should be noted that validation against randomly selected data points, even if stratified by study years, can generate much better model fits compared to independent sites because of the use of autocorrelated time-series data for both model training and validation ( Meyer et al, 2018 ; Ploton et al, 2020 ).…”
Section: Discussionmentioning
confidence: 99%
“…The other studies which outperformed ours used alternative model calibration and validation strategies ( Table S1 ). For example, random division of calibration and validation datasets was adopted by studies that reported more promising model fits (R 2 > 0.8) ( Liu et al, 2020 ; Greifeneder, Notarnicola & Wagner, 2021 ; Zhang et al, 2021 ). It should be noted that validation against randomly selected data points, even if stratified by study years, can generate much better model fits compared to independent sites because of the use of autocorrelated time-series data for both model training and validation ( Meyer et al, 2018 ; Ploton et al, 2020 ).…”
Section: Discussionmentioning
confidence: 99%
“…Surface soil moisture (SSM) is a source of water for the atmosphere through processes leading to evapotranspiration from land 1 – 3 . SSM has impacts on climate processes by influencing the partitioning of the incoming energy in the latent and sensible heat fluxes and controlling the partitioning of precipitation into runoff, evapotranspiration, and infiltration 2 , 3 . Therefore, a global high resolution, long-term, and spatiotemporally consistent SSM dataset is necessary for understanding the processes between the land surface and atmosphere, and is useful for numerous applications, e.g.…”
Section: Background and Summarymentioning
confidence: 99%
“…Although SSM has such high importance from many perspectives, there is still a paucity of global-scale long-term high resolution SSM datasets with acceptable precision and accuracy. There are three main sources of SSM 2 , 4 6 : in-situ soil moisture, satellite observations, and soil moisture products from either Machine Learning (ML) algorithms or Land Surface Model (LSM) 2 , 7 . The in-situ observations provide continuous observations from different soil depths at the point scale.…”
Section: Background and Summarymentioning
confidence: 99%
“…Results. Zhang et al [44] used the random forest model, which had land surface feature parameters and in situ soil moisture data to predict SSM. It would be good to test the same experiment on a diferent environment to check the consistency of the results.…”
Section: Land Surface Feature Infuence On Ssm Estimationmentioning
confidence: 99%