Next Article in Journal
Surface Tradeoffs and Elevational Shifts at the Largest Italian Glacier: A Thirty-Years Time Series of Remotely-Sensed Images
Previous Article in Journal
Deriving Tree Size Distributions of Tropical Forests from Lidar
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Evaluating Downscaling Factors of Microwave Satellite Soil Moisture Based on Machine Learning Method

College of Geoscience and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2021, 13(1), 133; https://doi.org/10.3390/rs13010133
Submission received: 27 November 2020 / Revised: 27 December 2020 / Accepted: 29 December 2020 / Published: 2 January 2021

Abstract

Downscaling microwave remotely sensed soil moisture (SM) is an effective way to obtain spatial continuous SM with fine resolution for hydrological and agricultural applications on a regional scale. Downscaling factors and functions are two basic components of SM downscaling where the former is particularly important in the era of big data. Based on machine learning method, this study evaluated Land Surface Temperature (LST), Land surface Evaporative Efficiency (LEE), and geographical factors from Moderate Resolution Imaging Spectroradiometer (MODIS) products for downscaling SMAP (Soil Moisture Active and Passive) SM products. This study spans from 2015 to the end of 2018 and locates in the central United States. Original SMAP SM and in-situ SM at sparse networks and core validation sites were used as reference. Experiment results indicated that (1) LEE presented comparative performance with LST as downscaling factors; (2) adding geographical factors can significantly improve the performance of SM downscaling; (3) integrating LST, LEE, and geographical factors got the best performance; (4) using Z-score normalization or hyperbolic-tangent normalization methods did not change the above conclusions, neither did using support vector regression nor feed forward neural network methods. This study demonstrates the possibility of LEE as an alternative of LST for downscaling SM when there is no available LST due to cloud contamination. It also provides experimental evidence for adding geographical factors in the downscaling process.
Keywords: soil moisture; downscaling; SMAP; MODIS; machine learning soil moisture; downscaling; SMAP; MODIS; machine learning
Graphical Abstract

Share and Cite

MDPI and ACS Style

Sun, H.; Cui, Y. Evaluating Downscaling Factors of Microwave Satellite Soil Moisture Based on Machine Learning Method. Remote Sens. 2021, 13, 133. https://doi.org/10.3390/rs13010133

AMA Style

Sun H, Cui Y. Evaluating Downscaling Factors of Microwave Satellite Soil Moisture Based on Machine Learning Method. Remote Sensing. 2021; 13(1):133. https://doi.org/10.3390/rs13010133

Chicago/Turabian Style

Sun, Hao, and Yajing Cui. 2021. "Evaluating Downscaling Factors of Microwave Satellite Soil Moisture Based on Machine Learning Method" Remote Sensing 13, no. 1: 133. https://doi.org/10.3390/rs13010133

APA Style

Sun, H., & Cui, Y. (2021). Evaluating Downscaling Factors of Microwave Satellite Soil Moisture Based on Machine Learning Method. Remote Sensing, 13(1), 133. https://doi.org/10.3390/rs13010133

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop