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Open AccessArticle

Machine Learning-Based CYGNSS Soil Moisture Estimates over ISMN sites in CONUS

1
Geosystems Research Institute, Mississippi State University, Mississippi State, MS 39759, USA
2
Department of Electrical and Computer Engineering, Mississippi State University, Mississippi State, MS 39759, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(7), 1168; https://doi.org/10.3390/rs12071168
Received: 11 March 2020 / Revised: 31 March 2020 / Accepted: 3 April 2020 / Published: 5 April 2020
(This article belongs to the Special Issue Applications of GNSS Reflectometry for Earth Observation)
Soil moisture (SM) derived from satellite-based remote sensing measurements plays a vital role for understanding Earth’s land and near-surface atmosphere interactions. Bistatic Global Navigation Satellite System (GNSS) Reflectometry (GNSS-R) has emerged in recent years as a new domain of microwave remote sensing with great potential for SM retrievals, particularly at high spatio-temporal resolutions. In this work, a machine learning (ML)-based framework is presented for obtaining SM data products over the International Soil Moisture Network (ISMN) sites in the Continental United States (CONUS) by leveraging spaceborne GNSS-R observations provided by NASA’s Cyclone GNSS (CYGNSS) constellation alongside remotely sensed geophysical data products. Three widely-used ML approaches—artificial neural network (ANN), random forest (RF), and support vector machine (SVM)—are compared and analyzed for the SM retrieval through utilizing multiple validation strategies. Specifically, using a 5-fold cross-validation method, overall RMSE values of 0.052, 0.061, and 0.065 cm3/cm3 are achieved for the RF, ANN, and SVM techniques, respectively. In addition, both a site-independent and a year-based validation techniques demonstrate satisfactory accuracy of the proposed ML model, suggesting that this SM approach can be generalized in space and time domains. Moreover, the achieved accuracy can be further improved when the model is trained and tested over individual SM networks as opposed to combining all available SM networks. Additionally, factors including soil type and land cover are analyzed with respect to their impacts on the accuracy of SM retrievals. Overall, the results demonstrated here indicate that the proposed technique can confidently provide SM estimates over lightly-vegetated areas with vegetation water content (VWC) less than 5 kg/m2 and relatively low spatial heterogeneity. View Full-Text
Keywords: artificial neural networks; random forest; SVM; CYGNSS; soil moisture retrieval; ISMN; machine learning artificial neural networks; random forest; SVM; CYGNSS; soil moisture retrieval; ISMN; machine learning
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MDPI and ACS Style

Senyurek, V.; Lei, F.; Boyd, D.; Kurum, M.; Gurbuz, A.C.; Moorhead, R. Machine Learning-Based CYGNSS Soil Moisture Estimates over ISMN sites in CONUS. Remote Sens. 2020, 12, 1168.

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