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Article

Evaluations of Machine Learning-Based CYGNSS Soil Moisture Estimates against SMAP Observations

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(21), 3503; https://doi.org/10.3390/rs12213503
Received: 8 September 2020 / Revised: 16 October 2020 / Accepted: 21 October 2020 / Published: 25 October 2020
(This article belongs to the Special Issue Applications of GNSS Reflectometry for Earth Observation)
This paper presents a machine learning (ML) framework to derive a quasi-global soil moisture (SM) product by direct use of the Cyclone Global Navigation Satellite System (CYGNSS)’s high spatio-temporal resolution observations over the tropics (within ±38° latitudes) at L-band. The learning model is trained by using in-situ SM data from the International Soil Moisture Network (ISMN) sites and various space-borne ancillary data. The approach produces daily SM retrievals that are gridded to 3 km and 9 km within the CYGNSS spatial coverage. The performance of the model is independently evaluated at various temporal scales (daily, 3-day, weekly, and monthly) against Soil Moisture Active Passive (SMAP) mission’s enhanced SM products at a resolution of 9 km × 9 km. The mean unbiased root-mean-square difference (ubRMSD) between concurrent (same calendar day) CYGNSS and SMAP SM retrievals for about three years (from 2017 to 2019) is 0.044 cm3 cm3 with a correlation coefficient of 0.66 over SMAP recommended grids. The performance gradually improves with temporal averaging and degrades over regions regularly flagged by SMAP such as dense forest, high topography, and coastlines. Furthermore, CYGNSS and SMAP retrievals are evaluated against 170 ISMN in-situ observations that result in mean unbiased root-mean-square errors (ubRMSE) of 0.055 cm3 cm3 and 0.054 cm3 cm3, respectively, and a higher correlation coefficient with CYGNSS retrievals. It is important to note that the proposed approach is trained over limited in-situ observations and is independent of SMAP observations in its training. The retrieval performance indicates current applicability and future growth potential of GNSS-R-based, directly measured spaceborne SM products that can provide improved spatio-temporal resolution than currently available datasets. View Full-Text
Keywords: GNSS-reflectometry; random forest; CYGNSS; soil moisture retrieval; ISMN; SMAP GNSS-reflectometry; random forest; CYGNSS; soil moisture retrieval; ISMN; SMAP
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MDPI and ACS Style

Senyurek, V.; Lei, F.; Boyd, D.; Gurbuz, A.C.; Kurum, M.; Moorhead, R. Evaluations of Machine Learning-Based CYGNSS Soil Moisture Estimates against SMAP Observations. Remote Sens. 2020, 12, 3503. https://doi.org/10.3390/rs12213503

AMA Style

Senyurek V, Lei F, Boyd D, Gurbuz AC, Kurum M, Moorhead R. Evaluations of Machine Learning-Based CYGNSS Soil Moisture Estimates against SMAP Observations. Remote Sensing. 2020; 12(21):3503. https://doi.org/10.3390/rs12213503

Chicago/Turabian Style

Senyurek, Volkan, Fangni Lei, Dylan Boyd, Ali Cafer Gurbuz, Mehmet Kurum, and Robert Moorhead. 2020. "Evaluations of Machine Learning-Based CYGNSS Soil Moisture Estimates against SMAP Observations" Remote Sensing 12, no. 21: 3503. https://doi.org/10.3390/rs12213503

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