Abstract
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 cm−3 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 cm−3 and 0.054 cm3 cm−3, 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.
Highlights
Soil moisture (SM) is an essential hydroecological variable that plays an important role in water dynamics, evapotranspiration [1], the energy and carbon flows between the surface and the atmosphere [2], vegetation states [3], climatic conditions [4], and many hydrological and agricultural processes
We evaluate the performance of the proposed Cyclone Global Navigation Satellite System (CYGNSS) machine learning (ML)-based SM retrieval against Soil Moisture Active Passive (SMAP) observations within the CYGNSS coverage
A ML model designed for surface SM estimation by using CYGNSS measurements in conjunction with ancillary data is presented for surface SM estimation
Summary
Soil moisture (SM) is an essential hydroecological variable that plays an important role in water dynamics, evapotranspiration [1], the energy and carbon flows between the surface and the atmosphere [2], vegetation states [3], climatic conditions [4], and many hydrological and agricultural processes. The European Space Agency’s Soil Moisture and Ocean Salinity (SMOS, launched in late 2009) [5] and the National Aeronautics and Space Administration (NASA)’s Soil Moisture Active Passive (SMAP, launched in early 2015) [6] are two passive microwave spaceborne missions that are currently operating at L-band Both provide global SM retrievals every 2–3 days with a spatial resolution on the order of several tens of kilometers. The GNSS-R approach re-purposes existing GNSS infrastructure for remote sensing by processing the forward-scattered signal that is reflected off the surface of the Earth [11] The exploitation of this approach has been accelerated with the availability of large and diverse datasets acquired from recent spaceborne GNSS-R observatories such as the United Kingdom’s TechDemoSat-1 (TDS-1, launched in mid-2014 and retired in mid-2019) [12] and NASA’s Cyclone Global Navigation Satellite System (CYGNSS, launched in late 2016) [13]. The considerable amount of CYGNSS land observation data measured in this region has greatly contributed to the development of new SM retrieval approaches from spaceborne GNSS-R [21,22,23,24,25,26,27,28,29]
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