Abstract
This paper presents a learning-based, physics-aware soil moisture (SM) retrieval algorithm for NASA’s Cyclone Global Navigation Satellite System (CYGNSS) mission. The goal of the proposed novel method is to advance CYGNSS-based SM estimations, exploiting the spatio-temporal resolution of the GNSS reflectometry (GNSS-R) signals to its highest potential within a machine learning framework. The methodology employs a fully connected Artificial Neural Network (ANN) regression model to perform SM predictions through learning the nonlinear relations of SM and other land geophysical parameters to the CYGNSS observables. In situ SM measurements from several International SM Network (ISMN) sites are used as reference labels; CYGNSS incidence angles, derived reflectivity and trailing edge slope (TES) values, as well as ancillary data, are exploited as input features for training and validation of the ANN model. In particular, the utilized ancillary data consist of normalized difference vegetation index (NDVI), vegetation water content (VWC), terrain elevation, terrain slope, and h-parameter (surface roughness). Land cover classification and inland water body masks are also used for the intermediate derivations and quality control purposes. The proposed algorithm assumes uniform SM over a 0.0833 ∘ × 0.0833 ∘ (approximately 9 km × 9 km around the equator) lat/lon grid for any CYGNSS observation that falls within this window. The proposed technique is capable of generating sub-daily and high-resolution SM predictions as it does not rely on time-series or spatial averaging of the CYGNSS observations. Once trained on the data from ISMN sites, the model is independent from other SM sources for retrieval. The estimation results obtained over unseen test data are promising: SM predictions with an unbiased root mean squared error of 0.0544 cm 3 /cm 3 and Pearson correlation coefficient of 0.9009 are reported for 2017 and 2018.
Highlights
Soil moisture (SM) has an active role in the Earth’s water cycle between the ground and the air
Regarding the fact that the Cyclone Global Navigation Satellite System (CYGNSS) spatial resolution would range from a theoretical minimum of 0.6 km to 8.3 km depending on the incidence angles, and relative orientations of the instruments, we considered a size for the open water bodies that is close to the minimum resolution that would work to initiate investigations in this study
There is a slight difference between two, additional Γratio term reduces the overall performance of the optimal combination so that it is not included in the optimal combination. (v) Instead of giving the specular point (SP) incidence angle as an input to the system, the reflectivity values are corrected for incidence and fed to the system without angle information; the model performance was slightly worse than feeding SP incidence angles to the learning
Summary
Soil moisture (SM) has an active role in the Earth’s water cycle between the ground and the air. The current state of the science for global SM estimation relies on microwave remote sensing with the use of traditional instruments such as monostatic radars and radiometers. This is because the microwave frequencies are sensitive to the changes in the soil dielectric properties with respect to the presence of moisture content [4]. SMAP mission was designed to make use of a 6 m mesh reflector antenna for both radar and radiometer instruments to provide high spatio-temporal resolution SM products [7,8]. The radar backscattering data of ESA’s Sentinel-1 (C-band) [9] and DLR’s TERRASAR-X (X-band) [10] were used for global SM estimates
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