Abstract
High spatiotemporal soil moisture (SM) is essential for many meteorological, hydrological, and agricultural applications and studies. Spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) provides a promising opportunity for high-resolution SM retrievals. NASA’s Cyclone Global Navigation Satellite System (CYGNSS) is a recent GNSS-R application that offers relatively high spatial and temporal resolution observations from earth’s surface. However, the quasi-random sampling of land surface by the CYGNSS constellation circumvents obtaining fully observed daily SM predictions at high spatial resolutions. Spatial interpolation techniques may fill this gap and provide a fully covered high-resolution daily SM estimation. However, the spatial interpolation errors need to be assessed when applied to the quasi-random 9-km CYGNSS-based SM estimations. In this article, we conduct interpolation error analysis using the Soil Moisture Active Passive (SMAP) Enhanced L3 Radiometer Global Daily 9-km product, sampled at the CYGNSS observation locations. The results indicate that the overall interpolation error (RMSE) was 0.013 m $^3$ m $^{-3}$ over SMAP’s recommended grids. In addition, sparse CYGNSS SM observations are directly interpolated. The achieved results show that interpolated and observed CYGNSS SM values have similar performance metrics when validated with the SMAP 9-km gridded SM product as well as sparse SM networks.
Highlights
S OIL moisture (SM) is one of the critical components of agriculture and terrestrial water management, and global climate studies [1]
The present study extends our previous work by incorporating ordinary Kriging [24], inverse distance weighting (IDW) [25], best linear unbiased estimation (BLUE) [26], and previously observed behavior interpolation (POBI) [22] techniques for spatial interpolation and analyzes the interpolation performance for different land cover and geographic factors
We provide more detailed analysis of the observed interpolation error with respect to Cyclone Global Navigation Satellite System (CYGNSS) revisit rate, geographical distribution, land cover, Soil Moisture Active Passive (SMAP) retrieval quality flags (RQFs), and number of annual CYGNSS observations
Summary
S OIL moisture (SM) is one of the critical components of agriculture and terrestrial water management, and global climate studies [1]. Global, and high-resolution SM estimation is required for many studies, including water resource management, irrigation scheduling, prediction of agricultural yields, and flood forecasting [2], [3]. Microwave remote sensing technologies have been widely used for SM estimation on a global scale [4]. There are several ongoing satellite missions that provide SM estimation with different spatial and temporal resolutions. Manuscript received July 18, 2021; revised September 3, 2021; accepted September 14, 2021. Date of publication September 20, 2021; date of current version October 8, 2021.
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