Dataset Information

Closing the Loop of Satellite Soil Moisture Estimation via Scale Invariance of Hydrologic Simulations.

ABSTRACT: Surface soil moisture plays a crucial role on the terrestrial water, energy, and carbon cycles. Characterizing its variability in space and time is critical to increase our capability to forecast extreme weather events, manage water resources, and optimize agricultural practices. Global estimates of surface soil moisture are provided by satellite sensors, but at coarse spatial resolutions. Here, we show that the resolution of satellite soil moisture products can be increased to scales representative of ground measurements by reproducing the scale invariance properties of soil moisture derived from hydrologic simulations at hyperresolutions of less than 100?m. Specifically, we find that surface soil moisture is scale invariant over regimes extending from a satellite footprint to 100?m. We use this evidence to calibrate a statistical downscaling algorithm that reproduces the scale invariance properties of soil moisture and test the approach against 1-km aircraft remote sensing products and through comparisons of downscaled satellite products to ground observations. We demonstrate that hyperresolution hydrologic models can close the loop of satellite soil moisture downscaling for local applications such as agricultural irrigation, flood event prediction, and drought and fire management.

SUBMITTER: Mascaro G

PROVIDER: S-EPMC6834674 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Publications

Closing the Loop of Satellite Soil Moisture Estimation via Scale Invariance of Hydrologic Simulations.

Mascaro Giuseppe G Ko Ara A Vivoni Enrique R ER

Scientific reports 20191106 1

Surface soil moisture plays a crucial role on the terrestrial water, energy, and carbon cycles. Characterizing its variability in space and time is critical to increase our capability to forecast extreme weather events, manage water resources, and optimize agricultural practices. Global estimates of surface soil moisture are provided by satellite sensors, but at coarse spatial resolutions. Here, we show that the resolution of satellite soil moisture products can be increased to scales representa ...[more]

PMID: 31695120

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Project description:Annual soil moisture estimates are useful to characterize trends in the climate system, in the capacity of soils to retain water and for predicting land and atmosphere interactions. The main source of soil moisture spatial information across large areas (e.g., continents) is satellite-based microwave remote sensing. However, satellite soil moisture datasets have coarse spatial resolution (e.g., 25-50 km grids); and large areas from regional-to-global scales have spatial information gaps. We provide an alternative approach to predict soil moisture spatial patterns (and associated uncertainty) with higher spatial resolution across areas where no information is otherwise available. This approach relies on geomorphometry derived terrain parameters and machine learning models to improve the statistical accuracy and the spatial resolution (from 27km to 1km grids) of satellite soil moisture information across the conterminous United States on an annual basis (1991-2016). We derived 15 primary and secondary terrain parameters from a digital elevation model. We trained a machine learning algorithm (i.e., kernel weighted nearest neighbors) for each year. Terrain parameters were used as predictors and annual satellite soil moisture estimates were used to train the models. The explained variance for all models-years was >70% (10-fold cross-validation). The 1km soil moisture grids (compared to the original satellite soil moisture estimates) had higher correlations (improving from r2 = 0.1 to r2 = 0.46) and lower bias (improving from 0.062 to 0.057 m3/m3) with field soil moisture observations from the North American Soil Moisture Database (n = 668 locations with available data between 1991-2013; 0-5cm depth). We conclude that the fusion of geomorphometry methods and satellite soil moisture estimates is useful to increase the spatial resolution and accuracy of satellite-derived soil moisture. This approach can be applied to other satellite-derived soil moisture estimates and regions across the world.

Project description:In runoff generation process, soil moisture plays an important role as it controls the magnitude of the flood events in response to the rainfall inputs. In this study, we investigated the ability of a new era of satellite soil moisture retrievals to improve the Soil & Water Assessment Tool (SWAT) daily discharge simulations via soil moisture data assimilation for two small (< 500 km2) and hydrologically different catchments located in Central Italy. We ingested 1) the Soil Moisture Active and Passive (SMAP) Enhanced L3 Radiometer Global Daily 9 km EASE-Grid soil moisture, 2) the Advanced SCATterometer (ASCAT) H113 soil moisture product released within the EUMETSAT Satellite Application Facility on Support to Operational Hydrology and Water Management (H-SAF) which has a nearly daily temporal resolution and sampling of 12.5 km, and 3) a fused ASCAT/Sentinel-1 (S1) satellite soil moisture product named SCATSAR-SWI with temporal and spatial sampling of 1 day and 1 km, respectively into SWAT hydrological model via the Ensemble Kalman Filter (EnKF). Different configurations were tested with the aim of exploring the effect of the hydrological regime, the land use conditions, the spatial sampling and the revisit time of the products (which controls the amount of available data to be potentially ingested). Results show a general improvement of SWAT discharge simulations for all products in terms of error and Nash Sutcliffe efficiency index. In particular, we found a relatively good behavior of both the active and the passive products in terms of low flows improvement especially for the catchment characterized by a higher baseflow component. The benefit of the higher spatial resolution of SCATSAR-SWI obtained via S1 over ASCAT was small, likely due to very challenging areas for the S1 retrieval. Eventually, better performances were obtained for the passive product in the more forested catchment. With the aim of exploring the benefit of having more frequent satellite soil moisture observations to be ingested, we tested the performance of the ASCAT product with a reduced temporal sampling obtained by temporally matching ASCAT observations to that of SMAP. The results show a significant reduction of the performance of ASCAT, suggesting that the correction frequency (due to the higher number of observations available) for small catchments is an important aspect for improving flood forecasting as it helps to adjust more frequently the pre-storm soil moisture conditions.

Dataset Information

Closing the Loop of Satellite Soil Moisture Estimation via Scale Invariance of Hydrologic Simulations.

Publications

Closing the Loop of Satellite Soil Moisture Estimation via Scale Invariance of Hydrologic Simulations.

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