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ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
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ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2023
License: CC BY
Data sources: ZENODO
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Satellite-driven 10km global root-zone soil moisture analysis for drought monitoring

Authors: Siyuan Tian; Luigi Renzullo; Dejun Cai;

Satellite-driven 10km global root-zone soil moisture analysis for drought monitoring

Abstract

Root-zone soil moisture condition is an important component of water cycle at all spatial scales, as it controls various hydrological, biological and meteorological processes such as plant transpiration and hydraulic redistribution. Passive microwave remote sensing offers the possibility to access to near-surface (0 - 5cm) soil moisture measurements over large areas, providing valuable information for agricultural and water resource management. However, the spatial resolution of satellite soil moisture estimates from passive microwave sensor is relatively coarse (25 to 50km) and infrequent in time. Data assimilation algorithms are widely used to obtain spatially complete and daily continuous soil moisture estimates from intermittent remotely sensed soil moisture data and numerical models. The dataset contains the most recent global surface and root-zone soil moisture conditions at 10km generated from the Satellite-Guided Root-zone moisture Analysis and Forecasting System (S-GRAFS) from 2015 to 2022. S-GRAFS is a near-real time data assimilation system that combines complementary information from model simulations and satellite observations to provide soil moisture estimates at near surface and root-zone. In S-GRAFS, satellite soil moisture observations from Soil Moisture Active Passive (SMAP) are assimilated into a simple first-order autoregressive model that captures the soil moisture conditions in response to precipitation. Satellite precipitation from Global Precipitation Measurement (GPM) is used to drive the model to simulate near surface soil moisture at 5cm. The assimilation of SMAP data relies on the four-dimensional variational (4DVAR) method to adjust the modelled surface soil moisture towards observations within a 4-day assimilation window. Soil Water Index (SWI) is then derived from the analysed surface soil moisture using an exponential filter and represents the root-zone soil wetness at approximate 1m depth. The surface and root-zone wetness from S-GRAFS can be converted into absolute soil moisture content using soil physical properties to provide essential support for a wide variety of hydrological and agricultural applications.

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Keywords

drought monitoring, global soil moisture condition, data assimilation

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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