Leveraging Google Earth Engine for Drought Assessment using Global Soil Moisture Data.

Nazmus Sazib,Iliana Mladenova,John Bolten

doi:10.3390/rs10081265

Nazmus Sazib, Iliana Mladenova + Show 1 more

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https://doi.org/10.3390/rs10081265

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Abstract

Soil moisture is considered a key variable to assess crop and drought conditions. However, readily available soil moisture datasets developed for monitoring agricultural drought conditions are uncommon. The aim of this work is to examine two global soil moisture data sets and a set of soil moisture web-based processing tools developed to demonstrate the value of the soil moisture data for drought monitoring and crop forecasting using Google Earth Engine (GEE). The two global soil moisture data sets discussed in the paper are generated by integrating Soil Moisture Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) satellite-derived observations into the modified two-layer Palmer model using a 1-D Ensemble Kalman Filter (EnKF) data assimilation approach. The web-based tools are designed to explore soil moisture variability as a function of land cover change and to easily estimate drought characteristics such as drought duration and intensity using soil moisture anomalies, and to inter-compare them against alternative drought indicators. To demonstrate the utility of these tools for agricultural drought monitoring, the soil moisture products, vegetation- and precipitation-based products are assessed over drought prone regions in South Africa and Ethiopia. Overall, the 3-month scale Standardized Precipitation Index (SPI) and Normalized Vegetation Index (NDVI) showed higher agreement with the root zone soil moisture anomalies. Soil moisture anomalies exhibited lower drought duration but higher intensity compare to SPIs. Inclusion of the global soil moisture data into GEE data catalog and the development of the web-based tools described in the paper enable a vast diversity of users to quickly and easily assess the impact of drought and improve planning related to drought risk assessment and early warning. GEE also improves the accessibility and usability of the earth observation data and related tools by making them available to a wide range of researchers and the public. In particular, the cloud-based nature of GEE is useful for providing access to the soil moisture data and scripts to users in developing countries that lack adequate observational soil moisture data or the necessary computational resources required to develop them.

Highlights

The remote sensing advances made over the past three decades have radically improved our ability to obtain routine, global information about the amount of water present in the soil [1,2,3]
This paper focuses on the value of a combined model-satellite soil moisture dataset for near-real time drought and crop condition monitoring, developed in an effort to improve the root zone soil moisture (RZSM) information used by the United States Department of Agriculture-Foreign Agricultural Services (USDA-FAS)
Soil moisture data are recognized as a fundamental physical variable that can be used to address science and resource-management questions requiring near real-time monitoring of the land–atmosphere boundary, including flood and drought monitoring and regional crop yield assessment

Summary

Introduction

The remote sensing advances made over the past three decades have radically improved our ability to obtain routine, global information about the amount of water present in the soil [1,2,3]. Several well-evaluated soil moisture (SM) datasets have proven useful for a wide range of applications, including weather and climate forecasting, monitoring of drought and wildfires, tracking floods and landslides, and enhanced agricultural productivity [4,5,6,7]. Many hydrological processes and forecasting and decision-making activities linked to soil moisture require knowledge of the root zone soil moisture (RZSM) information. The latter is typically estimated using hydrologic land surface models, which are traditionally driven by some weather-related observations such as precipitation, temperature, and so forth. The credibility of the modeled RZSM estimates is strongly dependent on the quality of the forcing data [8,9]

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