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Article

Leveraging the Google Earth Engine for Drought Assessment Using Global Soil Moisture Data

1
Hydrological Sciences Branch, NASA Goddard Space Flight Center, Greenbelt, MD 20706, USA
2
Science Application International Corporation (SAIC), Lanham, MD 20706, USA
3
Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD 20742, USA
*
Author to whom correspondence should be addressed.
Received: 29 June 2018 / Revised: 27 July 2018 / Accepted: 8 August 2018 / Published: 11 August 2018
(This article belongs to the Collection Google Earth Engine Applications)
Soil moisture is considered to be 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 datasets 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 the Google Earth Engine (GEE). The two global soil moisture datasets discussed in the paper are generated by integrating the Soil Moisture Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) missions’ satellite-derived observations into a modified two-layer Palmer model using a one-dimensional (1D) 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 intercompare them against alternative drought indicators. To demonstrate the utility of these tools for agricultural drought monitoring, the soil moisture products and vegetation- and precipitation-based products were assessed over drought-prone regions in South Africa and Ethiopia. Overall, the 3-month scale Standardized Precipitation Index (SPI) and Normalized Difference Vegetation Index (NDVI) showed higher agreement with the root zone soil moisture anomalies. Soil moisture anomalies exhibited lower drought duration, but higher intensity compared with SPIs. Inclusion of the global soil moisture data into the 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. The 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 the 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. View Full-Text
Keywords: soil moisture; Soil Moisture Ocean Salinity; Soil Moisture Active Passive; Google Earth Engine; drought soil moisture; Soil Moisture Ocean Salinity; Soil Moisture Active Passive; Google Earth Engine; drought
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MDPI and ACS Style

Sazib, N.; Mladenova, I.; Bolten, J. Leveraging the Google Earth Engine for Drought Assessment Using Global Soil Moisture Data. Remote Sens. 2018, 10, 1265. https://0-doi-org.brum.beds.ac.uk/10.3390/rs10081265

AMA Style

Sazib N, Mladenova I, Bolten J. Leveraging the Google Earth Engine for Drought Assessment Using Global Soil Moisture Data. Remote Sensing. 2018; 10(8):1265. https://0-doi-org.brum.beds.ac.uk/10.3390/rs10081265

Chicago/Turabian Style

Sazib, Nazmus, Iliana Mladenova, and John Bolten. 2018. "Leveraging the Google Earth Engine for Drought Assessment Using Global Soil Moisture Data" Remote Sensing 10, no. 8: 1265. https://0-doi-org.brum.beds.ac.uk/10.3390/rs10081265

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