Evaluation of GLDAS Model Soil Moisture Using Observational Data, VHI Index and Precipitation

Subject Areas : Drought in meteorology and agriculture

Sahar Rezaei ¹ (PhD student, Department of Water Engineering, Islamic Azad University, Science and Research Branch, Tehran, Iran.)
Seyed Abbas Hosseini ² (Associate Professor, Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran)
Ahmad Sharafati ³ (Associate Professor, Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran)

Received: 2024-03-07 Accepted : 2024-08-16 Published : 2024-10-02

Keywords: Drought, Soil Moisture, Precipitation, GLDAS, Remote Sensing, VHI,

Abstract :

Background and Aim: Soil moisture data obtained from satellites play an important role in the effective management of water resources, especially in areas susceptive to dehydration and drought. By monitoring soil moisture dynamics over time, water policymakers can develop sustainable water allocation strategies, implement water conservation measures, and reduce the adverse effects of drought on agriculture. Satellite data facilitates the identification of potential water stress points. The purpose of this study is to evaluate the accuracy of soil moisture in the GLDAS model with observational data in areas with dry, humid, and semi-humid climates, Agricultural drought monitoring using the GLDAS model and the VHI drought index and annual precipitation.

Method: In this study, soil moisture from the GLDAS model from 2003 to 2020 in 6 agricultural areas of Oklahoma was extracted daily and with observational data recorded in 6 agricultural areas located in Oklahoma compared. To better understand the degree of difference between the GLDAS model soil moisture data with observational data, agricultural areas are divided into three dry, humid, and semi-humid areas,they were split and finally evaluated on a daily, seasonal, and annual basis with the used of two indicators of correlation coefficient CC and average RMSE root mean squares error.

Results: The results showed that the highest average correlation of seasonal soil moisture is related to winter and autumn, and one of the reasons is more precipitation in these seasons. The highest correlation of average areas with 0/64 belonged to humid areas and the lowest average correlation of 0.47 was related to dry areas. Humid areas usually have vegetation cover more than dry areas. Vegetation cover due to the impact on microwave signals received by satellite sensors affects the accuracy of satellite soil moisture estimates. Also, the presence of dense vegetation improves the soil moisture recovery of satellite data, especially in areas with abundant vegetation. The results of RMSE (cm3/cm3) of the GLDAS model and observation data in 6 agricultural areas of Oklahoma showed the good performance of the GLDAS model. The correlation of GLDAS model data with VHI drought index and precipitation was further investigated, which correlated GLDAS model soil moisture with 17-year precipitation data equal to 0.68 the correlation of the model GLDAS and VHI was equal to 0.2. One of the reasons for the variation in the correlation between precipitation, VHI, and soil moisture of the GLDAS model is the changes in hydrological parameters such as groundwater feeding, evaporation and transpiration rates, and surface runoff, it is in different years.

Conclusion: According to the results, soil moisture serves as a critical intermediary between precipitation and vegetation health. While precipitation directly influences soil moisture dynamics, soil moisture, in turn, regulates plant water uptake, transpiration, and physiological processes that contribute to VHI. GLDAS model soil moisture data can be used to monitor and assess drought. GLDAS data are available at regular intervals (e.g., daily, monthly), allowing monitoring of soil moisture dynamics over time. This time resolution is crucial for tracking the start, duration, and severity of drought events. The results of this study show that GLDAS model soil moisture data can be used with confidence in monitoring agricultural drought.

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https://doi.org/10.3390/rs11030362