Validation and calibration of soil moisture using soil moisture and ocean salinity satellite data in Malaysia
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Date
2017
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Universiti Teknologi Malaysia
Abstract
Soil moisture is a key variable of the earth’s hydrological cycles, agricultural practices, and climate change model. Accurate soil moisture data at reasonable spatial and temporal resolutions is very important for improving weather predictions, disaster management, drought monitoring, precision farming, and climate modelling. However, a scarcity of soil moisture data over tropical regions is a major issue as the collection of field based soil moisture is both expensive and labour intensive. Remote sensing provides a platform for soil moisture estimation at larger scale. The launch of the Soil Moisture and Ocean Salinity (SMOS) satellite mission was aimed at providing global soil moisture product in a period of three days. Validation of the soil moisture product retrieved by SMOS is important to determine its reliability in scientific usage. The SMOS soil moisture products have been validated at several locations worldwide, except in the humid tropical regions. This study aims to validate various versions of the SMOS soil moisture products and to produce highly accurate soil moisture data for Malaysia. To achieve the objectives, an in-situ data collection network was built at selected agricultural plots in the Kluang district, Johor, Malaysia to collect hourly soil moisture, soil surface temperature, air temperature, and relative humidity data. The validation of SMOS Level 2 soil moisture products at version 551 (V551) and version 620 (V620) with the soil moisture data collected from the network show unconvincing results. The validation of V551 products show bias and Root Mean Square Errors (RMSE) of 0.057 m3 m-3 to 0.097 m3 m-3 and 0.078 m3 m-3 to 0.116 m3 m-3, respectively. The validation of V620 products show bias and RMSE from 0.050 m3 m-3 to 0.116 m3 m-3 and 0.068 m3 m-3 to 0.142 m3 m-3, respectively. Although V620 products show lower bias and RMSE when compared to V551, the accuracy is still below the target of SMOS mission which is 0.04 m3 m-3. Therefore, the accuracy of the V620 products was improved in this study by replacing the input parameters used by SMOS to estimate soil moisture with local parameters including soil moisture, vegetation optical thickness, soil roughness, and soil temperature. Based on the L-band Microwave Emission and Biosphere (L-MEB) model and SMOS Level 1C brightness temperature products, the cost function optimization of Levenberg-Marquardt algorithm was used to retrieve soil moisture. Results show an improved accuracy with bias ranging from only 0.020 m3 m-3 to 0.056 m3 m-3 and RMSE of 0.026 m3 m-3 to 0.065 m3 m-3. This study shows the importance of local parameters in retrieving soil moisture with higher accuracy compared to the use of global parameters in SMOS soil moisture retrieval algorithm. The improved soil moisture products can be used in various environmental applications in Malaysia
Description
Thesis (Ph.D (Remote Sensing))
Keywords
Soil moisture—Measurement