研究目的
To investigate the effects of soil texture on the estimation of bare soil moisture content using MODIS images and to develop a modified regression model to improve the accuracy of the estimated soil moisture content.
研究成果
The study found that soil texture significantly influences the accuracy of soil moisture content estimation from MODIS images, with medium texture soils showing the highest correlation. The proposed 3index_SMC model improved estimation accuracy by up to 1% for medium texture soils. The results highlight the importance of considering soil texture in remote sensing-based soil moisture estimation.
研究不足
The mismatch between the soil moisture field measurements and the pixel size of MODIS images introduces uncertainty. The study suggests radiometric cross-calibration between high and low spatial resolution images as a potential solution.
1：Experimental Design and Method Selection:
The study utilized MODIS images and in situ data from US-SCAN stations to investigate the relationship between soil texture and soil moisture content. A linear regression model combining LST, NDWI, and VSDI indices was proposed.
2：Sample Selection and Data Sources:
Data from 47 US-SCAN stations meeting specific criteria were used. The stations were selected based on soil texture homogeneity, absence of water bodies, minimal vegetation cover, and absence of extreme topography.
3：List of Experimental Equipment and Materials:
MODIS images (MOD09/MYD09 and MOD11/MYD11 products) and in situ soil moisture measurements from US-SCAN stations.
4：Experimental Procedures and Operational Workflow:
The study involved pre-processing of MODIS images, classification of stations based on soil texture, application of conventional and proposed regression models, and evaluation of model accuracy.
5：Data Analysis Methods:
The accuracy of the models was evaluated using the coefficient of determination (R2), Root Mean Square Error (RMSE), and relative RMSE (RRMSE).
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