Soil temperature estimation at different depths,using remotely-sensed data

【Author】

HUANG Ran;HUANG Jian-xi;ZHANG Chao;MA Hong-yuan;ZHUO Wen;CHEN Ying-yi;ZHU De-hai;Qingling WU;Lamin R.MANSARAY;College of Land Science and Technology,China Agricultural University/Key Laboratory of Remote Sensing for Agri-hazards,Ministry of Agriculture and Rural Affairs/Key Laboratory for Agricultural Land Quality,Ministry of Natural Resources;College of Information & Electrical Engineering,China Agricultural University;Department of Geography,University College London;Department of Agro-meteorology and Geo-informatics,Magbosi Land,Water and Environment Research Centre (MLWERC),Sierra Leone Agricultural Research Institute (SLARI);

【Institution】

College of Land Science and Technology,China Agricultural University/Key Laboratory of Remote Sensing for Agri-hazards,Ministry of Agriculture and Rural Affairs/Key Laboratory for Agricultural Land Quality,Ministry of Natural Resources;College of Information & Electrical Engineering,China Agricultural University;Department of Geography,University College London;Department of Agro-meteorology and Geo-informatics,Magbosi Land,Water and Environment Research Centre (MLWERC),Sierra Leone Agricultural Research Institute (SLARI);

【Abstract】

Soil temperatures at different depths down the soil profile are important agro-meteorological indicators which are necessary for ecological modeling and precision agricultural activities. In this paper, using time series of soil temperature(ST) measured at different depths(0, 5, 10, 20, and 40 cm) at agro-meteorological stations in northern China as reference data, ST was estimated from land surface temperature(LST) and normalized difference vegetation index(NDVI) derived from AQUA/TERRA MODIS data, and solar declination(Ds) in univariate and multivariate linear regression models. Results showed that when daytime LST is used as predictor, the coefficient of determination(R~2) values decrease from the 0 cm layer to the 40 cm layer. Additionally, with the use of nighttime LST as predictor, the R~2 values were relatively higher at 5, 10 and 15 cm depths than those at 0, 20 and 40 cm depths. It is further observed that the multiple linear regression models for soil temperature estimation outperform the univariate linear regression models based on the root mean squared errors(RMSEs) and R~2. These results have demonstrated the potential of MODIS data in tandem with the Ds parameter for soil temperature estimation at the upper layers of the soil profile where plant roots grow in. To the best of our knowledge, this is the first attempt at the synergistic use of LST, NDVI and Ds for soil temperature estimation at different depths of the upper layers of the soil profile, representing a significant contribution to soil remote sensing.

【Keywords】

soil temperature;;land surface temperature;;normalized difference vegetation index;;solar declination

References

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Total: 40 articles

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