A new method to inverse soil moisture based on thermal infrared and passive microwave remote sensing

Zhuang Zhou; Xiaokang Kou; Shaojie Zhao; Lingmei Jiang

doi:10.1117/12.2069373

8 November 2014 A new method to inverse soil moisture based on thermal infrared and passive microwave remote sensing

Zhuang Zhou, Xiaokang Kou, Shaojie Zhao, Lingmei Jiang

Proceedings Volume 9260, Land Surface Remote Sensing II; 926026 (2014) https://doi.org/10.1117/12.2069373
Event: SPIE Asia-Pacific Remote Sensing, 2014, Beijing, China

Abstract

Soil moisture is one of the main factors in the water, energy and carbon cycles. It constitutes a major uncertainty in climate and hydrological models. By now, passive microwave remote sensing and thermal infrared remote sensing technology have been used to obtain and monitor soil moisture. However, as the resolution of passive microwave remote sensing is very low and the thermal infrared remote sensing method fails to provide soil temperature on cloudy days, it is hard to monitor the soil moisture accurately. To solve the problem, a new method has been tried in this research. Thermal infrared remote sensing and passive microwave remote sensing technology have been combined based on the delicate experiment. Since the soil moisture retrieved by passive microwave in general represents surface centimeters deep, which is different from deeper soil moisture estimated by thermal inertia method, a relationship between the two depths soil moisture has been established based on the experiment. The results show that there is a good relationship between the soil moisture estimated by passive microwave and thermal infrared remote sensing method. The correlation coefficient is 0.78 and RMSE (root mean square error) is 0.0195􀜿􀝉􀬷 · 􀜿􀝉􀬿􀬷. This research provides a new possible method to inverse soil moisture.

Citation Download Citation

Zhuang Zhou, Xiaokang Kou, Shaojie Zhao, and Lingmei Jiang "A new method to inverse soil moisture based on thermal infrared and passive microwave remote sensing", Proc. SPIE 9260, Land Surface Remote Sensing II, 926026 (8 November 2014); https://doi.org/10.1117/12.2069373

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