Transferability of Hyperspectral Model for Estimating Soil Organic Matter Concerned with Soil Moisture
CHEN Yi-yun1, 2, QI Kun1, 4, LIU Yao-lin1, 2, HE Jian-hua1, 2, JIANG Qing-hu3*
1. School of Resource and Environmental Science，Wuhan University，Wuhan 430079，China 2. Key Laboratory of Geographic Information System of Ministry of Education，Wuhan University，Wuhan 430079，China 3. Key Laboratory of Aquatic Botany and Watershed Ecology，Wuhan Botanical Garden, Chinese Academy of Sciences，Wuhan 430074，China 4. College of Engineering, Peking University, Beijing 100871，China
Abstract：Hyperspectral remote sensing, known as the state-of-the-art technology in the field of remote sensing, can be used to retrieve physical and chemical properties of surface objects based on the interactions between electromagnetic waves and the objects. Soil organic matter (SOM) is one of the most important parameters used in the assessment of soil fertility. Quick estimation of SOM with hyperspectral remote sensing technique can provide essential soil data to support the development of precision agriculture. The presence of external parameters, however, may affect the modeling precision, and further handicap the transferability of existing model. With the aim to study the effects of soil moisture on the Vis/NIR estimation of soil organic matter, and the capacity of direct standardization(DS)algorithm in the calibration transfer, 95 soil samples collected in the Jianghan plain were rewetted and air-dried. Reflectance of these samplesat 13 moisture levels was measured. Results show that the model calibrated using air-dried samples has the highest prediction accuracy. This model, however, was not suitable for SOM prediction of the rewetted samples. Prediction bias and RPD improved from -8.34～3.32 g·kg-1 and 0.64～2.04 to 0 and 7.01, when DS algorithm was applied to the spectra of the rewetted samples. DS algorithm has been proven to be effective in removing the effects of soil moisture on the Vis/NIR estimation of SOM, ensuring a transferrable model for SOM prediction with soil samples at different moisture levels.
