Retrieval of Soil Water Content in Saline Soils from Emitted Thermal Infrared Spectra Using Partial Linear Squares Regression

Timely information of soil water content is urgently required for monitoring ecosystem processes and functions at various scales. Although remote sensing has already provided many practical applications of retrieving soil moisture, it is largely limited to visible/near infrared or microwave domains and few studies have ever been conducted on the thermal infrared. In addition, soil salinization in arid land further complicates the situation when retrieving soil moisture from emitted spectra. In this study, we attempt to fill the knowledge gap by retrieving the soil moisture of saline soils with various salt contents. This was based on lab-controlled experiments for spectroscopy using a Fourier Transform Spectrometer (2–16 μm). Partial least squares regression (PLSR) has been applied in analyses based on either original measured or first-order derivative spectra. The results revealed that the PLSR model using first-order derivative spectra, which had a determination coefficient (R) of 0.71 and a root mean square error (RMSE) of 3.3%, should be recommended for soil moisture estimation, judged from several statistical criteria. As thermal infrared wavelengths identified in this study are contained in several current available satellite sensors, the PLSR models should have great potential for large-scale application despite extensive validations are needed in future studies. OPEN ACCESS Remote Sens. 2015, 7 14647