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Depending on the topography and soil characteristics of an area, soil moisture, an important factor in crop productivity, can be quite variable over the land surface. Thus, a method for determination of soil moisture without the necessity for exhaustive manual measurements would be beneficial for characterizing soil moisture within a given region or field. In this study, soil surface reflectance data in the visible and near−infrared regions were analyzed in conjunction with surface moisture data in a field environment to determine the nature of the relationship between the two, and to identify potential methods for estimation of soil moisture from remotely sensed data in these wavelengths. Results indicate that it is feasible to estimate surface (0 to 7.6 cm) soil moisture from visible and near−infrared reflectance, although estimating moisture regimes rather than precise water content is perhaps more likely. Furthermore, an exponential model was appropriate to describe soil moisture from spectral reflectance data. In particular, the visible region of the electromagnetic spectrum works well with such a model. A partial least squares analysis with improved R 2 values over the single−band models indicated that mulitspectral data may add more useful information about soil moisture as compared to single−band data. The results also suggested that the performance of reflectance models for moisture estimation is a function of soil types; the estimation results were better for the lighter of the two soils in this study.

As one of the most effective measures for ecosystem remediation and climate mitigation, large scale vegetation restoration has been implemented worldwide over the past few decades (C.

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