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NOTES

Estimating Surface Soil Moisture from Soil Color Using Image Analysis

Dep. of Water Resources Engineering, Lund Univ., Box 118, SE-221 00 Lund, Sweden
^* Corresponding author (magnus.persson{at}tvrl.lth.se)

Received 11 February 2005.

In this technical note the ability to estimate surface soil moisture () from soil color using image analysis is evaluated. Four natural soils and uniform fine sand were used. Calibration soil samples with varying from 0 to 0.40 m³ m^–3 in 0.05 m³ m^–3 increments were prepared and photographed. The variations in soil color with were investigated in both the RGB (red, green, and blue) and HSV (hue, saturation, and value) color spaces. Generally, all tested soils became darker when wetted up to a certain limit (around 0.25 m³ m^–3). However, many soils actually became lighter again at the highest levels. This was due to that some water was visible on the soil surface causing reflections. A simple linear regression model between S and V was selected to estimate from the soil color. The model performed excellent in the fine sand and in two natural soils with a root mean square error (RMSE) of 0.011 to 0.017 m³ m^–3. In the two other soils the RMSE was about 0.025 m³ m^–3. An independent validation data set was also collected for the sand. The calibrated model performed well also in the validation data set with a RMSE of 0.015 m³ m^–3. From the limited data presented in this study, it seems that the relationship between soil color and is stronger in light colored soils with low organic matter content. Some examples of practical applications of the method are also suggested in the paper.

Abbreviations: HSV, hue, saturation, and value • RGB, red, green, and blue

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