Open AccessEstimating Surface Soil Moisture from Soil Color Using Image Analysis
Author(s) -
Persson Magnus
Publication year - 2005
Publication title -
vadose zone journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.036
H-Index - 81
ISSN - 1539-1663
DOI - 10.2136/vzj2005.0023
Subject(s) - soil water , hue , mean squared error , water content , soil science , environmental science , rgb color model , soil test , mathematics , geology , statistics , geotechnical engineering , artificial intelligence , computer science , operating system
In this technical note the ability to estimate surface soil moisture (θ) from soil color using image analysis is evaluated. Four natural soils and uniform fine sand were used. Calibration soil samples with θ varying from 0 to 0.40 m 3 m −3 in 0.05 m 3 m −3 increments were prepared and photographed. The variations in soil color with θ 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 m 3 m −3 ). However, many soils actually became lighter again at the highest θ 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 θ 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 m 3 m −3 . In the two other soils the RMSE was about 0.025 m 3 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 m 3 m −3 . From the limited data presented in this study, it seems that the relationship between soil color and θ 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.