8
Maryam Mazidi; Mousa Hesam; Khalil Ghorbani; Chooghi Bayram Komaki
Abstract
Water stress occurs as a result of the imbalance between soil water in the root zone and plant water use, which necessitates determining the water stress index of the plant. Surface soil moisture is directly related to plant water content. Availability of satellite data has led to temporal and spatial ...
Read More
Water stress occurs as a result of the imbalance between soil water in the root zone and plant water use, which necessitates determining the water stress index of the plant. Surface soil moisture is directly related to plant water content. Availability of satellite data has led to temporal and spatial resolution of field data and offers new opportunities for monitoring crop conditions. In this research, accurate and continuous monitoring of soil moisture content, as a representative of soil moisture stress, was done with field measurements of soil moisture, and comparison with multispectral data of Landsat 9 and Sentinel 2 satellite images. The relationship between plant indices, as an independent variable, and soil surface moisture, as a dependent variable, was studied using linear multivariate regression and M5 tree regression methods. Considering the non-linearity of the relationship between soil moisture and spectral reflectance, linear multivariate regression did not show satisfactory results with coefficient of determination (R2) of 0.46 and 0.34 for Landsat 9 and Sentinel 2 satellites, respectively, as well as the root mean square error (RMSE) equal to 0.043 and 0.052. However, M5 tree regression showed more acceptable results, such that by establishing 16 and 20 regression relationships for Landsat 9 and Sentinel 2 satellites, the soil moisture was estimated withR2 of 0.70 and 0.67 and RMSE of 0.033 and 0.038, respectively. The results showed that the estimation of soil moisture with methods based on machine learning, such as the M5 model, increases the accuracy of calculations. In the M5 decision tree regression, a high number of variables does not necessarily lead to an increase in the accuracy of soil moisture estimation, and a relationship with the highest accuracy was found in the low number of variables. Therefore, the relationship obtained at the field level can be used to evaluate soil water stress and determine irrigation time in agricultural lands on a large scale, without measuring soil data.
Maryam Mazidi; Isa Maroofpour
Abstract
There are different methods for measuring soil moisture. The TDR method (Time Domain Reflectometry) is a relatively modern method in which soil water content is estimated based on the velocity of electromagnetic waves. The effect of soil compositions on calibration curve necessitates further calibration ...
Read More
There are different methods for measuring soil moisture. The TDR method (Time Domain Reflectometry) is a relatively modern method in which soil water content is estimated based on the velocity of electromagnetic waves. The effect of soil compositions on calibration curve necessitates further calibration of the instrument. The aim of the current study was to present a calibration equation for soils with five different amounts of organic matter. This study was carried out in laboratory on three soil textures i.e. light, medium, and heavy. The results showed that the moisture measured in windows of 10 NS was more accurate than that of other windows. Also, in low moisture contents, the amount of moisture measured by TDR was higher than the gravity method and the difference between the two methods increased with increase in the clay and organic matter content. With higher organic matter in soil, RMSE increased. It was shown that soil organic matter content influenced the accuracy of TDR. Analyses of variance showed significant difference between TDR and gravity method for moisture content of soils with different amounts of organic matter (P<0.05). Finally, calibration curves with high coefficients of regression were obtained for the studied soil textures.