Estimating Soil Surface Moisture Content and Investigating Irrigation Schedule of Sugarcane Fields Using Thermal Trapezoidal Model
Soil moisture is one of the key parameters in water resources studies and irrigation remote planning. Measuring soil moisture on a large scale is costly and very time consuming. Traditional methods of measuring soil moisture at farm level cannot show the spatial changes of moisture in the best way. Various new methods have been developed to use satellite data to model soil moisture based on thermal images. This study was conducted in 2020 with the aim of investigating the ability of thermal satellite imagery to estimate soil moisture and to plan irrigation rounds of lands in sugarcane industry of Amirkabir located in the south of Khuzestan province. For this purpose, during growing season of sugarcane, soil moisture content was calculated for 9 crossings Landsat 8 satellite and evaluated using 180 ground control points, and also daily irrigation data of 32 farms (25-hectare) were recorded during the study period. The results showed that the accuracy of the model is suitable for estimating soil moisture with the measured values at the farm level. The mean square root of normalized error (NRMSE) was 12.9% and the coefficient of determination (R²) was 0.82. Also, the results of soil moisture in irrigation management of sugarcane fields showed that thermal trapezoidal model is effective due to using thermal bands to environmental factors such as relative humidity percentage, average air temperature, pest (leaf dryness) and plant temperature, and somewhat in June and July causes errors in irrigation planning of sugarcane fields. The mean square root of normalized error (NRMSE) during soil water stress was 24.32%, during irrigation time was 22.20%, at average humidity was 11.7%, during high humidity was 13.20% and during irrigation was 8.86%. Consequently, the accuracy of thermal trapezoidal model for planning irrigation of farms in estimating soil water stress and field irrigation time in some periods of growing season is moderate and for fields having sufficient soil moisture is well.
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Combination of Semi-Empirical Radar Remote Sensing Models for Soil Moisture Retrieval During the Plant Growing Season Based on Machine Learning
Amir Sedighi, Saeid Hamzeh *, Seyed Kazem Alavipanah, ,
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Planning the Water requirement supply of Pasture shrubs under different Irrigation systems using plant Water Stress Index
Gholamreza Bostanian *, Mohammad Albaji, , Seed Boroomand Nasab, Naser Alemzadeh Ansari
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Evaluating Remote Sensing Technique and Machine Learning Algorithms in Estimating Sugarcane Evapotranspiration
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