Soil Moisture Inversion for Winter Wheat Field Based on UAVMultispectral and Thermal Infrared Data

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Soil Moisture Inversion for Winter Wheat Field Based on UAVMultispectral and Thermal Infrared Data

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[1]ZHANG Chengcai,HOU Jiatong,WANG Rui,et al.Soil Moisture Inversion for Winter Wheat Field Based on UAVMultispectral and Thermal Infrared Data[J].Journal of Zhengzhou University (Engineering Science),2024,45(05):111-118.[doi:10.13705/j.issn.1671-6833.2024.05.002]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 45 Number of periods: 2024 05 Page number: 111-118 Column: Public date: 2024-08-08

Title:: Soil Moisture Inversion for Winter Wheat Field Based on UAVMultispectral and Thermal Infrared Data

Author(s):: ZHANG Chengcai¹; HOU Jiatong¹; WANG Rui¹; JIANG Mingliang¹; 2; ZHU Xingxing¹; 1. School of Water Conservancy and Transportation, Zhengzhou University, Zhengzhou 450001, China; 2. Institute of Farmland Irrigation of CAAS, Xinxiang 453002, China

Keywords:: soil moisture retrieval; UAV remote; multispectral; thermal infrared; random forest; drought indices; fractional vegetation cover

CLC:: TP79S152. 7

DOI:: 10.13705/j.issn.1671-6833.2024.05.002

Abstract:: The introduction of fractional vegetation cover could improve the accuracy of soil moisture inversion model to some extent, but most studies estimated fractional vegetation cover based on normalized difference vegetationindex NDVI, and without in-depth study on the impact of vegetation coverage based on other vegetation indices onthe model. Therefore, taking the winter wheat planting area in Xiping County, Zhumadian City, Henan Province asthe experimental area, based on the UAV platform with high resolution and strong mobility, the multi-spectral andTIR imaging apparatus were equipped to carry out the soil moisture inversion research of winter wheat covered surface, and to explore the changes of model accuracy after introducing different fractional vegetation cover parameters, so as to make up for the limitations of soil moisture monitoring caused by the low resolution and poor timelinessof satellite remote sensing images. The two drought indices of temperature vegetation dryness index TVDI and perpendicular drought index PDI were combined with the parameters of fractional vegetation cover estimated throughseven vegetation indices, respectively, and seven soil moisture inversion models were constructed based on the random forest algorithm, and the spatial distribution of soil moisture in the experimental area was analyzed according tothe inversion results of the optimal model. At the same time, the soil moisture inversion model TP model, whichcould integrateed TVDI and PDI indices and without introducing fractional vegetation cover, was built as the controlgroup. The results showed that the R2of the TP model was 0. 606,0. 670, the root mean square error RMSE was0. 045、0. 041 for the depths of 0 to10 cm and >10 to 20 cm respectively. The accuracy of the seven models introducing fractional vegetation cover was improved to some extent compared with that of the TP model. Among them, theR2of the optimal model TP OSAVI was improved by 0. 143,0. 158, the RMSE was reduced by 0. 7 percentage points,0. 8 percentage points respectively, compared with the TP model. It showed that the introduction of fractional vegetation cover based on the drought indices could improve the accuracy of model inversion and different fractional vegetation cover had different effects on the accuracy of the model.

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Last Update: 2024-09-02