Regional Monitoring of Soil Moisture Content in Winter Wheat Field Based on Multi-source Remote Sensing Data and Optimal Model Selection

doi:10.11924/j.issn.1000-6850.casb2023-0687

Abstract

Abstract:

Real-time and accurate monitoring of soil moisture content is the foundation of agricultural water management. Exploring the optimal model for soil moisture inversion in winter wheat is of great significance for improving agricultural water efficiency and sustainable development. This study took the soil moisture content in the winter wheat planting area of Jun County, Hebi City, Henan Province as the research object. Using unmanned aerial vehicle remote sensing data, satellite remote sensing data and field sampling data, three methods of temperature vegetation drought index model, water cloud model and improved water cloud model were used to perform comparative analysis of soil water content inversion and optimal model selection. The results showed that the inversion accuracy at a depth of 10 cm was higher than that in 20 cm in all three methods, and R² was greater than 0.4. The use of an improved water cloud model method resulted in R² of 0.7055 and RMSE of 0.0209 at a depth of 10 cm, R² of 0.5069 and RMSE of 0.0271 at a depth of 20 cm, which was superior to the inversion effect of water cloud model and temperature vegetation drought index. This indicated that using the improved water cloud model method for wheat field soil water inversion was appropriate and had high inversion accuracy.

Key words: winter wheat, monitoring of soil moisture content, soil moisture inversion, inversion accuracy, UAV remote sensing, satellite remote sensing, temperature vegetation drought index model, water cloud model

WU Dongli, LIU Cong, GUO Chaofan, DING Mingming, WU Su, QUE Yanhong, JIANG Mingliang, LI Yan. Regional Monitoring of Soil Moisture Content in Winter Wheat Field Based on Multi-source Remote Sensing Data and Optimal Model Selection[J]. Chinese Agricultural Science Bulletin, 2024, 40(25): 147-154.

Figures/Tables 10

References 24

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样本	样本数	最大值/(cm³/cm³)	最小值/(cm³/cm³)	平均值/(cm³/cm³)	置信区间(95%)
0~10 cm土壤水分含量	25	0.40	0.32	0.36	0.35~0.37
0~20 cm土壤水分含量	25	0.38	0.33	0.35	0.34~0.36

样本	样本数	最大值/(cm³/cm³)	最小值/(cm³/cm³)	平均值/(cm³/cm³)	置信区间(95%)
0~10 cm土壤水分含量	25	0.40	0.32	0.36	0.35~0.37
0~20 cm土壤水分含量	25	0.38	0.33	0.35	0.34~0.36

深度	土壤含水量/(cm³/cm³)	面积/m²	面积占比/%	深度	土壤含水量/(cm³/cm³)	面积/m²	面积占比/%
0~10 cm	<0.34	8766.79	4.8	0~20 cm	<0.34	10337.33	5.7
	0.34~0.42	139773.81	77.8		0.34~0.40	123836.43	68.9
	0.42~0.44	15544.18	8.7		0.40~0.43	31355.89	17.5
	0.44~0.46	7185.93	4.0		0.43~0.45	7326.00	4.1
	>0.46	8396.30	4.7		>0.45	6811.36	3.8

深度	土壤含水量/(cm³/cm³)	面积/m²	面积占比/%	深度	土壤含水量/(cm³/cm³)	面积/m²	面积占比/%
0~10 cm	<0.34	8766.79	4.8	0~20 cm	<0.34	10337.33	5.7
	0.34~0.42	139773.81	77.8		0.34~0.40	123836.43	68.9
	0.42~0.44	15544.18	8.7		0.40~0.43	31355.89	17.5
	0.44~0.46	7185.93	4.0		0.43~0.45	7326.00	4.1
	>0.46	8396.30	4.7		>0.45	6811.36	3.8