Affiliation:
1. State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan 430072, China
2. Jiangxi Center Station of Irrigation Experiment, Nanchang 330201, China
Abstract
The accurate identification of the water layer condition of paddy fields is a prerequisite for precise water management of paddy fields, which is important for the water-saving irrigation of rice. Until now, the study of unmanned aerial vehicle (UAV) remote sensing data to monitor the moisture condition of field crops has mostly focused on dry crops, and research on the water status of paddy fields has been relatively limited. In this study, visible and thermal infrared images of paddy fields at key growth stages were acquired using a UAV remote sensing platform, and three model input variables were constructed by extracting the color features and temperature features of each field, while K-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), and logistic regression (LR) analysis methods were applied to establish a model for identifying the water layer presence in paddy fields. The results showed that KNN, SVM, and RF performed well in recognizing the presence of water layers in paddy fields; KNN had the best recognition accuracy (89.29%) via algorithm comparison and parameter preference. In terms of model input variables, using multisource remote sensing data led to better results than using thermal or visible images alone, and thermal data was more effective than visible data for identifying the water layer status of rice fields. This study provides a new paradigm for monitoring the water status of rice fields, which will be key to the precision irrigation of paddy fields in large regions in the future.
Funder
NSFC-MWR-CTGC Joint Yangtze River Water Science Research Project
National Natural Science Foundation of China
Subject
Agronomy and Crop Science
Reference56 articles.
1. Analysis of crop water requirements and irrigation demands for rice: Implications for increasing effective rainfall;Luo;Agric. Water Manag.,2022
2. Performance of rice (Oryza sativa (L.)) under AWD irrigation practice—A brief review;Mote;Paddy Water Environ.,2021
3. Using multimodal remote sensing data to estimate regional-scale soil moisture content: A case study of Beijing, China;Cheng;Agric. Water Manag.,2022
4. Adab, H., Morbidelli, R., Saltalippi, C., Moradian, M., and Ghalhari, G.A.F. (2020). Machine Learning to Estimate Surface Soil Moisture from Remote Sensing Data. Water, 12.
5. Multispectral and Microwave Remote Sensing Models to Survey Soil Moisture and Salinity;Periasamy;Land Degrad. Dev.,2016