Evaluation Of Habitat Suitability For Rice Sheath Blight Based On Multi-source Satellite Remote Sensing Data

The occurrence and prevalence of rice sheath blight is a serious threat to rice production.To control and prevent the disease,it is of great significance to predict the disease at a regional scale.The occurrence of diseases is not only related to weather and disease causative factors,but also related to the growth period,growth status,cultivation environment and other disease habitat differences of host crops.However,these factors are usually difficult to obtain and are rarely considered in traditional disease prediction models.As a result,disease prediction models are usually driven by point data,and it is difficult to consider the differences in habitats of different plots,which limits the guidance of the prediction results to actual disease prevention and control.In response to this problem,this study comprehensively used multiple remote sensing methods such as optics,microwave and thermal infrared,combined with field survey data at a regional scale for many years,to evaluate the habitat suitability of rice sheath blight from two aspects:1)disease host growth factors;2)disease environmental factors.The specific research content and results are as follows:(1)The extraction of host distribution area is an important background information for the evaluation of rice sheath blight habitat.In this paper,a variety of commonly used vegetation index features extracted from multi-temporal Sentinel-2 remote sensing data and their temporal features are used to construct a classification regression tree(CART)model to classify different features(rice,other vegetation,water bodies,artificial ground).and extract the rice distribution area.It has been verified that the classification accuracy of rice reaches 91.73%,which provides a spatial data basis for subsequent remote sensing monitoring and evaluation of disease habitats.(2)Rice growth period is related to the occurrence and epidemic of sheath blight.This paper uses time series Sentinel-1 data to extract a variety of backscatter coefficient feature variables,and uses independent sample T-test for feature screening,to obtain the characteristic information as input to the model.On this basis,this study uses SVM,KNN,and random forest(RF)classification methods to construct a rice growth period monitoring model,and obtain multi-phase rice growth period monitoring results.The test results show that the overall monitoring accuracy of the SVM model is 89.00%,which is higher than the KNN model(86.96%)and the RF model(70.33%).By integrating the monitoring results of the growth period of rice in multiple phases,a comprehensive description of the growth period process parameters of the phenological status of rice is proposed to provide growth period information for disease habitat evaluation.(3)The growth status of the host has an impact on the epidemic of rice sheath blight.The nitrogen level of rice has a great influence on the susceptibility of sheath blight.Therefore,this study used remote sensing data to detect the chlorophyll level of plants closely related to nitrogen.The study uses Sentinel-2 remote sensing image reflectance data and the extracted vegetation index as feature variables,build rice chlorophyll inversion models based on Gaussian process regression(GPR)and partial least squares regression(PLSR),and analyze and compare the inversion accuracy of rice chlorophyll.(4)Disease environmental factors are an important part of rice sheath blight habitat.Environmental factors affecting rice sheath blight mainly include paddy field water layer and temperature.This study uses time series Sentinel-1 satellite remote sensing image data and thermal infrared data,combined with field survey data of rice field water layer status,and uses Fisher’s discriminant method to construct a rice field water layer status monitoring model to monitor the rice field water layer status in the study area.The classification accuracy of water layer state is 81.72%.On this basis,combined with the remote sensing monitoring results of the multi-temporal paddy field water layer,a state information extraction method that comprehensively describes the water layer maintenance and survival process is further proposed.In addition,for rice field temperature,the temperature field distribution of rice field is obtained based on MODTS LST data and temperature spatial interpolation data,which provides environmental information for disease habitat evaluation.(5)This study comprehensively used remote sensing to extract the habitat factors of rice sheath blight including host growth period,growth status,paddy field water layer status and temperature field information,and obtained geographic spatial information of habitat factors of consistent scale through spatial grid processing.On this basis,the partial least square regression(PLSR)method was used to construct a rice sheath blight habitat suitability evaluation model,and the performance of the model under different input data and factor combinations was compared and analyzed.The results showed that the accuracy of the rice sheath blight habitat suitability evaluation model based on land surface temperature(LST)and other habitat factors and the habitat extracted from it was 68.05%,which was higher than the model that combined surface temperature and air temperature with other habitat factors.