Research On Drought Prediction Method In Corn Planting Areas Of Jilin Province Based On Remote Sensing And Hydrological Models

Agricultural drought has always been a worldwide problem,and the duration and severity of agricultural drought are on the rise at this stage.In order to solve the problems of low prediction accuracy and poor reliability of traditional meteorological prediction methods for agricultural drought,this paper took Jilin Province as the research area,discussed the agricultural drought degree indicators of corn at different growth stages based on field water stress experiments,studied the corn drought prediction model based on remote sensing and SLIM(soil and land-use based rainfall-runoff and recharge model)models,and analyzed the effectiveness and reliability of drought prediction using remote sensing data products from 2021-2022,The drought situation in the corn planting area during the growth period of 2023 was predicted using the model.The main research contents and conclusions of this paper are as follows:(1)Evaluation of drought degree indicatorsIn order to find a quantitative indicator of agricultural drought degree,experiments were designed for different water stress treatments at different growth stages of maize,with the yield and aboveground dry matter losses as the research objectives.The dry matter and yield losses at different stress levels at different growth stages of maize were calculated,and the drought degree was quantitatively analyzed.The final conclusion is that 0-20% FW at seedling stage is severe drought,20%-40%FW is moderate drought,and 40%-60% FW is light drought.The degree of drought during the jointing stage is divided into 0%-20% FW,20%-50% FW,and 50-80%FW.The degree of drought from the jointing stage to the milking stage is the same,with 0-20% FW being severe drought,20%-60% FW being moderate drought,and60%-100% FW being light drought.(2)Construction of agricultural drought monitoring model using SLIM remote sensingIn order to construct a SLIM model operating environment suitable for Jilin Province,this study collected data sets such as DEM(Digital Elevation Model),soil type,and land use type in Jilin Province.Combining DEM data to extract the catchment area of Jilin Province,soil type data to calculate the soil water capacity and soil wilting point information in various regions of Jilin Province.The soil water capacity and wilting point distribution in Jilin Province is between 0-41% and 0-36%.Combining the NDV(Normalized Digital Vegetation Index)change rules of agricultural experimental crops,the NDVI change characteristics during drought stress are summarized,Based on image NDVI data and relevant remote sensing data,an NDVI estimation model for crop coefficients suitable for various growth stages of corn in Jilin Province was derived,with the determination coefficient R of each model’s R~2 are greater than 0.53.(3)Prediction and Evaluation of Agricultural Remote Sensing in Jilin ProvinceThe agricultural drought prediction model based on SLIM and remote sensing has verified the inversion accuracy of the model with experimental data from 2012 to2022,R~2 is 0.85.Based on the collected evapotranspiration data,precipitation data,and LAI product data,the prediction model for each data is calculated through regular inversion.The average evapotranspiration,precipitation,and LAI(Leaf Area Index)data for each month in 2023 are predicted,and then substituted into the remote sensing drought prediction model.The final inversion results indicate that drought events of varying degrees will occur in the corn planting areas of Jilin Province in the coming May to September,with the drought event in May occurring in the central part of Jilin Province,The drought situation in June and August is concentrated in the northwest of Jilin Province.