Research On Diagnosis Methods Of Soil Moisture Content Of Corn In Field Based On UAV Thermal Infrared Remote Sensing

Soil moisture content is an important factor that determines crop growth and yield.Efficient and accurate diagnosis of soil moisture content has important guiding significance for agricultural production and can also provide theoretical basis for water-saving irrigation.In this paper,field corn irrigated with five water gradients?95%,80%,70%,60%,40%field water capacity?was taken as the research object.In different growth periods of corn,thermal infrared,visible and multispectral images of the test area were obtained by unmanned aerial vehicle?UAV?.Through the processing of UAV images,different methods are used to extract the relevant temperature information and spectral information of corn canopy and surface soil,and the differences of various extraction methods are compared.Based on the above results,we calculated the corn cover?f?,surface relative temperature difference?SRTD?,canopy relative temperature difference?CRTD?,crop water stress index?CWSI?based on empirical algorithm,and then calculated soil thermal inertia?P?and crop water stress index?CWSI?based on auxiliary leaf area index?LAI?,plant height,air temperature,humidity,wind speed and other meteorological data.On this basis,the ratio of crown air temperature difference to coverage?Tca/f?,water-temperature composite index?WTCI?and composite index A were constructed to diagnose soil moisture content respectively.The following results were obtained:?1?Combined with UAV visible light images to extract canopy and soil temperature from thermal infrared images,this indirect method provides a new idea for temperature extraction of low-resolution thermal infrared images in Daejeon.The detailed step is as follows:Plant soil is separated from visible light images and vector images of corn canopy or soil region are extracted,which are superimposed on thermal infrared images and masked.Through mask extraction,canopy or soil temperature information in thermal infrared images is extracted.Compared with several classification methods,GBRI index is used to classify visible light images and extract the ground object temperature in thermal infrared images,and its correlation is the highest with the measured ground object temperature.?2?During the jointing period of partial coverage,the coverage of corn at noon?12:00?14:00?in a day can predict the low and high canopy temperature of corn.Compared with the canopy air temperature difference,the ratio of canopy air temperature difference to coverage?Tca/f?has a better linear correlation with soil moisture content?SMC??R²:0.600>0.538?.Moreover,through modeling and verification of soil moisture content at different depths,it is found that the ratio of canopy air temperature difference to coverage has a higher correlation with soil moisture content at corn root zone depth?10?20cm??modeling set R² is 0.600 higher than 0.488?0?10cm?,0.290?20?30cm?,verification set R²is 0.773 higher than 0.714,0.446?.The above shows that the ratio of canopy air temperature difference to coverage?Tca/f?is more effective in retrieving soil moisture content from corn root zone depth.?3?The crop water stress index?CWSI?,canopy relative temperature difference?CRTD?,and surface relative temperature difference?SRTD?calculated by UAV thermal infrared images have certain linear correlation with soil moisture content.However,the water-temperature comprehensive index?WTCI?constructed by adding the three indexes has a higher correlation with soil moisture content.Taking the test data on July 4,2018 as an example,the determination coefficients R² are 0.731?WTCI?,0.644?CWSI?,0.619?CRTD?and 0.659?SRTD?respectively.Moreover,when corn plants are small,WTCI has a better effect on diagnosing soil moisture content at a depth of 0-20cm,for example,on July 4,2018:R² is 0.731?0-20cm?>0.500?0-20cm?;With the growth of corn,the comprehensive water-temperature index WTCI has a better effect on diagnosing soil moisture content at a depth of 0-40cm,for example,on July 12,2018:R2 is 0.660?0-20cm?<0.821?0-40cm?.?4?Under the condition of partial coverage,the calculated crop water stess index?CWSI?and the soil real thermal inertia?P?were normalized respectively to obtain the normalized crop water stress index(N_CWSI)and the normalized thermal inertia?N_P?,and then the two indexes were subtracted to construct a comprehensive index A,which was used to diagnose the surface soil moisture content of corn.The results showed that compared with N_CWSI and N_P,the correlation between normalized index NA and surface soil moisture content was higher?R²:0.576>0.475,0.508 on July 4;R²:0.838>0.476,0.514on July 8;R²:0.679>0.642,0.617 on July 12;R²:0.668>0.586,0.558 on July 17?.This shows that it is better to combine thermal inertia?P?and crop water stress index?CWSI?to diagnose corn surface soil moisture content under partial coverage conditions.