| Agricultural drought is a recurrent natural disaster,and it lasts for a long time and has no structure.There have been experienced agricultural drought events of different frequency and severity on the land all over the world,especially in the countries whose economic sources mostly depend on agricultural production.Agricultural drought not only caused directly a large area of crop yield reduction,but also caused huge losses to the social economy,seriously affected the sustainable development of agriculture and social stability.Therefore,how to effectively monitor agricultural drought and select appropriate drought monitoring methods according to local conditions has become an urgent task for drought relief departments and agricultural management departments.The agricultural drought monitoring method based on remote sensing technology is often implemented through remote sensing derived agricultural drought monitoring indiecs.It has the advantages of objectivity,timeliness and wide coverage,and it makes up for the shortage of ground stations and has been proved to be the most promising technical means in agricultural drought monitoring.However,different remote sensing derived agricultural drought monitoring indices have different spatiotemporal adaptability.The characteristics of drought occurrence and development from the remote sensing spectral information are extracted by these indices.But the characteristics of drought are affected by the underlying surface in space;it is also affected by the growth pattern of crops in different phenological periods.How to select suitable remote sensing monitoring index of agricultural drought is the basis of accurate evaluation and monitoring of agricultural drought according to different underlying surface and different crop phenology.Spectral feature matching method,multivariate statistical analysis method,fuzzy comprehensive evaluation method,principal component analysis method and artificial neural network analysis method are used to evaluate the adaptability of agricultural drought remote sensing monitoring index.However,due to the complexity of the interaction of soil,phenology,topography,climate and other spatiotemporal factors of crop growth,from the perspective of environmental dependence,the spatiotemporal adaptability and sensitivity of agricultural drought monitoring index need to be further studied.In addition,the adaptability evaluation of agricultural drought monitoring index often has some human subjectivity and empirical problems,and its objectivity and automation level also need to be improved.Based on the above problems,this paper uses multi-source remote sensing data,meteorological station measured data,soil moisture data and so on,from the characteristics of different regional underlying surface,different phenological periods of crops and comprehensive spatiotemporal factors and other aspects,using machine learning technology to study the correlation between different agricultural drought monitoring parameters and crop environmental factors.This paper proposes remote sensing derived agricultural drought monitoring methods concerning spatiotemporal multi-factor.In addition,the adaptability of these methods in different application scenarios is evaluated.The main research work and achievements are as follows:(1)An improved standardized rainfall evapotranspiration index for agricultural drought monitoring is proposed considering underlying surfaces.In view of the obvious spatial-temporal difference of standardized rainfall evapotranspiration index response to drought under different regional underlying surface conditions,an improved SPEI is proposed concerning different underlying surface environmental factors,such as terrain elevation,land type(shrub,grassland,cultivated land and bare land)and other factors.At the same time,The PDSI,sc PDSI and SPI calculated from the measured rainfall,air temperature and available soil water content data are used as the benchmark to verify the method.The results show that the improved SPEI is consistent with the actual drought situation in the rain fed agricultural area of Inner Mongolia in recent 40 years;the correlation coefficients between the monitoring results and the sc PDSI and SPI(January and March scale)of 44 counties have passed the significance test.This method is more suitable for drought monitoring scenarios in rain fed agricultural areas.(2)In this paper,a method of soil moisture monitoring is proposed considering phenological periods.Soil moisture can reflect the state of crop soil water content and crop biomass,and retrieving soil moisture is very important to evaluate crop drought and growth environment.Soil moisture is a complex nonlinear coupling system,which is significantly affected by the complex soil structure and crop environment factors.How to analyze the nonlinear mapping relationship between multi-source input and output and improve the accuracy of soil moisture inversion is a problem worthy of study.The artificial neural network model can automatically analyze the nonlinear mapping relationship between multi-source input and output.Based on this,this paper proposes a remote sensing monitoring method of agricultural drought considering phenological periods,taking the turning green period of winter wheat as an example,based on MODIS drought index and radial basis function neural network method.This studies reveal that the inversion of soil moisture has a better effect in agricultural drought monitoring in Henan Province;compared with the linear model and BP neural network,the inversion of soil moisture has higher accuracy,and the regression analysis of the model has the smallest deviation compared with the 1:1 line;the average prediction accuracy of inversion is 93.27%,the correlation coefficient is 0.846,and the determination coefficient is 0.8626.The results show that the soil moisture model of Winter Wheat in green period can be effectively retrieved by using MODIS multi band drought index and radial basis function neural network.This method is more suitable for remote sensing monitoring of regional farmland soil moisture.(3)In this paper,a remote sensing monitoring method for agricultural drought is proposed,which integrates spatial and temporal multi-factor.In order to solve the problem of objectivity and automation in the weight setting of comprehensive remote sensing drought monitoring index.Based on deep learning method,convolution neural network method is introduced to automatically learn the relationship and rules between spatiotemporal multi environmental factors and multi agricultural drought remote sensing monitoring parameters without explicitly defining underlying surface features.A deep learning network model(Ieci Net)for agricultural drought remote sensing monitoring and its adaptability evaluation is proposed.Compared with other machine learning models,Ieci Net model has the highest accuracy.While fitting the measured drought parameters,Ieci Net model can also automatically obtain the importance coefficient of agricultural drought monitoring index from MODIS multi-band data.Taking the importance coefficient as the weight,a composite remote sensing monitoring method of agricultural drought based on spatial-temporal multi factors is proposed.At the same time,based on soil moisture data,site drought index PDSI,sc PDSI and u SPEI,it is verified that the composite agricultural drought monitoring index had good monitoring results in different climate dry and wet regions.This finding indicates that it is a suitable method for agricultural drought monitoring scenarios with complex local underlying surface. |
PDF Full Text Request