| China is the world’s largest agricultural country with the largest population.Food security has been a potential threat to China from past to now.Winter wheat is a main food crop and plays an important role in national economy and national food security.With the rapid development of "3S" technology and the increasing requirements of the country for precision agricultural production,accurate and timely access to agricultural information such as the spatial distribution of winter wheat planting area and growth status is of great significance for government departments at all levels to formulate relevant agricultural policies.In this study,remote sensing images of Sentinel-2 at overwintering stage(2021-12-04),flowering stage(2022-04-08)and milk ripening stage(2022-05-03),GF-6 at heading stage(2022-03-11)and filling stage(2022-04-21),Landsat-8 at ripening stage(2022-05-18)of winter wheat were used as data sources.Firstly,five common supervised classification methods based on mono-temporal image and NDVI addition and subtraction method based on multi-temporal images were used to extract winter wheat planting area in Yuhang District,Hangzhou City,Zhejiang Province.The accuracy of the extraction results is evaluated through the field investigation data and the measured area of winter wheat,compared all results to obtain the best extraction method for winter wheat in Yuhang District.Then,the difference values of NDVI,GNDVI,RVI and DVI in different growth stages were calculated to monitor the dynamic growth of winter wheat.The results of growth grade were verified by the distribution of harvest winter wheat data at maturity stage,compared the results to obtain the best data source and best vegetation index for winter wheat in Yuhang District.Finally,based on the results of growth grade,GIS network analysis was used to analyze the shortest path from the planting field to the drying points of winter wheat,and the following conclusions were obtained:(1)Based on the mono-temporal image interpretation results,it were concluded that: From the data source,Sentinel-2 image was more suitable for extracting winter wheat than GF-6 image;From the perspective of winter wheat growth period,flowering period was the best time to extract winter wheat.From the classification method,support vector machine method was the best method to extract winter wheat.Therefore,selecting Sentinel-2 data and using support vector machine to classify the image of flowering stage was the best method for extracting winter wheat based on mono-temporal image,and the area accuracy is 88.01%.Compared with the mono-temporal image,using threshold value of NDVI in overwintering stage to mask vegetation areas(including Tea garden,woodland),preserve the non-vegetation areas(including buildings,water,winter wheat),and then performing sum operations on the NDVI of non-vegetation areas during flowering and milky ripening stages by sentinel-2 was the best method for extracting winter wheat based on multi-temporal images,with on area accuracy is 91.96%.The study showed that using multi-temporal images combined with the phenological characteristics of vegetation and typical ground feature types can obtain highly accurate spatial distribution information of crop planting.(2)Based on GF-6 and Sentinel-2 image growth grade monitoring results,it were concluded that: Compared with Sentinel-2,GF-6 remote sensing image was more accurate in the classification monitoring of winter wheat growth in Yuhang District.The results showed that the spatial resolution of remote sensing image more higher,the better it could reflect the actual growth of winter wheat crops.The image accuracy is higher and the grade texture of remote sensing monitoring results is more detailed,which is conducive to realizing the requirement of the precision of remote sensing monitoring of crops growth.DVI was the best vegetation index for monitoring the growth grade of winter wheat in Yuhang District.The results indicated that DVI with strong sensitivity to soil could be selected for monitoring of winter wheat growth dynamics in the late growth period of Yuhang District.In addition,it was verified that the growth grade monitoring results were consistent with the harvest distribution results of winter wheat at maturity stage.Therefore,it also indicated that the DVI difference of different growth stages of winter wheat can be used to predict the distribution of harvested and unharvested winter wheat at maturity stage.(3)Based on the monitoring results of winter wheat growth grade,through analyzing the correlation between growth grade and maturity,using GIS network analysis technology to analyze the shortest-path from early maturing,middle maturing and late maturing winter wheat planting area to the drying points.By comparing the drying route of early maturing winter wheat with that of medium maturing winter wheat,it was found that there were repeated routes.It indicated that early and medium maturing winter wheat are relatively concentrated in distribution,and unified harvesting of early and medium maturing winter wheat can be considered during harvesting.It was a good attempt to integrate remote sensing and GIS to analyze the shortest path of winter wheat drying,which not only expands the development space of GIS,but also provides decision-making basis for government departments to effectively manage winter wheat harvesting and drying. |
