| Under the background of climate change,extreme weather events are increasing,and frequent agrometeorological disasters have an increasing impact on Chinese agricultural production,seriously affecting Chinese food security.Northeast China is the main production area of spring maize,and it is also one of the waterlogging disaster prone areas.It is of great significance to strengthen the research on the monitoring of spring maize waterlogging disaster in Northeast China for disaster prevention and reduction and food security.With the rapid development of 3S technology,by means of modern technologies such as GIS and remote sensing,new methods can be provided for monitoring and assessment of waterlogging disaster.In this paper,Heilongjiang,Jilin and Liaoning provinces in Northeast China are taken as the research areas,and the methods of index model construction and remote sensing data inversion are used to analyze the climate change background of spring maize waterlogging disaster,study the monitoring method of spring maize waterlogging disaster and discuss the relationship between the degree of spring maize waterlogging disaster and different environmental factors,the technical framework of monitoring and evaluation of spring maize waterlogging disaster in Northeast China was formed.The main research contents and conclusions are as follows:(1)During the past 30 years,the annual average precipitation of spring maize growing season(April-September)in Northeast China,in terms of time change,the overall trend is slightly upward with a growth rate of 0.8mm/10 a,the M-K mutation test shows that there is no significant mutation point,in terms of spatial distribution,there are great differences,the overall distribution is 349.0-884.7mm,showing a decreasing trend from southeast to northwest,the M-K trend test shows that most areas show an upward trend,while a small part shows a downward trend,but the change trend is not significant;the SPI value shows a slight upward trend as a whole in terms of time change,the growth rate is 0.01/10 a,the M-K mutation test shows that there is no significant mutation point,the difference is small in terms of spatial distribution,and the overall range is-0.11 to 0.11,the M-K trend test shows that most areas show an upward trend,while a small part shows a downward trend,but the change trend is not significant;the waterlogging characteristics based on SPI value,in terms of time change,the total waterlogging station ratio shows an expansion trend,the expansion rate is 1.7%/10 a,among the waterlogging station ratio of different grades,the light and moderate waterlogging station ratio show an expansion trend,the severe waterlogging station to station ratio shows a reduction trend,in terms of spatial distribution,the frequency of total waterlogging is between 0%-20%,among the waterlogging frequency of different grades,the frequency of light waterlogging is the highest,the frequency of moderate waterlogging is the second,and the frequency of severe waterlogging is the lowest.The results show that since 1989-2018,there is a trend of wetting in most areas of the study area,and the risk of waterlogging in spring maize is increased.(2)Based on the Weighted Moisture Index(WMI),the Improved Weighted Moisture Index(IWMI)was constructed by the method of accumulating crop water demand instead of accumulating reference crop evapotranspiration to determine the waterlogging threshold of spring maize in Northeast China at different growth stages,and to divide the affected areas of spring maize waterlogging.On this basis,the TVDI index is constructed by selecting a more suitable characteristic space of temperature-vegetation index.Based on the TVDI index and its threshold value for the degree of waterlogging disaster,the dynamic monitoring of the degree of waterlogging disaster of spring maize is realized by using the spatial distribution data of spring maize as a mask,and the monitoring results of the degree of waterlogging disaster in the whole growth period of spring maize in 2017 and2018 in Northeast China are obtained by the maximum degree extraction method.The results showed that in 2017,the total waterlogging area of spring maize accounted for20.5% of the total area of spring maize,of which 17.4% was light waterlogging,2.2% was moderate waterlogging and 0.9% was severe waterlogging;in 2018,the total waterlogging area of spring maize accounted for 24.0% of the total area of spring maize,of which 20.5%was light waterlogging,2.1% was moderate waterlogging and 1.4% was severe waterlogging.Comparing the spatial distribution of the results of the comprehensive assessment in 2018 with the spatial distribution of the yield reduction rate of spring maize in the cities of Northeast China in 2018,it is found that the yield reduction rate of spring maize in Changchun,Baishan and Jixi where the waterlogging disaster pixels are concentrated in the results of the comprehensive assessment is 10% or more at the same time,and it is also found that the yield reduction rate of spring maize in Siping,Jilin,Daqing,Harbin,Mudanjiang,Qitaihe,Jiamusi and Shuangyashan where the waterlogging disaster pixels are concentrated in the results of the comprehensive assessment is between 5% and 10%,which verifies the accuracy of the monitoring method of spring maize waterlogging based on multi-source data.(3)The waterlogging disaster of spring corn in Northeast China is affected by many environmental factors,and its distribution area has the following characteristics: from the analysis of elevation conditions,the disaster area is mostly concentrated in the area less than or equal to 500m;from the analysis of slope conditions,the disaster area is mostly concentrated in the area less than or equal to 6°;from the analysis of soil quality conditions,the disaster area is mostly concentrated in the area where loam and clay are distributed;from the analysis of soil type conditions,the disaster area is mostly concentrated in the area where eluvial soil and semi hydric soil are distributed. |
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