Study On Risk Zoning And Monitoring The Chilling Damage Of Rice In Northeast China By GIS And Remote Sensing

Chilling damage(also called as cold damage or chilling injury), which will occur when there is lack of accumulated temperature during the growing season or consecutive extreme low temperature below the optimum temperature at key development stage (booting, heading or flowering), is a main agro-meterological disaster for thermophilic crops in regions with low heat accumulation. Some studies have stated that Chilling damage is still a significant challenge for paddy rice in northeast China in the future. Therefore, to monitor and forecast the distribution and intensity of rice chilling damages timely and accurately is not support the local economic development but also stabilize the grain production safety. It must be the development trend for the agro-meteorological services system to using the advancemed technology such as Geographic Information System (GIS) and remote sensing (RS) to dynamically and preciously monitor the agro-meteorological environment and agricultural production at the regional scale in the future. There are several studies focusing on the freeze injury monitoring using remotely sensed data, while few application for chilling damage.In this paper, northeast China is selected to be study region since it is one of important production base for commodity rice but frequently occued low temperature weather in rice growth season.The main contents of the current study are including the risk assessment and risk zoning of rice chilling damage based on GIS, methodology to estimate the air temperature (Ta) under the all sky condition using Terra and Aqua moderate resolution imaging spectroradiometer (MODIS) Land surface temperature (LST)data, monitoring the delayed-type chilling damage of rice based on the all sky Ta data estimated from MODIS LST. and predicting the rice production losses due to chilling damage by combining the identification of planting area and key development stages.The main research achievements in this dissertation were as follows: (1) On the basis of theory of risk evaluation for natural disaster, data including daily mean air temperature, development stages, yield and planting area of rice were employed to analyze the hazard of climate factors, rice vulnerability and yield losses in chilling damage risk in Northeast China. When after determining the integrated weight for each assessment indicator by using the combination of the Entropy Method and Analytic Hierarchy Process (AHP), a comprehensive risk assessment index of rice chilling damage and its zoning were established by weighted comprehensive analysis method (WCA) method. The result of risk assessment index and risk zoning was validated quantitatively and qualitatively in two ways:1) analyzing the correlation between integrated index of chilling damage risk and average reduction rate of rice yield in chilling damage years, which showed a significant correlation at0.01level;2) comparing the risk zoning and the distribution of occurrence frequency for any typed chilling damage, which reveals a strong spatial consistency. In conclusion, our method was proved to be scientific and reasonable to support the prevention and mitigation of chilling damage in northeast China.(2) Numerous studies have developed frameworks to estimate near-surface air temperature from remote sensing for clear sky condition, which can not satisfy the need of data integrity in studies on low temperature disaster monitoring. To solve this critical problem in actual application, a novel spatio-temporal algorithm to estimate air temperature under the all sky condition from Terra and Aqua MODIS LST data is presented in current paper. Firstly, stepwise regression analysis was employed to estimate the four images of mean Ta with daytime and nighttime LST from two satellite platform and images of other factors for clear sky condition respectively.When after merging the four clear sky Ta images onto one Ta image, to estimate the cloudy sky Ta by applying a spatial interpolation algorithm based on the linear regression relationship between Ta and elevation in local sliding window. The methodology that applying the spatio-temporal algorithm in LST pixels under the all sky condition is determined to be the optimal method to estimate the mean air temperature under the all sky condition.The result reveals that. The root mean square errors (RMSE) of8-day mean MODIS_Ta under the clear, clouy and all sky condition comparing to ground-based measurements are1.4-1.8℃,1.6-2.3℃and1.4-2.0℃, respectively. There are over90%of all mean air temperature estimations under the all sky condition.were within3℃absolute bias of12years in total13years. There were big errors of MODIS_Ta estimation occurs in early spring and late autumn while quite small difference in summer days. Finally, we compared the estimation precious of monthly MODIS_Ta derived from the daily MODIS_Ta and8-day mean MODIS_Ta, and then choosed8-day mean MODIS_Ta as the optimal data source to eatimate the temperature indices of chilling damage monitoring.The spatio-temporal algorithm proposed in current paper for mean Ta is also an effective way to estimate the maximum and minimum Ta under the all sky condition.(3) According to the existing temperature indices of rice chilling damage, temperature indices with T5-9anomalies and relative accumulative growing degree day (AGDD) anomalies derived from the time series of8-day mean MODIS_Ta estimation under the all sky condition were proposed to monitor the area of chilling damage in northeast China at the pixel and county scales, respectively. The interannual variation trend of two categories of indices estimated from MODIS_Ta and ground observation are consistent.The significant sptio-temporal change of the heat accumulation in rice growing season from2000-2012are showed in forms with MODIS T5-9anomalies and MODIS rAGDD anomalies.The results reveal that it is an effective way to monitor the delayed chilling damage by using remotely sensed temperature indices when there is a wide range chilling damage occured. such as the northeast China in2003and2009. The precious of remotely sensed monitoring for moderately delayed chilling damage and seriously delayed chilling damage at the county scale are over70%and80%. respectively.(4) Taking total rainfall from May to Septermber, AGDDs over the different development stages periods and average EVI at different key development stages of rice as the predicting factors to estimate the meteorological yield by stepwise regression analysis, and then to predict the rice yield by adding the historical trend yield to the estimated meteorological yield.The result reveals that,the R2between predicting yield derived from MODIS data and actual statistics are all over the0.7at the county and prefecture levels, and the predicting precious is higher of prefecture levels than county levels.On the basis of research achievement with identification of rice key growth developments and the areas with different grades of chilling damage occurred; we used a losses evaluation model to predict the rice production losses caused by chilling damage.There are at least26.61and2.17million tons of rice production losses of northeast China in2009and2011, respectively.