基于MODIS数据的福建省农作物低温监测分析与风险评估

Low Temperature Monitoring and Risk Assessment of Crops in Fujian Province Based on MODIS Data

利用MODIS数据分别对福建省地表温度和农作物用地信息进行反演,建立基于分裂窗法的福建省地表温度反演模型,构建福建省土地利用信息的专家决策树分类体系,并在Surfer和ArcGIS辅助下提出基于遥感的福建省农作物地表低温风险评估法。结果表明,基于分裂窗法的地表温度监测精度较高,达到83.56%。通过与气象站实测温度对比分析,其高低温分布趋势基本一致,并能精细反映地形条件下的温度差异,弥补了气象站数量不足的缺陷。利用专家决策树分析法灵活构造NDVI、NDWI等不同判读因子,能较准确地提取福建省农作物土地利用信息。经过归一化处理建立的福建省主要农作物用地低温风险等级分为:轻度(0.45～1.00)、中度(0.24～0.45)和重度(0～0.24),能够细致反映出福建省地表低温分布和农作物所处的风险格局,为农作物合理区划和低温灾害风险评估等提供参考依据。

The land surface temperature and cropland information of Fujian were retrieved based on MODIS data. The land surface temperature retrieval model and cropland classification system were derived respectively by using the split windows algorithm and expert decision tree method. Furthermore, the low temperature risk assessment model of crop- land was established by Surfer and ArcGIS software. The results showed that land surface temperature monitoring had high accuracy （83.56%） with the split windows algorithm. There was same in distribution of high and low temperature to observed data from meteorological stations, which could provide fine temperature difference and make up insufficient numbers of meteorological stations. The cropland information was extraeted exactly from normalized difference vegeta- tion index （NDWI） according to expert decision tree method. Three grades of low temperature risk assessment for ma- jor crops in Fujian province were divided, which included light （0. 45 - 1.00）, medium （0. 24 -0.45） and heavy （0 -0. 24） by using normalization method. The results revealed the surface low temperature layout and risk pattern of crops in detail, which could provide reference for crops regionalization and low temperature risk assessment.