Drought monitoring is the base for drought coping and adaptation. Xingtai is located in North China＇s key winter wheat production areas where drought is severe and frequent. The rainfall during winter wheat growing s...Drought monitoring is the base for drought coping and adaptation. Xingtai is located in North China＇s key winter wheat production areas where drought is severe and frequent. The rainfall during winter wheat growing season is just about 1/3 of total demand. Xingtai has typical mountainous, hilly and plain agricultural zones, compound rain-fed and irrigated farming patterns. The winter wheat irrigation has heavily depended on overdraw of groundwater in recent decades. In the study, the MODIS （Moderate-Resolution Imaging Spectroradiometer） images taken at the key winter wheat growing season （Mar. to May） in normal rainfall year （2006） were selected, extracted NDVI （Normalized Difference Vegetation Index） and LST （Land Surface Temperature） data, calculated TVDI （Temperature and Vegetation Drought Index）, classified and mapped winter wheat drought intensity. Further, based on TVDI, a CDRA （Comprehensive Drought Risk Assessment） model for winter wheat drought disaster risk assessment was constructed and zoning was made. Verified by winter wheat yield, the risk zoning by CDRA is consistent with actual crop failure space. This method can be used in drought risk management.展开更多
为探索如何利用冬小麦生长过程中的积温信息来提高遥感估产的准确性,以2009-2010和2012-2013年2个冬小麦生长季的田间试验数据为基础,利用有效积温和植被指数(NDVI)构建冬小麦当季估产指数INSEY(In-season estimate of yield)和INSEY-CG...为探索如何利用冬小麦生长过程中的积温信息来提高遥感估产的准确性,以2009-2010和2012-2013年2个冬小麦生长季的田间试验数据为基础,利用有效积温和植被指数(NDVI)构建冬小麦当季估产指数INSEY(In-season estimate of yield)和INSEY-CGDD(In-season estimate of yield-cumulative growing degree days),分别用NDVI、INSEY和INSEY-CGDD与实测产量建立估产模型,并比较分析3类估产模型的精度。结果表明,3个变量与实测产量均成指数关系,其中INSEY-CGDD模型的精度最高(R2=0.59),预测能力最优,其次是INSEY模型(R2=0.55);而NDVI模型的精度最低(R2=0.35),预测能力最差。因此,在冬小麦估产模型中引入有效积温调整参数,可有效提高遥感估产模型精度。展开更多
基金The study was supported by the National Natural Science Foundation of China [No.46171501 ].
文摘Drought monitoring is the base for drought coping and adaptation. Xingtai is located in North China＇s key winter wheat production areas where drought is severe and frequent. The rainfall during winter wheat growing season is just about 1/3 of total demand. Xingtai has typical mountainous, hilly and plain agricultural zones, compound rain-fed and irrigated farming patterns. The winter wheat irrigation has heavily depended on overdraw of groundwater in recent decades. In the study, the MODIS （Moderate-Resolution Imaging Spectroradiometer） images taken at the key winter wheat growing season （Mar. to May） in normal rainfall year （2006） were selected, extracted NDVI （Normalized Difference Vegetation Index） and LST （Land Surface Temperature） data, calculated TVDI （Temperature and Vegetation Drought Index）, classified and mapped winter wheat drought intensity. Further, based on TVDI, a CDRA （Comprehensive Drought Risk Assessment） model for winter wheat drought disaster risk assessment was constructed and zoning was made. Verified by winter wheat yield, the risk zoning by CDRA is consistent with actual crop failure space. This method can be used in drought risk management.
文摘为探索如何利用冬小麦生长过程中的积温信息来提高遥感估产的准确性,以2009-2010和2012-2013年2个冬小麦生长季的田间试验数据为基础,利用有效积温和植被指数(NDVI)构建冬小麦当季估产指数INSEY(In-season estimate of yield)和INSEY-CGDD(In-season estimate of yield-cumulative growing degree days),分别用NDVI、INSEY和INSEY-CGDD与实测产量建立估产模型,并比较分析3类估产模型的精度。结果表明,3个变量与实测产量均成指数关系,其中INSEY-CGDD模型的精度最高(R2=0.59),预测能力最优,其次是INSEY模型(R2=0.55);而NDVI模型的精度最低(R2=0.35),预测能力最差。因此,在冬小麦估产模型中引入有效积温调整参数,可有效提高遥感估产模型精度。
