【预售 按需印刷】Wheat Production Forecast in Himachal Pradesh Using Remote Sensing Data
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书名：Wheat Production Forecast in Himachal Pradesh Using Remote Sensing Data
定价：558.0
ISBN：9783659634000
作者：Manik Kalubarme
版次：1
出版时间：2014-11
内容提要：
This report summarizes the results of six year (1996-97 to 2002-03) of Remote Sensing (RS) based district-level wheat acreage estimates and agromet model predicted wheat yields. District-level preharvest wheat production forecast in major wheat growing districts in Himachal Pradesh was attempted under crop acreage and production estimation (CAPE) project. Single date Indian Remote Sensing Satellite digital data, coinciding with flowering to grain filling stage of wheat was analysed for wheat acreage estimation using maximum likely hood (MXL) supervised classification and district boundary mask approach. Agrometeorologial yield models were developed for wheat yield forecast using meteorological data such as minimum-maximum temperatures, rainfall and sunshine hours. The meteorological indices viz. Growing Degree Days (GDD), Temperature Difference (TD), Accumulated Rainfall (ARF) and Accumulated Sunshine Hours (ASH) were computed and used in model development. This report also brings out problems in wheat acreage estimation in hilly terrains using Remote Sensing data.
定价：558.0
ISBN：9783659634000
作者：Manik Kalubarme
版次：1
出版时间：2014-11
内容提要：
This report summarizes the results of six year (1996-97 to 2002-03) of Remote Sensing (RS) based district-level wheat acreage estimates and agromet model predicted wheat yields. District-level preharvest wheat production forecast in major wheat growing districts in Himachal Pradesh was attempted under crop acreage and production estimation (CAPE) project. Single date Indian Remote Sensing Satellite digital data, coinciding with flowering to grain filling stage of wheat was analysed for wheat acreage estimation using maximum likely hood (MXL) supervised classification and district boundary mask approach. Agrometeorologial yield models were developed for wheat yield forecast using meteorological data such as minimum-maximum temperatures, rainfall and sunshine hours. The meteorological indices viz. Growing Degree Days (GDD), Temperature Difference (TD), Accumulated Rainfall (ARF) and Accumulated Sunshine Hours (ASH) were computed and used in model development. This report also brings out problems in wheat acreage estimation in hilly terrains using Remote Sensing data.
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