Development of methodology to estimate regional net primary productivity (NPP) using modified Monteith model

开发使用修正的 Monteith 模型估算区域净初级生产力 (NPP) 的方法

基本信息

批准号：
12660020
负责人：
TSUNEKAWA Atsushi
金额：
$ 2.62万
依托单位：
THE UNlVERSITY OF TOKYO
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
2000
资助国家：
日本
起止时间：
2000 至 2002
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-12660020/
关键词：
net primary productivity PAR biomass spectral vegetation index photosynthesis photon sensor thermal emission sensor radiation use efficiency

项目摘要

lt was attempted to identify the potential area affected by desertification using the data of year 2000 in Asia.Methods : I estimated actual NPP using improved version of CASA model to adjust dry land vegetation. Namely, I used fAPAR estimation by NDVI-fAPAR regression line which I obtained by my own field experiments. Soil moisture estimation model was improved by changing datasets used from monthly to daily one. Chikugo model was applied to estimate climatologically potential NPP from climatologic datasets. The datasets used are as follows ; GPCC precipitation. NCEP/NCAR Global Reanalysis Products for temperature and PAR, FAO soil map for soil texture, and NOAA/AVHRR USGS 1km AVHRR Global Land Dataset for NDVI. Potential evapotranspiration was calculated using Thornthwaite's method. The vegetation type was derived from NOAA GVI. It is assumed that actual NPP would be below climatologically potential NPP in desertified areas. Thus, first, drylands was derived and then the area where actual NPP is less than potential NPP in drylands was derived and mapped (Asian vegetation degradation map).Results : Compared to soil degradation map of GLASOD, Asian vegetation degradation map corresponds well at the degraded regions from China to Central Asia. However, it seems to have a tendency to show relatively low potential NPP in high elevation areas. It is necessary to improve the method using multi-years datasets, because this analysis used only one year dataset.

LT试图使用亚洲2000年的数据来识别受荒漠化影响的潜在区域。也就是说，我使用了我自己的现场实验获得的NDVI-FAPAR回归线的FAPAR估计。通过更改每月使用的数据集，可以改善土壤水分估计模型。 Chikugo模型用于从气候数据集中估算到气候潜在的NPP。所使用的数据集如下： GPCC沉淀。 NCEP/NCAR全球重新分析产品，用于温度和PAR，粮农组织土壤图和NOAA/AVHRR USGS 1公里AVHRR全球陆地数据集用于NDVI。使用Thornthwaite的方法计算了潜在的蒸散量。植被类型源自NOAA GVI。假定在沙漠地区，实际NPP将低于气候潜在的NPP。因此，首先，得出了旱地，然后得出和映射了旱地的实际NPP小于潜在的NPP的区域（亚洲植被降解图）。消除：与Glasod的土壤降解图相比，亚洲植被降解图在从中国到中亚的地区退化。但是，似乎在高海拔地区显示出相对较低的潜在NPP的趋势。有必要使用多年数据集改进该方法，因为此分析仅使用了一年数据集。