Integrating Remotely Sensed Data With an Ecosystem Model to Estimate Net Primary Productivity in East Asia
4. Input Requirements for the Model
4.1 Land Cover Types
Theoretically, the relationship between NDVI (Normalized Difference Vegetation Index) and LAI depends on the land cover types. However, Loveland et al. (1991) pointed out that traditional land cover classifications based on botanical, ecological or functional metrics may be unsuitable for LAI estimation, because these classifications are not necessarily based on NDVI-LAI considerations. For instance, if several canopies have a similar or a nearly similar NDNI-LAI relationship, information on such land covers is redundant for the estimation of LAI (Myneni et al., 1997). Therefore, Myneni et al. (1997) developed a new land cover classification for global application to estimate LAI from NDVI. They classified global land covers into six types depending on their canopy structure. The structural attributes of these land covers were parameterized in terms of variables that the radiative transfer models admit. The six land cover types are: (1) grasses and cereal crops; (2) shrubs; (3) broadleaf crops; (4) savanna; (5) broadleaf forests; and (6) needle forests. In this study, we incorporate these land cover types into our model to resolve the limitation of specific region in BEPS model.
Figure 2. Framework of estimation NPP showing the major modeling steps, the input requirements, and
the data's spatial resolutions and temporal intervals prior to simulation. (Modified from Liu et al., 1997)
4.2 Leaf Area Index
LAI is a key parameter to integrate remotely sensed data with an ecosystem model. The LAI was estimated from NOAA/AVHRR 10-day composite NDVI by using the NDVI-LAI algorithm developed by Myneni et al. (1997). Similar LAI images were calculated in this study for each 10- (or 11-) day period in 1998 by using the 10- (or 11-) day NDVI composites. Atmospheric corrections were performed to NOAA/AVHRR channel 1 and 2 using 6S code before using them to estimate NDVI. The NDVI composites were produced from single-day co-registered images by using the maximum NDVI criterion to obtain cloud-free pixels.
4.3 Daily Meteorological Data
The meteorological data required by the inputs of ecosystem model include daily maximum and minimum air temperatures, incoming short-wave radiation, precipitation, and specific humidity. These data were obtained from National Center for Atmospheric Research (NCAR), USA. The gridded data were 6-hourly forecasts by the National Meteorological Center (NMC) of NCAR, using their global spectral model (the MRF model). For the maximum and minimum air temperatures, the maximum and minimum of the four 6-hourly readings were used as the daily maximum and minimum air temperatures. For the incoming short-wave radiation and precipitation, the sums of the four 6-hourly readings were used as the daily total. For the specific humidity, the average of the four 6-hourly readings was used as the daily specific humidity. The gridded data were bilinearly interpolated for each pixel of 1km
2 to match the remote sensing data because their resolutions are so coarse (approximately 1-degree intervals).
4.4 Soil Data
Soil data (Soil Water Holding Capacity) was downloaded from a free access web site of Center for Global Environmental Research (CGER) of National Institute for Environmental Studies (NIES) (http://
www-cger.nies.go.jp/grid-j/gridtxt/grid18.html). This data set shows the global distribution of soil water holding capacity, at field capacity for the top soil (0-30cm), with a 1-degree latitude/longitude spatial resolution, and was derived from information on soil type and texture (FAO Soil Map of the World). This data was also bilinearly interpolated for each pixel of 1km
2 to match the remote sensing data.
