A primary study on crop production prediction using global vegetation index
Xuemei Bai and Shurji Murai
Institute of Industrial Science
University of Tokyo
7-22 Roppongi, Minato-ku, Tokyo 106
Japan
Abstract
NOAA GVI (Global Vegetation Index) data have been considered as the index of the amount of chlorophyll of green biomass of land cover. In this study, a primary model for crop production prediction was developed by using the NOAA GVI data, weather data and other geographical data. In this model, the GVI volume, which is the product of the summation of GVI value of a certain time duration and the area, was used as the index of green biomass. Study area was divided into several subareas, and the relationship between GVI volume and production of these subareas every year was approximated by a straight line. The coefficients of the linear function was determined by the weather data. Once the linear function is determined, the crop production of the study are can be predicted by adding up the estimated crop production of every subarea. The GVI data sand weather data needed were both only from June to August. Huagn-Huai River catchment area were chosen as study area including six provinces (Hebei, Shanxi, Shanxi, Gansu, Shandong, Henan) and relatively satisfactory results were obtained.
Introduction
Recently, research on crop production prediction using satellite remote sensing data has become very important. Land sat data is most widely used for this purpose. However, for large scale prediction, NOAA GVI data have better characteristics compared with other satellite data. Previous studies by the authors showed that there exist strong relationships between NOAA GVI data, weather data to predict crop production. The study area includes six provinces like Hebei, Henal Shanxi1, Shanxi2, Gansu, Shandong, which is located in Huang-Guai River catchments area, in the central part of china and inside the study area the combination of cultivating crop is correlation between GVI and crop production with all the correlation coefficient over 90% were obtained. The weather affections on the annual change of this linear relationship were studied and a preliminary prediction model was developed.
Brief description of data
- Monthly maximum value of GVI (global Vegetation Index):
The original
data are hemispheric pairs of Polar -stereographic arrays where each
hemispheric arrya is 1024 by 1024 pixels in size with weekly maximum
value of GVI all over the world (1). From original images, monthly
maximum value component of GVI were made firstly, and resampled to new
images which cover the areas from longitude 70 to 140 degree East and
from latitude 10 to 70 degree north by the size of 512 by 480 pixels.
Longitude latitude projection were used for the convenience of are
calculation.
- Monthly average value of temperature, total rainfall of every month:
The weather data were provided by the Meteorological
Agency of Japan. From all 2 thousand weather observation stations all
over the world, those stations that located in study area ere chosen and
the rainfall and temperature data were studies. The average temperature
and rainfall were calculated for every province by taking the average
value of the data of weather stations that located in that province.
This calculation was carried out for different time durations like April
to August, June to August and January to December.
- Other Geographical Data
- Crop production data of every province
in study area: The crop production data in terms of the total crop
production of every province of the study are from 1983 to 1987 were
used.
- Cultivated area data of every province: the
ctual cultivated area data from983 to 1987 were used.
- Map of river system in China: Political
boundary map of china was scanned geometrically corrected, resampled
to the same size as new images and overlaid with them to decide the
study area boundary on images.
