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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
  1. 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.

  2. 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.

  3. Other Geographical Data

    1. 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.

    2. Cultivated area data of every province: the ctual cultivated area data from983 to 1987 were used.

    3. 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.

Prediction Model Derivation
Mathematically, if these is an arrays [x1, y1], [x2, y2], …………… [xn. Yn], the regression line is y= ax+b, then the original y and regressive value y' have the relationship: y1 + y2+……..+yn = y' 1+2' 2+…….=y' n. Using this theory, the study area was divided into several subareas, and the crop production of study area could be estimated by predicting the crop production of every subarea and add them up if only the regression line for cop production can be decided.
  1. GVI and Crop Production
    Using the average value of GVI from July to September, the study area were classified into 4 categories such as forest, agricultural area, grass land, water or desert by using the threshold classification method. For those agricultural areas of every province, the GVI volume of different time duration were calculated, which can be expressed as follows : GVIVLMK: SIGMA (AREA) (SIGMA (TIME) PIXGVI) * PIXAREA……….(1) Where, GVIGLM: GVI volume of agricultural area of certain time duration. PIXGVI: the monthly maximum value of GVI of a certain agricultural pixel. PIXAREA; the area of the same pixels. GVI data were usually considered as to represent the greenness of ground vegetation cover, therefore the GVI volume should be able to represent the amount go green biomass of ground vegetation cover, and the crop production should be related to the amount of green biomass in agricultural area. Since the original data had low resolution and the classification were carried out without ground truth data, the result of classification can show the potential agricultural area but it can't represent the accurate cultivated area. In order to get better result, the GVI volume of agricultural are calculated former was calibrated using the area ratio between classified agricultural area and real cultivated area. GVIVLM (MIDIF): Calibrated GVI Volume AREAR: Cultivated area AREAC: Classified agricultural area The relationship between the calibrated GVI volume of different time duration and crop production of six province in the study area were studies and it was found that the crop production was highly related to the GVI volume of different time duration with all the correlation coefficient more than 90%. Fig. 1 shoes the relationships between the GVI volume from April to August and the crop production of that year.

  2. Weather Affection

    3. From Fig. 1 it can be seen that the crop production has direct ratio with the calibrated GVI volume but the slop and segment of the linear equation for every year are different. Fig. 2 and Fig. 3 show the annual change of these tow values. If the straight line of GVI and crop production for certain year can be decided, the crop production of that year can be obtained. The weather affection was considered as the most important factors to decide the linear equation.

    Average temperature, rainfall and aridity of different time duration were calculatied on province level. In order to reflect the range of affection of weather data, weight method was used. The weight of a province was decided by dividing the cultivating area of all six provinces. Table 1 shows the weight values can be used for prediction even without the cultivating area data of that year. The average temperature, rainfull and aridity of every province were multiplied by the weight of that province and added up to be the average rainfall and temperature of the study area of that year, and the relationship between these weighted weather factors and the coefficient of the crop-GVI linear function were studied. As the results, it has been fount that the weighted average temperature from June to August highly related both to the slope and segment with correlation coefficient 94% and 80% ( see Fig. 4 and Fig. 5 ). Therefore, It's possible to decide the crop-GVI equation by observing temperature from June to August.

  3. Prediction Processing

    4. Using the results obtained up to now, the crop production prediction of the study ara can be carried out as follows:

    Step 1: Collecting weather data from June to August and using the weight value in Table1, calculated the average temperature of this time duration, and decide the crop- GVI equation according to following equations:

    SLOPE = 0.03623 * t - 0.06988------------------------(3)

    SEGMENT = -127.6 * t + 2701.6------------------------(4)
    where,

    t : weighted temperature

    The crop-GVI equation is:

    Crop = SLOPE * GVIVLM + SEGMENT------------------(5)

    Step 2: Calculate the GVI volume using equation (1) from June to August for every province and using the ratio of cultivating area and agricultural area, calibrate the calculated GVI volume using equation. (2)

    Step 3: Add up the caliberated GVI volume of every province and introduce it to equation (5) to get the crop production prediction value of study area.
Conclusions
Fig. 6 shows the real and estimated value of crop production of study area. The average accuracy is approximately 95%.



The results obtained upto now show the possibility of using NOAA GVI data and weather data for large scale crop production prediction. Study area can be divided into several subareas and by deciding the regression line for crop production to GVI volume, the crop production of every subarea can be obtained. The summation of these predicted value forms the prediction value of the study area. The regression line can be determined by weather factors. The study should be improved in following aspects 1) the possibility of earlier prediction should be studies 2) Find out better correlationships which was used to decide the GVI crop regression line.

References
  1. Satellite Data Division, USA, Global Vegetarian Index User's guide

  2. Xuemei Bai and Shunji Murai, (1989), "Regional Environment Evaluation of Changjiang and Huang - Huai River Catchment Area in China using global vegetation index and weather data" the 10th Asian conference on Remote Sensing, Nov, 23-29 1989, Kualalumpur, Malaysia.

  3. Shunji Murai and Xuemei Bai, (1990), Habitability anaysis in China Using Global Vegetation Index, The 2nd GIS workshop, Beijing, Aug. 6-8 1990, Beijing China.