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Estimating maize production in Kenya using NDVI: Some statistical considerations

January 1, 1998

A regression model approach using a normalized difference vegetation index (NDVI) has the potential for estimating crop production in East Africa. However, before production estimation can become a reality, the underlying model assumptions and statistical nature of the sample data (NDVI and crop production) must be examined rigorously. Annual maize production statistics from 1982-90 for 36 agricultural districts within Kenya were used as the dependent variable; median area NDVI (independent variable) values from each agricultural district and year were extracted from the annual maximum NDVI data set. The input data and the statistical association of NDVI with maize production for Kenya were tested systematically for the following items: (1) homogeneity of the data when pooling the sample, (2) gross data errors and influence points, (3) serial (time) correlation, (4) spatial autocorrelation and (5) stability of the regression coefficients. The results of using a simple regression model with NDVI as the only independent variable are encouraging (r 0.75, p 0.05) and illustrate that NDVI can be a responsive indicator of maize production, especially in areas of high NDVI spatial variability, which coincide with areas of production variability in Kenya.

Citation Information

Publication Year 1998
Title Estimating maize production in Kenya using NDVI: Some statistical considerations
DOI 10.1080/014311698214677
Authors J.E. Lewis, James Rowland, A. Nadeau
Publication Type Article
Publication Subtype Journal Article
Series Title International Journal of Remote Sensing
Index ID 70187672
Record Source USGS Publications Warehouse
USGS Organization Earth Resources Observation and Science (EROS) Center