Estimating maize production in Kenya using NDVI: Some statistical considerations

International Journal of Remote Sensing
By: , and 

Links

Abstract

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.

Additional publication details

Publication type Article
Publication Subtype Journal Article
Title Estimating maize production in Kenya using NDVI: Some statistical considerations
Series title International Journal of Remote Sensing
DOI 10.1080/014311698214677
Volume 19
Issue 13
Year Published 1998
Language English
Publisher Taylor & Francis
Contributing office(s) Earth Resources Observation and Science (EROS) Center
Description 9 p.
First page 2609
Last page 2617