Use of Satellite-based Leaf Area Index Data for Monitoring Green Gram Crop Growth in Ikombe-Katangi Area, Machakos County, Kenya

Abstract

Monitoring the conditions under which crops grow and estimating their yield is essential to the process of economic development in any nation. The traditional approaches to crop monitoring are labor- and resource-intensive, as well as limited in their ability to cover expansive geographic regions. Since remote sensing data is integrated with ground measurements, it has been used as a potentially useful tool to extract biophysical variables such as leaf area index (LAI), biomass, and phenology. Leaf Area Index (LAI) is a key variable that bridges remote sensing observations to the quantification of agroecosystem processes such as yield. The potential of satellite-based LAI for monitoring crop growth was investigated and compared to field measurements. This was the objective of his study. High resolution leaf area index was retrieved from Landsat 8 OLI imageries, and ground leaf area index measurements were taken in Ikombe-Katangi Machakos in Machakos county. An equation based on regression was developed to estimate the leaf area index using the normalized difference vegetation index that was derived from Landsat-8 OLI (NDVI). According to the findings, the derived LAI exhibited strong linear relationships with the leaf area index that was measured on the ground for green gram crops, with RMSE values being 0.09846, and R2 values of 0.9249. The overall findings shed light on the viability of using multispectral data, in estimating leaf area index in a very fragmented agricultural landscape, such as that found in the Ikombe and Katangi areas of Machakos, Kenya. Therefore, accurate crop monitoring on a large scale can be accomplished through remote sensing data in conjunction with LAI measurements taken from the ground. The implementation of the leaf area index is subject to the environmental condition which requires to be investigated as the use of remote sensing data is advocated for. The advancement in the use of satellite data for extraction LAI data is key towards transforming crop production within Kenya ad Africa at large. Scarce resources for crop growth monitoring can now be augmented with open-source satellite datasets such as Landsat 8 in LAI estimation.