APPLICATION OF SATELLITE BASED REMOTE SENSING TO THE ESTIMATION AND MONITORING OF CROP HEALTH
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- NGN 3000
ABSTRACT
The effect of loss in the availability of farm products has a lot of negative impact on the society. The decrease in crops production has created disparity between the food demand of world population and the global agricultural output. Crop production faces alot of challenges, some of which includes water scarcity, bad soils, unsuitable temperatures, pests, diseases and weeds which attacks the crops. Ground based or manual agricultural approach to detecting pest invasion and rapidly curbing it is not only time consuming and laborious, it is not also a real-time option especially for large scale farmlands. Remote sensing provides a rapid, intrusive and a more viable option for the collection and analysis of spectral properties of earth surfaces from various distances, ranging from satellites to ground-based policy. This study is aimed at assessing the applicability of remote sensing instrumentation and its techniques in evaluating and estimating crop health. This is to boycott the long process, time consuming and expensive biological laboratory tests always carried out by agricultural scientists to estimate the same attribute of crops. Sentinel-2A images and Landsat 8 images were acquired for use in this study. The total area covered for this research is101 Hectares. These images were preprocessed using ArcGIS software in order to remove effects of atmospheric conditions on the reflectance properties of the image channels. The images were processed to produce several representations of vegetation indices, soil indices and tasseled cap indices. Correlation, regression and analysis of variance (ANOVA) statistical tools were employed to assess the agreement of these vegetation and soil indices and their correlation with that of the tasseled cap indices. On the other hand, laboratory tests were performed to assess sampled crops and estimate their health status. A PCA model was developed to convert the laboratory test results to an equivalence of the remote sensing NDVI, a de-facto vegetation index for assessing crop health. Statistical analysis was performed to evaluate the relationship between the outputs from the remote sensing approach and the agricultural approach of crop health estimation, and the result shows a very weak correlation (0.16) between the two techniques. This implies that on general consideration of crop health estimation, there are just 1.6% (approximately negligible) chances that the result from the remote sensing technique will give equivalence to that of the laboratory result in agriculture. Statistical and graphical analysis performed based on each crop species reveal that cassava gave a 48.8% similarity with that of laboratory, 50.2% for groundnut and 63.8% for maize similarity respectively for two techniques, for rice health status, the study found out that the remote sensing technique could give 23.9% similar to that of laboratory. This makes it unreliable for such approach to be used in estimating maize health analysis. The result also found out that when satellite images are employed for estimating soya beans health, the output will be negatively correlated with that of the laboratory. For yam specie, the results show that there is no any correlation between the results from the laboratory and that of the remote sensing (0.021). Therefore, attention should be focused on the northern region of the study area for cultivation. Also, result obtained from this study should be further validated in order to establish a more valid PCA model for the study variables.
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