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Development of Machine Learning Based Approach for Computing Optimal Vegetation Index with The Use of Sentinel-2 And Drone Data

Satellite imagery has been used in most of the applications to estimate, monitor and predict various situations and hurdles in the current scenario. According to the application, the satellite data is chosen on the basis of its spatial, spectral and temporal characteristics. From agriculture to the road highways construction and their health monitoring, it is playing a major role by providing its features details from a high level to medium and low level. In the context of agriculture, there are many ways to find crop health like visual interpretation, health parameter, existing vegetation indices, etc. For the purpose of monitoring and predicting, we require band values from the satellite data which may lack in providing accurate values because of the several parameters that affect it by which there is a possibility of error because they have to be calibrated. Therefore in this paper, a machine learning based calibration approach is proposed by considering multispectral drone data as in-situ data. The methodology is developed and tested for Sentinel-2 data. It is observed that the proposed calibration technique has good potential to calibrate satellite data.