Investigating the Capability of DOVE Satellite Temporal Data for Mapping Harvest Dates of Sugarcane Crop Types Using Fuzzy Model

Abstract

The information generated about crop harvesting can aid several purposes, including the maximization of crop yield, minimizing crop losses, assessing quality deterioration and crop health, and studying phenology. This study aims to detect the harvesting cycle of Sugarcane-plant and ratoon and analyze the underlying trends. The agriculture domain makes use of remote sensing data extensively for applications like crop yield forecast, crop type mapping, monitoring crop patterns, etc. Sugarcane is cultivated in abundance in the Muzzafarnagar district of Uttar Pradesh, India. The two variants of sugarcane (ratoon and plant) are commonly grown in this region along with other crops like wheat, paddy, and oil seeds (sesame). To monitor the harvesting of the sugarcane crop fields, the phenology of the crop type (from germination to maturity stage) was considered as base temporal data from the DOVE sensor. The temporal Planetscope DOVE sensor base data with particular harvesting dates were used to map harvested fields of sugarcane ratoon and plants on a particular date. Modified soil adjusted vegetation index 2 (MSAVI2) and its variant class-based sensor independent modified soil adjusted vegetation index 2 (CBSI-MSAVI2) were tested to reduce spectral dimensionality and map the harvested fields on approximately a weekly basis. The harvested sugarcane ratoon and plant fields were successfully mapped using the innovative machine-learning approach with a Mean Membership Difference (MMD) value of about 0.01 and 0.02 respectively.