In Environmental monitoring and assessment
As the Earth's population continuously increase with the passage of time, the demand for agricultural raw material for human need increases. It is critical to maintaining updated and accurate information about the dynamics and properties of the world agricultural systems. As cash crop, the updated information of the spatial distribution of cotton field is necessary to monitor the crop area and growth changes at regional level. We used 8-day enhanced vegetation index (EVI) time series to detect cotton crop area and binomial probabilistic approach to obtain the probability distribution of cotton crop occurrence. We used Gaussian kriging to derive cotton yield inside the detected cotton crop areas through crop reporting data. We also used field data from farmers to validate the cotton yield results. A strong correlation between the MODIS-derived cotton cultivated area and statistical data at the tehsil level were achieved (R2 = 0.84) for all study years (2004-2019). The total accuracy for the cotton crop area detection was 84.6% and yield prediction was 92.1%. Our study presents new approaches to map cotton area and yield, which are applicable to other regions through machine learning.
Naveed Muhammad, He Hong S, Zong Shengwei, Du Haibo, Satti Zulqarnain, Sun Hang, Chang Shuai
2023-Feb-15
8-day MODIS EVI, Agro-ecological inference, Binomial kriging, Cotton crop, Gaussian kriging
