Integrated method for rice cultivation monitoring using Sentinel-2 data and Leaf Area Index

it is necessary to apply a remote sensing-based system for rice cultivation assessment parallel with the field measurements of the crop biophysical parameters. This study aims to map the rice cultivated areas and give an estimate for the expected yield (ton/ha) using Sentinel-2 satellite data. The study was carried out in an experimental site in the Kafr El-Sheikh governorate with a total area of 3240 ha. The multi temporal Normalized Difference Vegetation Index (NDVI) extracted from nine Sentinel-2 imagery cover the whole summer season. The supervised nearest neighborhood object-based classification method was employed, resulting in a classification map with an overall accuracy of 95% and a kappa coefficient of 0.93. Yield prediction was carried out by using an empirical yield prediction model using the NDVI and the Leaf Area Index (LAI). The LAI was calculated using the Surface Energy Balance Algorithm for Land (SEBAL) model and then validated against the measured LAI. the Mean Absolute Percentage Error (MPAE) was calculated to estimate the error between the measured and predicted LAI and yield. The MPAE was found to be +/- 6.76% (i.e. +/- 0.28 m(2)/m(2)) with a high correlation between the measured and the calculated LAI with a coefficient of determination (R-2 = 0.94). While for the yield, the MPAE was found to be +/- 6.53% (i.e. +/- 0.66 ton/ha) and R-2 of 0.95. This method is applicable to estimate area and yield of rice in the northern Nile delta in adequate time before harvest. (C) 2020 National Authority for Remote Sensing and Space Sciences. Production and hosting by Elsevier B. V.

Automated summary

This study successfully mapped the rice cultivated areas in Kafr El-Sheikh governorate using Sentinel-2 satellite data and a remote sensing-based classification method, providing an effective estimate for the expected yield. The methodology showed high accuracy and correlation between the measured and predicted parameters, demonstrating its potential application in estimating rice area and yield in the northern Nile delta before harvest.