Statistical Inference Based on Sentinel-2 and in situ NDVI Measurements Using Monte Carlo Sampling

NDVI is the most used vegetation index for the monitoring of vegetation status of agricultural crops. In situ measurements are generally used for the validation of remote sensing data. However, due to various factors, the remotely sensed data may not completely agree with in situ acquired data. We computed NDVI based on Sentinel-2 data and performed in situ NDVI measurements on a specific agricultural area. We used Monte Carlo sampling for the estimation of the distribution of coincidence values, assuming either statistical independence and correlation of data. Based on the estimated distribution, we computed the probability that NDVI falls in two intervals of interest, based on a threshold widely used for the interpretation of the NDVI values.