Prediction of plant growth based on statistical methods and remote sensing data

• Published in: Journal of applied remote sensing 2021-10, Vol.15 (4), p.042410-042410
• Main Authors: Hachicha, Marwa, Kallel, Abdelaziz, Louati, Mahdi
• Format: Article
• Language: English
• ISSN: 1931-3195; 1931-3195
• DOI: 10.1117/1.JRS.15.042410

Based on the data provided from the new satellite constellation, the crop phonology can be mapped accurately with a high spatial and temporal resolution. The potential of Sentinel-2 (high spatial and temporal resolution, 10 m and 5 days) is investigated in order to monitor and forecast the crop growth over different olive groves located in Sfax, Tunisia, from the beginning of 2016 to the end of 2020. The normalized difference vegetation index (NDVI) is used as an indicator of vegetation health due to its high correlation with crop growth health and productivity, and it is particularly forecasted to predict the trends. The prediction is done using two different statistical methods, autoregressive (AR) and Markov chain (MC), based on historical data derived from Sentinel-2 data. The prediction is applied for three different areas having various vegetation types and density. Moreover, for an accurate and precise prediction, our study areas are divided into different homogeneous clusters using the well-known Gaussian mixture model. Furthermore, the performances of our approach are assessed by means of the root mean squared error (RMSE) between the predicted results and the actual data. Globally, the obtained results for all the clusters of each area show that the forecast, using both methods, is accurate where the error is less than 5%. Nevertheless, the MC model displays the highest performance where the predicted NDVI curves of the different study areas are the closest to the actual observation. MC (resp. AR) precision is of 97% with RMSE  =  0.025 (resp. 95% and RMSE  =  0.041) for all the clusters of Jbeniana and Limaya, and 99% with RMSE  =  0.0073 (resp. 98% and RMSE  =  0.0127) for the four clusters of Chaâl.