Utilizing Machine Learning and time series Sentinel 1-2 Imagery to Map Rice Fields for Sustainable Agriculture using the Google Earth Engine System in Mazandaran Province, Iran

The spatial distribution of rice fields in the Mazandaran province is of utmost importance for understanding various crucial aspects such as food security, water usage, greenhouse gas emissions, and disease transmission. Agricultural irrigation plays a significant role in expanding crop lands as well. However, the availability of limited information regarding cropland areas, particularly in the Mazandaran province, is a challenge. To address this issue, we have employed a pixel-based paddy rice mapping (PPPM) algorithm to generate accurate ground truth data. This algorithm identifies flooding signals during the rice transplanting phase, providing the necessary ground truth data. We have also cross-checked the transplanting start and end times with Mazandaran's agricultural calendar for further validation. Furthermore, we have proposed a novel method that utilizes machine learning and time series Sentinel-1 and Sentinel-2 images in the Google Earth Engine system to differentiate rice fields from other types of crop lands. The proposed method involves several steps. Firstly, we have calculated various bands and essential indicators such as blue, green, red, red edge 1/2/3, NIR, nNIR bands, as well as NDVI, LSWI, DVI, RVI, WDRVI, SAVI, EVI, VARIGREEN, and GNDVI from Sentinel-2 images. Secondly, we have utilized multi-collinearity analysis to obtain the optimal indexes and bands, which include NIR, Red Edge 3, NDVI, LSWI, VARIgreen, DVI, and GNDVI. In the third step, we have generated monthly composites of the optimal indices and bands from March to August. Subsequently, we have employed the Random Forest classification algorithm to classify the study area into six classes: water, crop land, urban, forest, outcrop, and range land. Finally, we have used the Radar Vegetation Index extracted from Sentinel-1 to accurately separate the rice fields from other types of crop lands. Our approach arrived impressive results with a high accuracy and kappa coefficient of 99% and 98%, respectively. This information is crucial for policymakers and researchers as it enables them to make informed decisions concerning food security, water usage, greenhouse gas emissions, and disease transmission in the Mazandaran province. Additionally, it provides valuable insights into the expansion of crop lands through agricultural irrigation. By leveraging machine learning and satellite imagery, we are able to generate accurate and reliable information about cropland areas, facilitating the development of effective strategies for sustainable agriculture and food security.