With the increase of the world's population, while urbanization is increasing, agricultural lands are decreasing. Therefore, monitoring of up-to-date agricultural lands is important for agricultural product estimation. The study investigates suitability of Sentinel-2 data for the phenological stage analysis and yield estimation of sunflower plant. To this aim, fieldworks was conducted and sunflower parcels were identified in Zile district of Tokat province, Turkey which has dense sunflower production. In this study, ten Vegetation Indices (VIs) were performed by using multi-temporal Sentinel-2 data obtained during the growth stages of sunflower plant and yield estimation was obtained. As a result, the indexes obtained on 30 June, at the stage of inflorescence emergence, provided coefficient of determination (R2) higher than 0.67 and The Root Mean Square Error (RMSE) lower than 13 kg/da. Among the VIs, the best forecast obtained by NDVI (R2 = 0.74 and RMSE =10.80 kg/da) approximately three months before the harvest of sunflower.

Forests play a crucial role in carbon dioxide absorption, biodiversity preservation, and climate regulation. Timely and precise maps detailing tree cover attributes are vital tools for the fields of environmental research and natural resource management. This study addresses the challenge of accurately estimating forest canopy cover by integrating Global Ecosystem Dynamics Investigation (GEDI) and Landsat data in the Eastern Marmara Region of Türkiye. Despite the advancements in remote sensing technologies such as light detection and ranging (LiDAR) and optical sensors, and the importance of forest canopy cover in assessing forest health and carbon stocks, there is limited research on utilizing spaceborne GEDI Level 2B data for forest canopy cover mapping. Six different machine learning methods were employed, namely Classification and Regression Trees (CART), Categorical Boosting (CTB), Light Gradient Boosting Machines (LGBM), Multilayer Perceptron (MLP), Random Forest (RF), and Extreme Gradient Boosting (XGB), to generate forest canopy cover maps using Landsat 8 and 9 satellite images with a variety of vegetation indices and texture features. Model performances were evaluated using metrics such as R², Root Mean Square Error (RMSE), and Median Absolute Error (MdAE), with statistical significance assessed via Friedman and Wilcoxon signed-rank tests. The results of the tests indicated that the XGB (R² = 0.5570, RMSE = 0.1603, MdAE = 0.0885) and RF (R² = 0.5497, RMSE = 0.1617, MdAE = 0.0896) algorithms, which were trained with GEDI Level 2B data, provided greater accuracy in forest canopy cover estimation compared to the other algorithms. This study offers insight into the prediction performance of GEDI Level 2B spaceborne LiDAR data in conjunction with XGB and RF algorithms for forest canopy cover estimation and underscores the significance of integrating advanced remote sensing data for forest monitoring.