The Polarimetric Synthetic Aperture Radar technique has provided various opportunities and challenges in agricultural activities mainly on crop management. The aim of this study is to investigate the sensitivity of 10 parameters derived from multi-temporal Sentinel-1 Synthetic Aperture Radar (SAR) data, to crop height and canopy coverage (CC) of maize, sunflower, and wheat. The correlation coefficient values indicate a high correlation for maize during the early growing stage. The coefficient determinations (R²) of 0.82 and 0.81 indicate that there is a strong relationship between the maize height and SAR parameters including VV + VH and VV, respectively. The maize CC is well correlated with VV parameter (R² = 0.73), but it is observed that at the later growing stage the correlation became weaker. This means that the sensitivity decreases with increasing vegetation cover growth. Compared to maize, the sensitivity of SAR parameters to wheat variables is often good at the early stage. However, the highest correlation with wheat height represented by Alpha (α) decomposition parameter (R² = 0.67). The sunflower height has an insignificant correlation with the majority of SAR parameters and only VH polarization shows low sensitivity (R² = 0.31). The sunflower CC shows relatively higher correlation with VV polarization (R² = 0.46) at the early stage while no considerable correlation is observed at the later stage. It is found that Sentinel-1 has a high potential for estimation of crop height and CC of the maize as a broad-leaf crop. The same is not true for sunflower as another broad-leaf crop.

In areas where groundwater overexploitation occurs, land subsidence triggered by aquifer compaction is observed, resulting in high socio-economic impacts for the affected communities. In this paper, we focus on the Konya region, one of the leading economic centers in the agricultural and industrial sectors in Turkey. We present a multi-source data approach aimed at investigating the complex and fragile environment of this area which is heavily affected by groundwater drawdown and ground subsidence. In particular, in order to analyze the spatial and temporal pattern of the subsidence process we use the Small BAseline Subset DInSAR technique to process two datasets of ENVISAT SAR images spanning the 2002–2010 period. The produced ground deformation maps and associated time-series allow us to detect a wide land subsidence extending for about 1200 km² and measure vertical displacements reaching up to 10 cm in the observed time interval. DInSAR results, complemented with climatic, stratigraphic and piezometric data as well as with land-cover changes information, allow us to give more insights on the impact of climate changes and human activities on groundwater resources depletion and land subsidence.

This study aims to test the spectral and spatial consistency of Sentinel-2 and Landsat-8 OLI data for the potential of monitoring longos forests for four seasons in Igneada, Turkey. Vegetation indices, including Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Normalized Difference Water Index (NDWI), were generated for the study area in addition to the five corresponding bands of Sentinel-2 and Landsat-8 OLI Images. Although the spectral consistency of the data was interpreted by cross-calibration analysis using the Pearson correlation coefficient, spatial consistency was evaluated by descriptive statistical analysis of investigated variables. In general, the highest correlation values were achieved for the images that were acquired in the spring season for almost all investigated variables. In the spring season, among the investigated variables, the Red band (B4), NDVI and EVI have the largest correlation coefficients of 0.94, 0.92 and 0.91, respectively. Regarding the spatial consistency, the mean and standard deviation values of all variables were consistent for all seasons except for the mean value of the NDVI for the fall season. As a result, if there is no atmospheric effect or data retrieval/acquisition error, either Landsat-8 or Sentinel-2 can be used as a combination or to provide the continuity data in longos monitoring applications. This study contributes to longos forest monitoring science in terms of remote sensing data analysis.