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Daily or weekly irrigation monitoring conducted per sub-field or management zone is an important factor in vine irrigation decision-making. The objective is to determine the crop coefficient (Kc) and the leaf area index (LAI). Since the 1990s, optic satellite imagery has been utilized for this purpose, yet cloud-cover, as well as the desire to increase the temporal resolution, raise the need to integrate more imagery sources. The Sentinel-1 (a C-band synthetic aperture radar—SAR) can solve both issues, but its accuracy for LAI and Kc mapping needs to be determined. The goals of this study were as follows: (1) to test different methods for integrating SAR and optic sensors for increasing temporal resolution and creating seamless time-series of LAI and Kc estimations; and (2) to evaluate the ability of Sentinel-1 to estimate LAI and Kc in comparison to Sentinel-2 and Landsat-8. LAI values were collected at two vineyards, over three (north plot) and four (south plot) growing seasons. These values were converted to Kc, and both parameters were tested against optic and SAR indices. The results present the two Sentinel-1 indices that achieved the best accuracy in estimating the crop parameters and the best method for fusing the optic and the SAR data. Utilizing these achievements, the accuracy of the Kc and LAI estimations from Sentinel-1 were slightly better than the Sentinel-2′s and the Landsat-8′s accuracy. The integration of all three sensors into one seamless time-series not only increases the temporal resolution but also improves the overall accuracy.

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This study applies remote sensing technology to assess and examine the spatial and temporal Brightness Temperature (BT) profile in the city of Tel-Aviv, Israel over the last 30 years using Landsat imagery. The location of warmest and coldest zones are constant over the studied period. Distinct diurnal and temporal BT behavior divide the city into four different segments. As an example of future application, we applied mixed regression models with daily random slopes to correlate Landsat BT data with monitored air temperature (Tair) measurements using 14 images for 1989–2014. Our preliminary results show a good model performance with R2 = 0.81. Furthermore, based on the model’s results, we analyzed the spatial profile of Tair within the study domain for representative days.