Crop growth and yield monitoring over agricultural fields is an essential procedure for food security and agricultural economic return prediction. The advances in remote sensing have enhanced the process of monitoring the development of agricultural crops and estimating their yields. Therefore, remote sensing and GIS techniques were employed, in this study, to predict potato tuber crop yield on three 30 ha center pivot irrigated fields in an agricultural scheme located in the Eastern Region of Saudi Arabia. Landsat-8 and Sentinel-2 satellite images were acquired during the potato growth stages and two vegetation indices (the normalized difference vegetation index (NDVI) and the soil adjusted vegetation index (SAVI)) were generated from the images. Vegetation index maps were developed and classified into zones based on vegetation health statements, where the stratified random sampling points were accordingly initiated. Potato yield samples were collected 2–3 days prior to the harvest time and were correlated to the adjacent NDVI and SAVI, where yield prediction algorithms were developed and used to generate prediction yield maps. Results of the study revealed that the difference between predicted yield values and actual ones (prediction error) ranged between 7.9 and 13.5% for Landsat-8 images and between 3.8 and 10.2% for Sentinel-2 images. The relationship between actual and predicted yield values produced R² values ranging between 0.39 and 0.65 for Landsat-8 images and between 0.47 and 0.65 for Sentinel-2 images. Results of this study revealed a considerable variation in field productivity across the three fields, where high-yield areas produced an average yield of above 40 t ha^-1; while, the low-yield areas produced, on the average, less than 21 t ha^-1. Identifying such great variation in field productivity will assist farmers and decision makers in managing their practices.

Understanding the temporal and spatial variability in a crop yield is viewed as one of the key steps in the implementation of precision agriculture practices. Therefore, a study on a center pivot irrigated 23.5 ha field in Saudi Arabia was conducted to assess the variability in alfalfa yield using Landsat-8 imagery and a hay yield monitor data. In addition, the study was designed to also explore the potential of predicting the alfalfa yield using vegetation indices. A calibrated yield monitor mounted on a large rectangular hay baler was used to measure the actual alfalfa yield for four alfalfa harvests performed in the period from October 2013 to May 2014. A total of 18 Landsat-8 images, representing different crop growth stages, were used to derive different vegetation indices (VIs). Data from the yield monitor was used to generate yield maps, which illustrated a definite spatial variation in alfalfa yield across the experimental field for the four studied harvests as indicated by the high spatial correlation values (0.75 to 0.97) and the low P-values (4.7E-103 to 8.9E-27). The yield monitor-measured alfalfa actual yield was compared to the predicted yield form the Vis. Results of the study showed that there was a correlation between actual and predicted yield. The highest correlations were observed between actual yield and the predicted using NIR reflectance, SAVI and NDVI with maximum correlation coefficients of 0.69, 0.68 and 0.63, respectively.

Accurate estimation of evapotranspiration (ET) is essential for hydrological modeling and efficient crop water management in hyper-arid climates. In this study, we applied the METRIC algorithm on Landsat-8 images, acquired from June to October 2013, for the mapping of ET of a 50 ha center-pivot irrigated alfalfa field in the eastern region of Saudi Arabia. The METRIC-estimated energy balance components and ET were evaluated against the data provided by an eddy covariance (EC) flux tower installed in the field. Results indicated that the METRIC algorithm provided accurate ET estimates over the study area, with RMSE values of 0.13 and 4.15 mm d⁻¹. The METRIC algorithm was observed to perform better in full canopy conditions compared to partial canopy conditions. On average, the METRIC algorithm overestimated the hourly ET by 6.6 % in comparison to the EC measurements; however, the daily ET was underestimated by 4.2 %.

Tomato (Solanum lycopersicum L.) is an important vegetable crop that grows easily under controlled conditions, such as in greenhouses and hydroponics. To overcome freshwater scarcity, researchers are searching for alternatives to groundwater sources such as desalinated water (saline water) for irrigation. High salinity in irrigation water alters physiological functions and crop development, thereby reducing the yield. Best management practices and the use of grafted tomato plants on salt-tolerant rootstocks can alleviate salinity stress. The present study was conducted to address the impact of salinity stress on leaf transpiration (Tr), stomatal conductance (gs), photosynthesis (Pn), leaf chlorophyll content, proline content, and yield of hydroponically cultivated tomato plants. Saline (NaCl) water was used for the preparation of nutrient solution with three salinity levels, electrical conductivity (EC, dS m⁻¹) of 2.5 (control), 6.0, and 9.5. Three commercial tomato cultivars (Valouro-RZ, Ghandora-F1, and Feisty-Red) were used. Both self-rooted plants and plants grafted onto Maxifort rootstocks were transplanted onto a perlite substrate. The recorded data revealed that all studied cultivars were critically affected by higher salinity (≈ 9.5 dS m^-1) compared to low (≈ 2.5 dS m^-1) and medium (≈ 6.0 dS m^-1) salinity levels. The Variations in Tr, Pn, gs, chlorophyll content of leaf, and yield between medium and high salinity trials were reported at 3%, 5%, 9%, 5%, and 7.1%, respectively, whereas no significant differences were observed between low and medium salinities. However, at medium salinity levels, grafted plants performed better in photosynthesis than non-grafted plants. This is due to the accumulation of leaf proline, which maintains osmotic regulation and photosynthetic activity by preventing cell damage at medium salinities. Hence, this study confirmed the use of saline water for growing tomatoes under hydroponic conditions up to an EC of 6.0 dS m^-1 including the EC of nutrient fertilizers.

Characterization of soil properties is a key step in understanding the source of spatial variability in the productivity across agricultural fields. A study on a 16 ha field located in the eastern region of Saudi Arabia was undertaken to investigate the spatial variability of selected soil properties, such as soil compaction 'SC', electrical conductivity 'EC', pH (acidity or alkalinity of soil) and soil texture and its impact on the productivity of Rhodes grass (Chloris gayana L.). The productivity of Rhodes grass was investigated using the Cumulative Normalized Difference Vegetation Index (CNDVI), which was determined from Landsat-8 (OLI) images. The statistical analysis showed high spatial variability across the experimental field based on SC, clay and silt; indicated by values of the coefficient of variation (CV) of 22.08%, 21.89% and 21.02%, respectively. However, low to very low variability was observed for soil EC, sand and pH; with CV values of 13.94%, 7.20% and 0.53%, respectively. Results of the CNDVI of two successive harvests showed a relatively similar trend of Rhodes grass productivity across the experimental area (r = 0.74, p = 0.0001). Soil physicochemical layers of a considerable spatial variability (SC, clay, silt and EC) were utilized to delineate the experimental field into three management zones (MZ-1, MZ-2 and MZ-3); which covered 30.23%, 33.85% and 35.92% of the total area, respectively. The results of CNDVI indicated that the MZ-1 was the most productive zone, as its major areas of 50.28% and 45.09% were occupied by the highest CNDVI classes of 0.97-1.08 and 4.26-4.72, for the first and second harvests, respectively.