Frequent, region-wide monitoring of changes in pasture quality due to human disturbances or climatic conditions is impossible by field measurements or traditional ecological surveying methods. Remote sensing imagery offers distinctive advantages for monitoring spatial and temporal patterns. The chemical parameters that are widely used as indicators of ecological quality are crude protein (CP) content and neutral detergent fiber (NDF) content. In this study, we investigated the relationship between CP, NDF, and reflectance in the visible–near-infrared–shortwave infrared (VIS–NIR–SWIR) spectral range, using field, laboratory measurements, and satellite imagery (Sentinel-2). Statistical models were developed using different calibration and validation data sample sets: (1) a mix of laboratory and field measurements (e.g., fresh and dry vegetation) and (2) random selection. In addition, we used three vegetation indices (Normalized Difference Vegetative Index (NDVI), Soil-adjusted Vegetation Index (SAVI) and Wide Dynamic Range Vegetation Index (WDRVI)) as proxies to CP and NDF estimation. The best models found for predicting CP and NDF contents were based on reflectance measurements (R² = 0.71, RMSEP = 2.1% for CP; and R² = 0.78, RMSEP = 5.5% for NDF). These models contained fresh and dry vegetation samples in calibration and validation data sets. Random sample selection in a model generated similar accuracy estimations. Our results also indicate that vegetation indices provide poor accuracy. Eight Sentinel-2 images (December 2015–April 2017) were examined in order to better understand the variability of vegetation quality over spatial and temporal scales. The spatial and temporal patterns of CP and NDF contents exhibit strong seasonal dependence, influenced by climatological (precipitation) and topographical (northern vs. southern hillslopes) conditions. The total CP/NDF content increases/decrease (respectively) from December to March, when the concentrations reach their maximum/minimum values, followed by a decline/incline that begins in April, reaching minimum values in July.

Soil moisture content (SMC) down to the root zone is a major factor for the efficient cultivation of agricultural crops, especially in arid and semi-arid regions. Precise SMC can maximize crop yields (both quality and quantity), prevent crop damage, and decrease irrigation expenses and water waste, among other benefits. This study focuses on the subsurface spatial electromagnetic mapping of physical properties, mainly moisture content, using a ground-penetrating radar (GPR). In the laboratory, GPR measurements were carried out using an 800 MHz central-frequency antenna and conducted in soil boxes with loess soil type (calcic haploxeralf) from the northern Negev, hamra soil type (typic rhodoxeralf) from the Sharon coastal plain, and grumusol soil type (typic chromoxerets) from the Jezreel valley, Israel. These measurements enabled highly accurate, close-to-real-time evaluations of physical soil qualities (i.e., wave velocity and dielectric constant) connected to SMC. A mixture model based mainly on soil texture, porosity, and effective dielectric constant (permittivity) was developed to measure the subsurface spatial volumetric soil moisture content (VSMC) for a wide range of moisture contents. The analysis of the travel times for GPR reflection and diffraction waves enabled calculating electromagnetic velocities, effective dielectric constants, and spatial SMC under laboratory conditions, where the required penetration depth is low (root zone). The average VSMC was determined with an average accuracy of ±1.5% and was correlated to a standard oven-drying method, making this spatial method useful for agricultural practice and for the design of irrigation plans for different interfaces.

Salinization of irrigated lands in the semi-arid Jezreel Valley, Northern Israel results in soil-structure deterioration and crop damage. We formulated a generic rule for estimating salinity of different vegetation types by studying the relationship between Cl/Na and different spectral slopes in the visible–near infrared–shortwave infrared (VIS–NIR–SWIR) spectral range using both field measurements and satellite imagery (Sentinel-2). For the field study, the slope-based model was integrated with conventional partial least squares (PLS) analyses. Differences in 14 spectral ranges, indicating changes in salinity levels, were identified across the VIS–NIR–SWIR region (350–2500 nm). Next, two different models were run using PLS regression: (i) using spectral slope data across these ranges; and (ii) using preprocessed spectral reflectance. The best model for predicting Cl content was based on continuum removal reflectance (R2 = 0.84). Satisfactory correlations were obtained using the slope-based PLS model (R2 = 0.77 for Cl and R2 = 0.63 for Na). Thus, salinity contents in fresh plants could be estimated, despite masking of some spectral regions by water absorbance. Finally, we estimated the most sensitive spectral channels for monitoring vegetation salinity from a satellite perspective. We evaluated the recently available Sentinel-2 imagery’s ability to distinguish variability in vegetation salinity levels. The best estimate of a Sentinel-2-based vegetation salinity index was generated based on a ratio between calculated slopes: the 490–665 nm and 705–1610 nm. This index was denoted as the Sentinel-2-based vegetation salinity index (SVSI) (band 4 − band 2)/(band 5 + band 11).

Traditional sheep and cattle grazing in natural semiarid Mediterranean, Asian and African regions is based on night corrals, where animal secretions accumulate. Lack of management and disregard for the long-term effects of using the same sites for corrals on underground soil characters may negatively affect soil values. This locally increases the content of organic matter and nutrients such as nitrogen, potassium, phosphorus and others that are stockpiled in the corrals. As these activities are long-lasting, they affect the soil parameters, leading to nutrient leakage and contamination of the upper and sub-soil surface. This alarming situation demands a technique to reveal and estimate sub-soil contamination in corrals by using the frequency domain electromagnetic method (FDEM) for measuring soil salinity. The aim of this study is to correlate electrical conductivity measurement with the FDEM to study the influence of sheep corrals on the changes within the sub-soils of corrals in the semiarid region of the northern Negev desert. The results show that a correlation was found between the laboratory soil analysis and the electromagnetic analysis in all sites. Plugot forest site results found to be anomalous indicated sub-surface conductivity resulting from the presence of the corral, with a higher conductivity value of about 230 mS/m, while no differences were found between the soil layers outside the active corral and the corral edge. High values were found in the center of the active corral: 960 mS/m by the laboratory analysis and 200 mS/m by the FDEM. The values obtained in the abandoned corral in the laboratory were about 10 times lower than those obtained from the active corral and six times lower that those found with the FDEM. At the Beit Nir site, high values were found in the center of the active corral: 300 mS/m by the laboratory analysis and 130 mS/m by the FDEM. With different sources of manure, cattle and sheep have shown similar patterns of electrical conductivity (EC) obtained in the sub-soil layers between active and abandoned corrals: high in the center and low at the edge and outside the corral and decreased with depth.

Soil subsurface moisture content, especially in the root zone, is important for evaluation the influence of soil moisture to agricultural crops. Conservative monitoring by point-measurement methods is time-consuming and expensive. In this paper we represent an active remote-sensing tool for subsurface spatial imaging and analysis of electromagnetic physical properties, mostly water content, by ground-penetrating radar (GPR) reflection. Combined with laboratory methods, this technique enables real-time and highly accurate evaluations of soils' physical qualities in the field. To calculate subsurface moisture content, a model based on the soil texture, porosity, saturation, organic matter and effective electrical conductivity is required. We developed an innovative method that make it possible measures spatial subsurface moisture content up to a depth of 1.5 m in agricultural soils and applied it to two different unsaturated soil types from agricultural fields in Israel: loess soil type (Calcic haploxeralf), common in rural areas of southern Israel with about 30% clay, 30% silt and 40% sand, and hamra soil type (Typic rhodoxeralf), common in rural areas of central Israel with about 10% clay, 5% silt and 85% sand. Combined field and laboratory measurements and model development gave efficient determinations of spatial moisture content in these fields. The environmentally friendly GPR system enabled non-destructive testing. The developed method for measuring moisture content in the laboratory enabled highly accurate interpretation and physical computing. Spatial soil moisture content to 1.5 m depth was determined with 1–5% accuracy, making our method useful for the design of irrigation plans for different interfaces.