Rachel Lugassi - AIR-O Lab, Porter School of Environment and Geosciences, Faculty of Exact Sciences, Tel Aviv University, Tel Aviv 6997801, Israel; Civil Engineering Department, Ariel University, Ariel 40700, Israel.
Eli Zaady - Department of Natural Resources, Agricultural Research Organization-Volcani Center Institute of Plant Sciences, Gilat Research Center, Mobile Post Negev 2 8531100, Israel.
Naftaly Goldshleger - Civil Engineering Department, Ariel University, Ariel 40700, Israel.
Maxim Shoshany - Mapping and GeoInformation Engineering, Faculty of Civil & Environmental Engineering, Technion, Israel Institute of Technology, Haifa 32000, Israel.
Alexandra Chudnovsky - AIR-O Lab, Porter School of Environment and Geosciences, Faculty of Exact Sciences, Tel Aviv University, Tel Aviv 6997801, Israel
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.
