Studying Vegetation Salinity: From the Field View to a Satellite-Based Perspective

Abstract

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 (R 2 = 0.84). Satisfactory correlations were obtained using the slope-based PLS model (R 2 = 0.77 for Cl and R 2 = 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).

Spatial and Temporal Monitoring of Pasture Ecological Quality: Sentinel-2-Based Estimation of Crude Protein and Neutral Detergent Fiber Contents

“…Its data is especially important for the specific characterization of spatial and temporal vegetation changes over several growing seasons, combined with in-situ field validation. Examples include: urban ecosystem service mapping, using a combination of Sentinel-1A SAR and Sentinel-2A [34], estimation of the above-ground biomass of mangrove forests [35], vegetation salinity assessment [36], monitoring of Low-Arctic Tundra vegetation [37], vegetation states indices in grassland and savanna [38], and estimation of biophysical variables in vegetation [39].…”

Sentinel-2, Herramienta en La Seguridad Agroalimentaria Y Agricultura De Precisión

“…Se sugiere que las bandas de monitoreo del agua (principalmente entre 1840 -2235 nm) son susceptibles para detectar niveles de salinidad en plantas. A medida que aumenta la concentración de Cl/Na disminuye la absorción de REM en tal intervalo, se especula que el agua decrece conforme se eleva la salinidad en las células eucariotas (Lugassi et al, 2017). Sin embargo, ello no ocurre cuando el objeto de estudio fue la conductividad eléctrica (CE) en el suelo, se atribuye a la textura del suelo y tipo de sal (Naimi et al, 2021;Wang et al, 2021).…”

Elaborating Hungarian Segment of the Global Map of Salt-Affected Soils (GSSmap): National Contribution to an International Initiative

“…Remotely sensed information, including aerial photographs, on land surface have been used for decades to study the spatio-temporal variability of salt-affected soils (e.g., [29,30,76,77]). Thus, we dedicated a separate subsection to discuss and highlight the importance of remote sensing in SAS mapping.…”

“…By the combination of certain bands of satellite images, such image indices (e.g., normalized difference vegetation index, salinity index and vegetation soil salinity index) can be gained, which provide specific information for the problem in hand. Thus, a lot of papers have addressed the issue of exploiting information acquired by remote sensing techniques in spatial modelling and inferencing (e.g., [29][30][31][32]).…”