Use of remote sensing to evaluate the effects of environmental factors on soil salinity in a semi-arid area
Authors: Pedrero Salcedo, Francisco; Perez Cutillas, Pedro; Alarcon Cabanero, Juan Jose; Vivaldi, Alessandro Gaetano
The global water crisis, driven by water scarcity and water quality deterioration, is expected to continue and intensify in dry and overpopulated areas, and will play a critical role in meeting future agricultural demands. Sustainability of agriculture irrigated with low quality water will require a comprehensive approach to soil, water, and crop management consisting of site- and situation-specific preventive measures and management strategies. Other problem related with water quality deterioration is soil salinization. Around 1Bha globally are salinized and soil salinization may be accelerating for several reasons including the changing climate. The consequences of climate change on soil salinization need to be monitored and mapped and, in this sense, remote sensing has been successfully applied to soil salinity monitoring. Although many issues remain to be resolved, some as important as the imbalance between ground-based measurements and satellite data. The main objective of this paper was to determine the influence of environmental factors on salinity from natural causes, and its effect on irrigated agriculture with degraded water. The study was developed on Campo de Cartagena, an intensive water-efficient irrigated area which main fruit tree is citrus (30%), a sensible crop to salinity. Nine representative citrus farms were selected, soil samples were analysed and different remote sensing indices and sets of environmental data were applied. Despite the heterogeneity between variables found by the descriptive analysis of the data, the relationship between farms, soil salinity and environmental data showed that applied salinity spectral indices were valid to detect soil salinity in citrus trees. Also, a set of environmental characterization provided useful information to determine the variables that most influence primary salinity in crops. Although the data extracted from spatial analysis indicated that to apply soil salinity predictive models, other variables related to agricultural management practices must be incorporated.
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