Remote Sensing Soil Salinity Map for the San Joaquin Vally, California
Abstract
Soil salinization is a major natural hazard to worldwide agriculture. We present a remote imagery approach that maps salinity within a range (i.e., salinities less than 20 dS m-1, when measured as the electrical conductivity of the soil saturation extract), accuracy, and resolution most relevant to agriculture. A case study is presented for the western San Joaquin Valley (WSJV), California, USA (~870,000 ha of farmland) using multi-year Landsat 7 ETM+ canopy reflectance and the Canopy Response Salinity Index (CRSI). Highly detailed salinity maps for 22 fields (542 ha) established from apparent soil electrical conductivity directed sampling were used as ground-truth (sampled in 2013), totaling over 5000 pixels (30×30 m) with salinity values in the range of 0 to 35.2 dS m-1. Multi-year maximum values of CRSI were used to model soil salinity. In addition, soil type, elevation, meteorological data, and crop type were evaluated as covariates. The fitted model (R2=0.73) was validated: i) with a spatial k-folds (i.e., leave-one-field-out) cross-validation (R2=0.61), ii) versus salinity data from three independent fields (sampled in 2013 and 2014), and iii) by determining the accuracy of the qualitative classification of white crusted land as extremely-saline soils. The effect of land use change is evaluated over 2396 ha in the Broadview Water District from a comparison of salinity mapped in 1991 with salinity predicted in 2013 from the fitted model. From 1991 to 2013 salinity increased significantly over the selected study site, bringing attention to potential negative effects on soil quality of shifting from irrigated agriculture to fallow-land. This is cause for concern since over the 3 years of California's drought (2010-2013) the fallow land in the WSJV increased from 12.7% to 21.6%, due to drastic reduction in water allocations to farmers.
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2015
- Bibcode:
- 2015AGUFMNH43A1871S
- Keywords:
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- 4314 Mathematical and computer modeling;
- NATURAL HAZARDS;
- 4315 Monitoring;
- forecasting;
- prediction;
- NATURAL HAZARDS;
- 4337 Remote sensing and disasters;
- NATURAL HAZARDS;
- 4341 Early warning systems;
- NATURAL HAZARDS