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In California, predicted climate warming increases the likelihood of extreme droughts. As irrigated agriculture accounts for 80% of the state’s managed water supply, the response of the agricultural sector will play a large role in future drought impacts. This study examined one drought adaptation strategy, changes in planting decisions, using Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) imagery from June 2013, 2014, and 2015 from the Central Valley of California. We used the random forest classifier to classify crops into categories of similar water use. Classification accuracy was assessed using the random forest out-of-bag accuracy, and an independently validated accuracy at both the pixel and field levels. These results were then compared to simulated Landsat Operational Land Imager (OLI) and simulated Sentinel-2B results. The classification was further analyzed for method portability and band importance. The resultant crop maps were used to analyze changes in crop area as one measure of agricultural adaptation in times of drought. The results showed overall field-level accuracies of 94.4% with AVIRIS, as opposed to 90.4% with Landsat OLI and 91.7% with Sentinel, indicating that hyperspectral imagery has the potential to identify crops by water-use group at a single time step at higher accuracies than multispectral sensors. Crop maps produced using the random forest classifier indicated that the total crop area decreased as the drought persisted from 2013 to 2015. Changes in area by crop type revealed that decisions regarding which crop to grow and which to fallow in times of drought were not driven by the average water requirements of crop groups, but rather showed possible linkages to crop value and/or crop permanence.

Using Imaging Spectrometry to Study Changes in Crop Area in California’s Central Valley during Drought