MASTER is a thermal sensor that captures 8 bands of emissivity between 4-12 μm, used to represent the proposed SBG thermal bands . The AVIRIS data was resampled to a resolution of 18 m while the MASTER data was resampled to a 36 m resolution. This paired dataset was flown over a portion of the Southern Central Valley seasonally while the state experienced severe drought effects. This unique dataset allows for study of remote sensing capabilities while also providing valuable information as to the response of crops in California to drought. The goal of this dissertation is to use data from the HyspIRI Airborne Campaign to evaluate how hyperspectral and thermal imagery can be used to improve upon current initiatives to account for and manage food and water resources in the face of a changing climate. This research will study patterns of agriculture and crop water use in the Central Valley as they shift throughout the course of an intense drought period from 2013-2015. These patterns will be investigated using imaging spectrometry from AVIRIS and thermal imaging from MASTER by mapping crops into relevant water use groups and then analyzing three indirect measures of crop water use from the imagery: choice of crop plantings, land surface temperatures, and water vapor patterns. Moreover,large plastic pots this dissertation will serve as a proof of concept for actively monitoring and measuring agriculture from space when the proposed SBG satellite is launched. In Chapter 2, I use three hyperspectral images acquired from AVIRIS over the course of the 2013-2015 drought in the Central Valley of California to both evaluate the performance of hyperspectral imagery for crop classification and to study farmer decision making with drought. A random forest classifier is run on the AVIRIS imagery to classify crops into groups of similar water use. Results are then compared to equivalent classifications using Landsat Operational Land Imager and Sentinel-2 imagery.
The results of this classification are then used to study the prevalence of crops as they change with increasing drought. Analysis highlights the economic and environmental drivers of planting decisions, and what this means for the future of California agriculture. In Chapter 3, I use spatially coincident AVIRIS and MASTER imagery from 2013, 2014 and 2015 to study the health of perennial crops over drought. First, I use a mixing model on AVIRIS imagery to decompose the scene into its fractional makeup of green vegetation , non-photosynthetic vegetation , and soil. Next, I model the expected temperature of each pixel as the fractional linear sum of its thermal components. I then calculate a thermal residual for each pixel as the difference between its measured temperature from MASTER and the modeled temperature. This method strips away thermal variability due to air temperature, time of day, fractional cover, structure, and moisture to allow for direct thermal comparisons between pixels and crop species. Thermal variability within agricultural fields is quantified and crop health is assessed. In Chapter 4, I evaluate spatiotemporal patterns of water vapor as they occur over agricultural fields in the Central Valley to evaluate the potential of this imagery to assist with agricultural applications. I use pixel-level column water vapor estimates derived from AVIRIS radiance imagery, surface characteristics obtained from AVIRIS reflectance imagery, and interpolated maps of wind to investigate relationships between the atmosphere and the surface. I propose and test a set of hypotheses for how water vapor will interact with the landscape in a diverse and complex agricultural scene at the pixel, field and scene scales. Results and analysis further knowledge of opportunities and limitations for using water vapor imagery to better understand crop water use. Although California faces substantial variability in inter annual precipitation and is accustomed to multi-year dry periods, the 2012 to 2016 drought was exceptional in its severity, and may be emblematic of greater shifts in California’s climate associated with anthropogenic warming .
Climate projections for California indicate that mean and extreme temperatures are likely to increase over the next century, which will increase the risk of experiencing future droughts of the severity of the 2012–2016 event . Future droughts will undoubtedly continue to put strain on water supplies, but the magnitude and extent to which these events impact water resources will depend not only on the characteristics of the drought, but also on the adaptive responses of people . In California, where the agriculture sector uses roughly 80% of the state’s managed water , agriculture simultaneously shows high vulnerability to a warming climate while also offering the greatest opportunity to mitigate the intensity of future drought impacts through adaptation strategies . Consequently, it is critical to study how we can monitor crop management response in real-time in order to assist with policy making during drought and analyze the ways in which the long-term sustainability of food and water security can be improved. This research used annual hyperspectral remote sensing imagery to assess the accuracy at which imaging spectroscopy can be used to map crops into categories of similar water demand and analyze changes in cropping patterns in a portion of the Central Valley. The study takes advantage of data collected over three years of a multi-year drought as a unique opportunity to measure agricultural response and adaptation in times of drought. Climate change is likely to significantly affect regional agricultural patterns and crop yields , in part due to management decisions such as fallowing fields or switching crop varieties or species . Therefore, monitoring how crop patterns change during droughts is a direct measure of adaptive response. Cropping decisions impact society in multiple ways by altering regional water requirements , food yields , economic production , and pesticide exposure . Consequently, accurate and timely crop maps are necessary to support long-term adaptation planning for a broad range of sectors, and are of use to farmers, managers, policymakers, and scientists.
Remote sensing has the potential to map crops and monitor changes in crop area more efficiently and frequently than time and labor-intensive on-the-ground crop accounting. Hyperspectral imagery, which samples hundreds of spectrally contiguous wavelengths, has the potential to identify crops at a single time point with a single sensor at higher accuracies than a broadband sensor . This ability is critical to enabling managers and scientists to stay abreast of rapidly changing planting choices and assess current risks, which is a need that current mapping initiatives with remote sensing are unable to fulfill. Most remote sensing mapping initiatives in the United States rely on satellites such as Landsat and the Moderate Resolution Imaging Spectrometer because of their large spatial and temporal coverage, ease of accessibility, and free availability . The National Agricultural Statistics Service ’s Cropland Data Layer is the most comprehensive current agricultural mapping initiative for the United States with an easily accessible crop map published at yearly intervals at a 30-m resolution . It relies on data from Deimos-1,raspberry container the United Kingdom’s Disaster Monitoring Constellation 2 , and Landsat 8 Operational Land Imager and produced an overall accuracy of 81.1% in California in 2016, with accuracies of crop groups ranging from a low of 32.8% for berries to 77.6% for forage crops. Although widely used and highly useful, the CDL has limitations concerning reproducibility and timeliness. First, by using three sensors, not all of which produce publicly available data, reproducing this map or using this methodology on a different study area or at a different time would not be possible. Furthermore, with maps published at the end of each year, the CDL does not offer near real-time or mid-growing season assessments of crop area. Another method of crop mapping uses multi-temporal MODIS imagery to classify crops using annual crop phenology for identification . These studies illustrate the ability of time series datasets to produce detailed and accurate crop classification maps at the end of an agricultural year in a single study area, but this methodology also faces challenges that hinder its practical and scientific usefulness in California. First, the spatial resolution of MODIS is not fine enough to individually classify many fields. For example, the average size of a field in the area of this study is approximately 0.2 km2 . Therefore, even at its finest resolution of 250 m, most MODIS pixels will result in mixtures of different fields or crop types, and are therefore best suited for croplands at larger scales . Second, multitemporal crop mapping is limited in its spatial scope due to a spatial variation in phenology that would decrease the accuracy if it was applied over a large spatial area . Third, the co-registration of multiples images and the need for cloud-free images create challenges for time-series analysis that single-data hyperspectral analyses do not face .
Finally, the need for multiple images throughout time obviates the ability to conduct real-time crop assessments. Hyperspectral imagery can act as a complement to these current crop-mapping initiatives, as it has the potential to identify crops at a single time point with greater accuracy than broadband sensors, and therefore can provide mid-season assessments of crop area without a yearly time-series . Discriminating crop types is challenging due to differing biophysical traits, development stages, variable management practices, regional weather and topography, and the timing of plantings . Despite these complications, various studies have successfully shown the ability to use hyperspectral imagery to classify crops and cultivars . By discriminating crop types with a single image from one time point, hyperspectral imagery can serve as a time-critical agricultural management tool, providing scientists, farm managers, and policymakers with improved information regarding the agricultural landscape and on-the-ground food and water needs. This study uses airborne hyperspectral imagery over a portion of the Central Valley to assess the accuracy of imaging spectroscopy for agricultural classifications and conducts a case study to display the utility of these classifications for analyzing changes in farming decisions. The results of this study, while limited in their spatial scope due to the use of airborne imagery, are salient in light of recently available Sentinel-2 data and the proposed HyspIRI mission, which would provide repeat, global hyperspectral imagery. In order to separate soil or fallow pixels from those of agricultural plant matter, a spectral mixture analysis was run on each of the three images to obtain fractional green vegetation cover. Multiple End member Spectra Mixture Analysis uses a linear mixture model to unmix pixels into fraction images while allowing the number and types of end members to vary on a per-pixel basis, thus better accounting for end member variability. Pixels were modeled as a mixture of green vegetation , soil, nonphotosynthetic vegetation , and shade. Image end members were chosen from each of the three images from 2013, 2014, and 2015 by selecting pixels with high overall reflectance from each of the three end member categories that were well-distributed spatially throughout the image in order to capture the variability from north to south along the flight line. A combined library of all of the chosen end members, consisting of eight NPV, 10 Soil, and 21 GV endmembers, was used for analysis in order to obtain consistent results throughout the years. MESMA was partially constrained by requiring shade fractions to vary between 0–0.8, and setting a maximum allowable root mean squared error of 0.025. The spectral mixture result was then shade normalized by dividing each non-shade component, GV, NPV, and soil by the sum total of all of the non-shade components in that pixel to obtain physically realistic fraction estimates . Only those pixels that contained 50% or more shade-normalized GV were chosen for training and validation, as this was decided as the threshold for classifying a pixel as a crop.Due to the high diversity of crop species in the Central Valley, we focused on a smaller set of crop classes that would be of the most practical use to stakeholders such as water managers, farmers, and scientists. Crops were classified into categories defined and used by the California Department of Water Resources to estimate water use . The crops within each category have similar rates of development, rooting depths, and soil characteristics, and are therefore presumed to have similar water requirements. Categories were included in the classification if they were prominent in the area, defined as ≥20 fields of that category, each of which contained ≥50% green vegetation, in the validation layers .