The Midwest is one of the major agricultural regions in the United States. In 2007, the Midwestern states had a market value of over $76 billion for crops (corn, soybean, wheat, and forage grass) and livestock [1
]. Currently corn grain is still the most commonly utilized feedstock for ethanol [2
]. With increased biofuel demand, food security and environmental contamination from intensified corn cropping become major concerns in this region [3
]. Perennial native prairie grasses are recognized as promising alternative energy crops for cellulosic feedstock [4
]. The USDA National Agricultural Statistics Service (NASS) publishes annual cropland data layer (CDL) products, in which annual crops are classified from satellite images at 30–56 m resolutions [5
]. Perennial crops, however, are not examined in these products. To assist with sustainable bioenergy land use, there is a need for accurate mapping of energy crops in this important agricultural region.
Remote sensing has been proven to be an effective tool of regional and global agricultural monitoring. Aside from the U.S. CDL products from medium-resolution imagery, global cropland extents and irrigated areas have been extracted from frequently observed, coarse-resolution data [6
]. A comprehensive review was presented in [8
] about studies of global croplands and their water use with remote sensing and non-remote sensing approaches by the world’s leading researchers. Yet, even with high spatial-resolution imagery, mapping of individual crop types is often limited to large fields with homogeneous structures [9
]. In complex agricultural areas with a diversity of crops, different crops often have similar spectra during the growing season. It is thus difficult to delineate crops using regular binary classifiers [10
]. For satellite images at coarse resolutions, the mixed-pixel problem also results in uncertainties in crop delineation.
In the temporal domain, seasonal variations of the normalized difference vegetation index (NDVI) are closely related to phenological features such as the onset dates of green-up, peak growth, and senescence along vegetation development [11
]. Phenology of annual crops is associated with their planting dates and development cycles in growing seasons. For example, corn is usually planted 1–2 weeks earlier than soybeans, but both have similar growth cycles along the season. Winter wheat is planted in winter and primarily grows in early spring. Spring wheat has a narrow growth cycle due to cold weather in the north. For perennial crops, cool-season grass (CSG) starts its growth in early spring and reaches peak growth in May, while warm-season grass (WSG) starts in later spring and has delayed Peak dates [12
]. In addition, CSG turns to dormancy in hot, dry summers and has a second growth peak in the fall, while WSG remains green in summer. These phenological differences provide important information for crop mapping with repeated satellite observations [13
]. Relying on these unique phenological features, multi-temporal, medium-resolution satellite imagery has been used for national mapping products such as the annual CDL maps [4
] and the National Land Cover Databases (NLCD) [16
] in the conterminous United States. Due to the tremendous amount of such satellite scenes needed in these products, the classification processes are time-consuming and labor-/cost-intensive. Moreover, limited by their coarse revisit cycles (e.g., 16-day interval for Landsat) and frequent contamination from cloud cover in the growing season, it is often difficult to extract stable phenological features from these data series for regional mapping processes.
The Moderate Resolution Imaging Spectroradiometer (MODIS) satellite product has been available since 2000. Its capabilities for daily observation and global coverage allow for efficient monitoring of seasonal crop development in large regions [17
] and operational cropland estimation [18
]. Algorithms using MODIS time series to derive phenological parameters have also been developed for crop mapping and monitoring its interannual dynamics [12
]. At 250–1000 m resolution, a MODIS pixel often covers multiple crop fields on the ground. It reduces the accuracies of crop mapping, and smaller crop clusters are often lost with conventional binary classification approaches [15
]. This may severely affect regional crop analysis that relies on the accuracies of crop delineation [19
Spectral mixture analysis (SMA) has been widely used to extract sub-pixel information of land covers based on their spectral differences [20
]. Recently, some efforts were made to extract sub-pixel land covers with crop phenology from MODIS time series [23
], the so-called phenological mixture analysis (PMA). Both methods share the same principle, i.e.
, the SMA approaches improve the spatial resolutions with spectral signature of crops, while the PMA approaches perform the same process with their temporal signatures. However, two major challenges remain: (1) appropriate selection of endmembers [20
]; and (2) identifying the correct signatures that characterize crops. Differences in spectral or temporal signatures of the same class (endmember variability) may significantly affect the accuracies of sub-pixel land cover fractions [21
]. To reduce these in-class differences, Deng and Wu [22
] developed a spatially adaptive spectral mixture analysis, in which spatial patterns were used to overcome the endmember variability in extracting sub-pixel impervious surfaces in urban lands. The idea of spatial adjustment could also be employed in crop unmixing and regional crop mapping.
This study aims to combine the phenology-based mapping and unmixing approaches to extract in-pixel fractional crop covers in the Midwest using MODIS time series in 2007. A spatially constrained PMA (SPMA) approach was developed to overcome the challenges in regular unmixing processes. The results were validated with the 56-m CDL products in the region. The extracted crop planting acreages were also compared with the county-level Crop Census records in the same year. Findings in this study provide spatially detailed information about bioenergy land use in the Midwest.