At over 40 years, the Landsat satellites provide the longest temporal record of space-based land surface observations, and the successful 2013 launch of the Landsat-8 is continuing this legacy. Ideally, the Landsat data record should be consistent over the Landsat sensor series. The Landsat-8 Operational Land Imager (OLI) has improved calibration, signal to noise characteristics, higher 12-bit radiometric resolution, and spectrally narrower wavebands than the previous Landsat-7 Enhanced Thematic Mapper (ETM+). Reflective wavelength differences between the two Landsat sensors depend also on the surface reflectance and atmospheric state which are difficult to model comprehensively. The orbit and sensing geometries of the Landsat-8 OLI and Landsat-7 ETM+ provide swath edge overlapping paths sensed only one day apart. The overlap regions are sensed in alternating backscatter and forward scattering orientations so Landsat bi-directional reflectance effects are evident but approximately balanced between the two sensors when large amounts of time series data are considered. Taking advantage of this configuration a total of 59 million 30m corresponding sensor observations extracted from 6,317 Landsat-7 ETM+ and Landsat-8 OLI images acquired over three winter and three summer months for all the conterminous United States (CONUS) are compared. Results considering different stages of cloud and saturation filtering, and filtering to reduce one day surface state differences, demonstrate the importance of appropriate per-pixel data screening. Top of atmosphere (TOA) and atmospherically corrected surface reflectance for the spectrally corresponding visible, near infrared and shortwave infrared bands, and derived normalized difference vegetation index (NDVI), are compared and their differences quantified. On average the OLI TOA reflectance is greater than the ETM+ TOA reflectance for all bands, with greatest differences in the near-infrared (NIR) and the shortwave infrared bands due to the quite different spectral response functions between the sensors. The atmospheric correction reduces the mean difference in the NIR and shortwave infrared but increases the mean difference in the visible bands. Regardless of whether TOA or surface reflectance are used to generate NDVI, on average, for vegetated soil and vegetation surfaces (0 ≤ NDVI ≤ 1), the OLI NDVI is greater than the ETM+ NDVI. Statistical functions to transform between the comparable sensor bands and sensor NDVI values are presented so that the user community may apply them in their own research to improve temporal continuity between the Landsat-7 ETM+ and Landsat-8 OLI sensor data. The transformation functions were developed using ordinary least squares (OLS) regression and were fit quite reliably (r2 values >0.7 for the reflectance data and >0.9 for the NDVI data, p-values <0.0001).

Agricultural field size is indicative of the degree of agricultural capital investment, mechanization and labor intensity, and it is ecologically important. A recently published automated computational methodology to extract agricultural crop fields from weekly 30m Web Enabled Landsat data (WELD) time series was refined and applied to a year of Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhance Thematic Mapper Plus (ETM+) acquisitions for all of the conterminous United States (CONUS). For the first time, spatially explicit CONUS field size maps and derived information are presented. A total of 4,182,777 fields were extracted with mean and median field sizes of 0.193km2 and 0.278km2, respectively. The CONUS field size histogram was skewed; 50% of the extracted fields had sizes greater than or smaller than 0.361km2, and there were four distinct peaks that corresponded closely to sizes equivalent to fields with 0.25×0.25mile, 0.25×0.5mile, 0.5×0.5mile, and 0.5×1mile side dimensions. There were discernible patterns between field size and the majority crop type as defined by the United States Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) cropland data layer (CDL) classification. In general, larger field sizes occurred where a greater proportion of the land was dedicated to agriculture, predominantly in the U.S. Wheat Belt and Corn Belt, and in regions of irrigated agriculture. The results were validated by comparison with field boundaries manually digitized from Landsat 5 and Google-Earth high resolution imagery. The validation was undertaken at 48 approximately 7.5×7.5km sites selected across a gradient of field sizes in each of the top 16 harvested cropland areas in U.S. states that together cover 76% of harvested U.S. cropland. Conventional per-pixel confusion matrix based measures that assess pixel level thematic mapping accuracy, and object extraction accuracy measures, were derived. The overall per-pixel crop field classification accuracy was 92.7% and the overall crop field producer's and user's accuracies were 93.7% and 94.9%. Comparing all the reference and extracted field objects, 81.4% were correctly matched and the extracted field sizes were on average underestimated by 1.2% relative to the reference field objects

An automated computational methodology to extract agricultural crop fields from 30m Web Enabled Landsat data (WELD) time series is presented. The results for three 150×150km WELD tiles encompassing rectangular, circular (center-pivot irrigation) and irregularly shaped fields in Texas, California and South Dakota are presented and compared to independent United States Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) cropland data layer (CDL) classifications. Coherent fields that are visually apparent were extracted with relatively limited apparent errors of omission or commission compared to the CDL classifications. This is due to several factors. First, the use of multi-temporal Landsat data, as opposed to single Landsat acquisitions, that enables crop rotations and inter-annual variability in the state of the vegetation to be accommodated for and provides more opportunities for cloud-free, non-missing and atmospherically uncontaminated surface observations. Second, the adoption of an object-based approach, namely the variational region-based geometric active contour method that enables robust segmentation with only a small number of parameters and that requires no training data. Third, the use of a watershed algorithm to decompose connected segments belonging to multiple fields into coherent isolated field segments and a geometry-based algorithm to detect and associate parts of circular fields together. A preliminary validation is presented to gain quantitative insights into the field extraction accuracy and to prototype a validation protocol including new geometric measures that quantify the accuracy of individual field objects. Implications and recommendations for future research and large-area applications are discussed