Crop emergence is a critical stage for crop development modeling, crop condition monitoring, and biomass accumulation estimation. Green-up dates (or the start of the season) detected from remote sensing time series are related to, but generally lag, crop emergence dates. In this paper, we refine the within-season emergence (WISE) algorithm and extend application to five Corn Belt states (Iowa, Illinois, Indiana, Minnesota, and Nebraska) using routine harmonized Landsat and Sentinel-2 (HLS) data from 2018 to 2020. Green-up dates detected from the HLS time series were assessed using field observations and near-surface measurements from PhenoCams. Statistical descriptions of green-up dates for corn and soybeans were generated and compared to county-level planting dates and district- to state-level crop emergence dates reported by the National Agricultural Statistics Service (NASS). Results show that emergence dates for corn and soybean can be reliably detected within the season using the HLS time series acquired during the early growing season. Compared to observed crop emergence dates, green-up dates from HLS using WISE were ~3 days later at the field scale (30-m). The mean absolute difference (MAD) was ~7 days and the root mean square error (RMSE) was ~9 days. At the state level, the mean differences between median HLS green-up date and median crop emergence date were within 2 days for 2018–2020. At this scale, MAD was within 4 days, and RMSE was less than 5 days for both corn and soybeans. The R-squares were 0.73 and 0.87 for corn and soybean, respectively. The 2019 late emergence of crops in Corn Belt states (1–4 weeks to five-year average) was captured by HLS green-up date retrievals. This study demonstrates that routine within-season mapping of crop emergence/green-up at the field scale is practicable over large regions using operational satellite data. The green-up map derived from HLS during the growing season provides valuable information on spatial and temporal variability in crop emergence that can be used for crop monitoring and refining agricultural statistics used in broad-scale modeling efforts.

Area Sampling Frames are used for surveys including crop acreage and yield, forests, and natural resource inventories and are the foundation of the statistical program of the USDA National Agricultural Statistics Service (NASS) and many statistical survey programs around the world. An automated area frame stratification method was recently implemented into NASS operations, which is based on the objective calculation of percent cultivation derived from the NASS geospatial Cropland Data Layers (CDLs). While autostratification consistently outperforms manual stratification in cultivated areas, we found that CDL-based pixel counting estimation consistently underestimated crop acreage. Previous research indicates that CDL classification accuracy is affected by training data pixel level buffering. We hypothesize that training data pixel level buffering will also affect the CDL based auto-stratification results and crop acreage estimation. This paper evaluates the impact of training data buffering on area frame stratification results and crop estimates. Preliminary results indicate that the crop acreage underestimation can be directly attributed to the training data pixel level buffering procedure.

Area Sampling Frames (ASFs) are the foundation of the agricultural statistics program of USDA National Agricultural Statistics Service (NASS). A geospatial Cropland Data Layer (CDL) based automated stratification (AS) method was recently implemented to achieve higher accuracies than traditional stratification (TS), based on visual interpretation, in cultivated areas. This paper extends the AS assessment to the post stratification estimates. South Dakota (SD) US 2013 post stratification estimates, based on AS, are compared with the SD 2013 June Agricultural Survey estimates based on TS. Post stratification estimates obtained using AS are comparable, to the TS estimates, based on estimate percent differences. Considering the significant improvement in accuracy using AS in cultivated strata in five test states, improved accuracy in the highly cultivated stratum and improved stratum homogeneity in this study, it is concluded that the CDL based AS method generates ASFs that are more objective, efficient, accurate, and homogeneous and reduces labor costs.

The United States Department of Agriculture (USDA), National Agricultural Statistics Service (NASS) annually produces crop specific classifications and acreage estimates over the major growing regions of the United States using medium resolution satellite imagery. The classifications are published in the public domain as the Cropland Data Layer (CDL) after the release of official county estimates. This program previously used; Landsat TM and ETM+ imagery, the NASS June Agricultural Survey (JAS) segments for ground truth information, and Peditor software for producing the classification and regression estimates. The unpredictability of the Landsat program, the labor intensive nature of JAS digitizing for the CDL program, and the potential efficiencies gained by using commercial software warranted investigations into new program methods. NASS began investigating alternative sensors to the Landsat platform in 2004, acquiring ResourceSat-1 Advanced Wide Field Sensor (AWiFS) data over the active CDL states. Additionally, evaluations were performed on alternative ground truth methodologies using data collected through the USDA/Farm Service Agency (FSA) Common Land Unit (CLU) program and testing began with See5 software to produce the CDL. NASS began pilot AWiFS studies for the State of Nebraska in 2004 and followed up with studies of Arkansas, Louisiana, Mississippi, Missouri, Nebraska and North Dakota in 2005. Accuracy assessments and acreage indications determined that the AWiFS results positively reduced the statistical variance of acreage indications from the JAS area frame, delivering a potential successor to the Landsat platform. In 2006 pilot testing was complete and the AWiFS sensor was selected as the exclusive source of imagery for the production of the CDL and acreage estimates. The FSA CLU program provides a comprehensive national digitized and attributed GIS dataset collected annually for inclusion into programs like the CDL. Commercial image processing programs such as See5 were tested in 2006 against the AWiFS imagery and CLU datasets, providing evidence of efficiency gains in statistical accuracy, scope of coverage and time of delivery to make further investigation warranted. The results of these program updates are presented.

The US Department of Agriculture (USDA)/National Agricultural Statistics Service (NASS) has generated the cropland data layer (CDL) product for more than twelve years, providing annual geospatial updates of the agricultural landscape across the US Heartland. The CDL program delivers acreage estimates based on regression modeling for decision support and provides a crop-specific geospatial dataset for the public domain. This model produces acreage estimates for statisticians and key decision makers in NASS Field Offices and the Agricultural Statistics Board, the official statistical reporting unit of USDA. The CDL program has grown incrementally as collaborative partnerships and technological efficiencies have increased via reengineering both the classification and estimation process. The CDL is now operational in 19 states for 2008 covering the major corn, soybeans, cotton, and wheat areas. Additionally, the CDL is generated multiple times during the growing season. This allows the program to take advantage of updated satellite imagery and updated farmer reported ground data, in consideration for the crop reports that NASS releases in June, August, September, and October. Satellite data have been used successfully for years by the CDL program to accurately identify crop types and produce acreage estimates at the state, district, and county levels. Continued expansion of the CDL program would be impossible without leveraging both satellite and ground truth data partnerships. The USDA/Foreign Agricultural Service/Satellite Image Archive (SIA) provides year round coverage of all major growing areas, while the USDA/Farm Services Agency (FSA) provides farmer reported agricultural specific ground truth. These data sharing partnerships are synergized by the CDL to provide a crop specific land cover classification utilizing regression tree software derived from two major inputs; 1) 56 meter multispectral imagery from Resourcesat-1 AWiFS and 2) ground truth training data from the FSA, Common Land Unit Program. Additionally, 3) ancillary datasets are incorporated into the classification method to improve non-agricultural land cover, including; The National Elevation Dataset; the 2001 National Land Cover Dataset (NLCD), the NLCD Imperviousness and Forest Canopy products. The current CDL product is a comprehensive land cover inventory produced operationally in-season annually.