NEW METHODS AND SATELLITES: A PROGRAM UPDATE ON THE NASS CROPLAND DATA LAYER ACREAGE PROGRAM

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.

[1]  Indian Society of Agricultural Statistics , 1950 .

[2]  J. Monteith.  SOLAR RADIATION AND PRODUCTIVITY IN TROPICAL ECOSYSTEMS , 1972 .

[3]  Robert M. Hagan,et al.  Functions to Predict Effects of Crop Water Deficits , 1973 .

[4]  Remote Sensing: A Better View , 1977 .

[5]  J. Monteith.  Climate and the efficiency of crop production in Britain , 1977 .

[6]  R. Lyndon,et al.  References and Selected Bibliography , 1984 .

[7]  W. Hargrove,et al.  Photogrammetric Engineering & Remote Sensing , 2022 .

[8]  S. M. E. Groten,et al.  NDVI—crop monitoring and early yield assessment of Burkina Faso , 1993 .

[9]  I. Supit,et al.  System description of the WOFOST 6.0 crop simulation model implemented in CGMS , 1994 .

[10]  Paul C. Doraiswamy,et al.  Spring Wheat Yield Assessment Using NOAA AVHRR Data , 1995 .

[11]  R. DeFries,et al.  Classification trees: an alternative to traditional land cover classifiers , 1996 .

[12]  J. Ross Quinlan,et al.  Bagging, Boosting, and C4.5 , 1996, AAAI/IAAI, Vol. 1.

[13]  James Rowland,et al.  Estimating maize production in Kenya using NDVI: Some statistical considerations , 1998 .

[14]  A. Rosema,et al.  The Relation between Laser-Induced Chlorophyll Fluorescence and Photosynthesis , 1998 .

[15]  F. J. Gallego.  CROP AREA ESTIMATION IN THE MARS PROJECT , 1999 .

[16]  Wu Bing.  Operational Remote Sensing Methods for Agricultural Statistics , 2000 .

[17]  Rick Mueller,et al.  Creating a Cropland Data Layer for an Entire State , 2002 .

[18]  Igor Y. Savin,et al.  Relative time NDVI mosaics as an indicator of crop growth , 2003, SPIE Remote Sensing.

[19]  A. J. W. De Wit,et al.  Efficiency and accuracy of per-field classification for operational crop mapping , 2004 .

[20]  Limin Yang,et al.  Development of a 2001 National land-cover database for the United States , 2004 .

[21]  Li Qiang-zi.  Crop Acreage Estimation Using Two Individual Sampling Frameworks with Stratification , 2004 .

[22]  Rick L. Lawrence,et al.  Classification of remotely sensed imagery using stochastic gradient boosting as a refinement of classification tree analysis , 2004 .

[23]  The Relationship of Vegetaion-derived Index and Site-measured Rice LAI , 2004 .

[24]  Claire G. Boryan,et al.  MULTIRESOLUTION LANDSAT TM AND AWIFS SENSOR ASSESSMENT FOR CROP AREA ESTIMATION IN NEBRASKA , 2005 .

[25]  Li Qiangzi.  Agricultural Drought Monitoring with Vegetation Condition and Temperature Condition , 2006 .

[26]  EVALUATING THE ACCURACY OF 2005 MULTITEMPORAL TM AND AWiFS IMAGERY FOR CROPLAND CLASSIFICATION OF NEBRASKA , 2007 .

[27]  Baruth Bettina,et al.  ISPRS Workshop Proceedings: Remote Sensing Support to Crop Yield Forecast and Area Estimates , 2007 .

[28]  David M. Johnson.  A Comparison of Coincident Landsat-5 TM and Resourcesat-1 AWiFS Imagery for Classifying Croplands , 2008 .