Details

Autor(en) / Beteiligte

• Johnson, David M.

Titel

Using the Landsat archive to map crop cover history across the United States

Ist Teil von

• Remote sensing of environment, 2019-10, Vol.232, p.111286, Article 111286

Ort / Verlag

New York: Elsevier Inc

Erscheinungsjahr

2019

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• Landsat Thematic Mapper has been collecting multispectral imagery at 30 m resolution globally since 1984. One utility of the data has been for detailed mapping of agricultural regions and seasonal identification of crops grown within them. However, the ability to do so has only been applied sporadically and eluded widespread adoption due to cost of the imagery, burdensome preprocessing requirements, and computing not being up to the task. These hurdles have become much reduced of recent with the free and open distribution of the Landsat imagery, emphasis on ready-to-use surface reflectance data products, and distributed high performance computing infrastructures available online in “the cloud.” As such, this work leverages these aspects and investigates the ability to retrospectively map summer crops over the United States (US) annually from 1984 to 2007. Google's Earth Engine Internet-based analytical platform containing the historical Landsat archive in surface reflectance format was used as a foundation for the classification work. Robust 30 m Cropland Data Layer (CDL) information from US Department of Agriculture (USDA) for years 2008 through 2011 were leveraged to train rule-based classifiers which were applied back through time to each year 1984 through 2007. Focus crops were corn, soybeans, and winter wheat – the three largest by area in the US. A large sampling of highly intensive counties throughout the country were prototyped for generation of the 24 years of historical crop cover. For validation, crop area statistics were calculated for each county-year and compared to survey-based information existing from the USDA. Results were muted overall with the average crop area coefficient of determination (R2) correlations for the years 1984–2007 found to be 0.192, 0.159, and 0.142 for corn, soybeans, and winter wheat, respectively. Furthermore, the standard deviations were variable at 0.132, 0.177, and 0.133, also respectively. While unimpressive, it was found as a benchmark that the R2 between the 2008 through 2017 CDL classifications were only 0.478, 0.686, and 0.726 and thus a suggestion that the USDA area statistics are an imperfect measure of map accuracy. Deletion of approximately one third to one half of the grossest 1984–2007 outlier years from the historical outputs pulled the correlations to the benchmark standard. Qualitatively, most of the remaining years classified looked of high quality and were believed useful as field-level thematic crop area maps. These historical cropland maps could provide the ability to better detail the role farming has played on the broad US landscape over recent decades. •Google Earth Engine was used to prototype annual historical crop maps for the USA.•Landsat 5 and 7 satellite imagery from 1984 to 2007 was useful as a foundation.•Generalized classification rules were derivable and applied retroactively.•Assessment of the maps against official area statistics showed weak correlations.•Removal of outlier years showed remaining classifications to look relatively robust.