Taken as a given: Evaluating the accuracy of remotely sensed crop data in the USA

Larsen, Ashley E.; Hendrickson, Brandon T.; Dedeic, Nicholas; MacDonald, Andrew J.

doi:10.1016/j.agsy.2015.10.008

Cited by 19 publications

(10 citation statements)

References 16 publications

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“…Indeed, the reported CDL accuracy from 2008 to 2015 across MT croplands varies from 69.6% (2010) to 85.0% (2014), while the CDL accuracy levels may be inflated because they do not account for edge effects [54]. CDL accuracy may also be degraded in regions with sparse or complex agriculture [55], which is characteristic of MT croplands.…”

Section: Discussionmentioning

confidence: 99%

Regional Crop Gross Primary Productivity and Yield Estimation Using Fused Landsat-MODIS Data

et al. 2018

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Accurate crop yield assessments using satellite remote sensing-based methods are of interest for regional monitoring and the design of policies that promote agricultural resiliency and food security. However, the application of current vegetation productivity algorithms derived from global satellite observations is generally too coarse to capture cropland heterogeneity. The fusion of data from different sensors can provide enhanced information and overcome many of the limitations of individual sensors. In thitables study, we estimate annual crop yields for seven important crop types across Montana in the continental USA from 2008-2015, including alfalfa, barley, maize, peas, durum wheat, spring wheat and winter wheat. We used a satellite data-driven light use efficiency (LUE) model to estimate gross primary productivity (GPP) over croplands at 30-m spatial resolution and eight-day time steps using a fused NDVI dataset constructed by blending Landsat (5 or 7) and Terra MODIS reflectance data. The fused 30-m NDVI record showed good consistency with the original Landsat and MODIS data, but provides better spatiotemporal delineations of cropland vegetation growth. Crop yields were estimated at 30-m resolution as the product of estimated GPP accumulated over the growing season and a crop-specific harvest index (HI GPP). The resulting GPP estimates capture characteristic cropland productivity patterns and seasonal variations, while the estimated annual crop production results correspond favorably with reported county-level crop production data (r = 0.96, relative RMSE = 37.0%, p < 0.05) from the U.S. Department of Agriculture (USDA). The performance of estimated crop yields at a finer (field) scale was generally lower, but still meaningful (r = 0.42, relative RMSE = 50.8%, p < 0.05). Our methods and results are suitable for operational applications of crop yield monitoring at regional scales, suggesting the potential of using global satellite observations to improve agricultural management, policy decisions and regional/global food security.

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Section: Discussionmentioning

confidence: 99%

Regional Crop Gross Primary Productivity and Yield Estimation Using Fused Landsat-MODIS Data

et al. 2018

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“…The preceding analyses contribute to existing work investigating land cover map error and its consequences (e.g., Fritz, See, et al., ; Olofsson et al., ; Verburg et al., ). Previous studies have assessed map errors either using point‐based accuracy assessments (e.g., Foody, ; Frey & Smith, ; Olofsson et al., ), by evaluating between‐map discrepancies (e.g., Fritz & See, ; Fritz et al., ; Fritz, See, et al., ), or by comparing map‐derived estimates to aggregated statistics (e.g., Fritz et al., ; Larsen et al., ; Yu et al., ). A smaller number based their assessments on contiguous ground‐truth maps, but these covered relatively small regions (<3,000 km 2 , or <0.03% of the area covered here; Dendoncker, Schmit, & Rounsevell, ; Schmit et al., ).…”

Section: Discussionmentioning

confidence: 99%

“…Because comprehensive, spatially representative ground‐truth data are typically unavailable for rapidly changing regions (Kuemmerle et al., ; See et al., ), what remains an open question is exactly how much the maps researchers typically use deviate from actual land cover, how this affects analyses based on these maps, and how this in turn impacts our understanding of processes being studied. Our current understanding of map accuracy over such areas is often based on scarce information or top‐down “sanity checks” made in comparison to aggregated survey data (Larsen, Hendrickson, Dedeic, & MacDonald, ; Yu et al., ).…”

Section: Introductionmentioning

confidence: 99%

A large‐area, spatially continuous assessment of land cover map error and its impact on downstream analyses

Estes

Chen

Debats

et al. 2017

Global Change Biology

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Land cover maps increasingly underlie research into socioeconomic and environmental patterns and processes, including global change. It is known that map errors impact our understanding of these phenomena, but quantifying these impacts is difficult because many areas lack adequate reference data. We used a highly accurate, high-resolution map of South African cropland to assess (1) the magnitude of error in several current generation land cover maps, and (2) how these errors propagate in downstream studies. We first quantified pixel-wise errors in the cropland classes of four widely used land cover maps at resolutions ranging from 1 to 100 km, and then calculated errors in several representative "downstream" (map-based) analyses, including assessments of vegetative carbon stocks, evapotranspiration, crop production, and household food security. We also evaluated maps' spatial accuracy based on how precisely they could be used to locate specific landscape features. We found that cropland maps can have substantial biases and poor accuracy at all resolutions (e.g., at 1 km resolution, up to $ 45% underestimates of cropland (bias) and nearly 50% mean absolute error (MAE, describing accuracy); at 100 km, up to 15% underestimates and nearly 20% MAE). National-scale maps derived from higherresolution imagery were most accurate, followed by multi-map fusion products.Constraining mapped values to match survey statistics may be effective at minimizing bias (provided the statistics are accurate). Errors in downstream analyses could be substantially amplified or muted, depending on the values ascribed to croplandadjacent covers (e.g., with forest as adjacent cover, carbon map error was 200%-500% greater than in input cropland maps, but $ 40% less for sparse cover types).The average locational error was 6 km (600%). These findings provide deeper insight into the causes and potential consequences of land cover map error, and suggest several recommendations for land cover map users. K E Y W O R D Sagent-based model, agriculture, bias, carbon, crop yield, evapotranspiration, land cover, remote sensing

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“…Given this lack of evaluation, several articles have questioned the reliability of analyses that use CDL data to identify recent agricultural trends, citing concerns about both the CDL's accuracy and its appropriateness for measuring changes to the landscape [35][36][37][38][39][40]. Such critiques often cite low reported accuracies for the CDLs when mapping certain crops in specific regions or when depicting nonagricultural land covers such as grasslands.…”

Section: Introductionmentioning

confidence: 99%

Accuracy, Bias, and Improvements in Mapping Crops and Cropland across the United States Using the USDA Cropland Data Layer

2021

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The U.S. Department of Agriculture’s (USDA) Cropland Data Layer (CDL) is a 30 m resolution crop-specific land cover map produced annually to assess crops and cropland area across the conterminous United States. Despite its prominent use and value for monitoring agricultural land use/land cover (LULC), there remains substantial uncertainty surrounding the CDLs’ performance, particularly in applications measuring LULC at national scales, within aggregated classes, or changes across years. To fill this gap, we used state- and land cover class-specific accuracy statistics from the USDA from 2008 to 2016 to comprehensively characterize the performance of the CDL across space and time. We estimated nationwide area-weighted accuracies for the CDL for specific crops as well as for the aggregated classes of cropland and non-cropland. We also derived and reported new metrics of superclass accuracy and within-domain error rates, which help to quantify and differentiate the efficacy of mapping aggregated land use classes (e.g., cropland) among constituent subclasses (i.e., specific crops). We show that aggregate classes embody drastically higher accuracies, such that the CDL correctly identifies cropland from the user's perspective 97% of the time or greater for all years since nationwide coverage began in 2008. We also quantified the mapping biases of specific crops throughout time and used these data to generate independent bias-adjusted crop area estimates, which may complement other USDA survey- and census-based crop statistics. Our overall findings demonstrate that the CDLs provide highly accurate annual measures of crops and cropland areas, and when used appropriately, are an indispensable tool for monitoring changes to agricultural landscapes.

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Taken as a given: Evaluating the accuracy of remotely sensed crop data in the USA

Cited by 19 publications

References 16 publications

Regional Crop Gross Primary Productivity and Yield Estimation Using Fused Landsat-MODIS Data

Regional Crop Gross Primary Productivity and Yield Estimation Using Fused Landsat-MODIS Data

A large‐area, spatially continuous assessment of land cover map error and its impact on downstream analyses

Accuracy, Bias, and Improvements in Mapping Crops and Cropland across the United States Using the USDA Cropland Data Layer

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