Taken as a given: evaluating the accuracy of remotely sensed crop data in the USA.
TL;DR: In this paper, the authors compared area estimates for cropland and major US crops (corn, soybeans, wheat and small grains) at the county level for the contiguous US in 2012 and for a subset of states in 2007, and found that accuracy of the Cropland Data Layer is high in regions dominated by a few crop types.
About: This article is published in Agricultural Systems.The article was published on 2015-12-01. It has received 14 citations till now. The article focuses on the topics: Agricultural land & Geospatial analysis.
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TL;DR: The 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.
Abstract: 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 (HIGPP). 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.
82 citations
Cites background from "Taken as a given: evaluating the ac..."
...CDL accuracy may also be degraded in regions with sparse or complex agriculture [55], which is characteristic of MT croplands....
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TL;DR: Overall, it is found that higher crop diversity does reduce insecticide use, but the relationship is strongly influenced by the differences in crop types between diverse and less diverse landscapes.
Abstract: Agricultural landscape intensification has enabled food production to meet growing demand. However, there are concerns that more simplified cropland with lower crop diversity, less noncrop habitat, and larger fields results in increased use of pesticides due to a lack of natural pest control and more homogeneous crop resources. Here, we use data on crop production and insecticide use from over 100,000 field-level observations from Kern County, California, encompassing the years 2005–2013 to test if crop diversity, field size, and cropland extent affect insecticide use in practice. Overall, we find that higher crop diversity does reduce insecticide use, but the relationship is strongly influenced by the differences in crop types between diverse and less diverse landscapes. Further, we find insecticide use increases with increasing field size. The effect of cropland extent is distance-dependent, with nearby cropland decreasing insecticide use, whereas cropland further away increases insecticide use. This refined spatial perspective provides unique understanding of how different components of landscape simplification influence insecticide use over space and for different crops. Our results indicate that neither the traditionally conceived “simplified” nor “complex” agricultural landscape is most beneficial to reducing insecticide inputs; reality is far more complex.
52 citations
TL;DR: A highly accurate, high-resolution map of South African cropland was used to assess the magnitude of error in several current generation land cover maps, and how these errors propagate in downstream studies, and several recommendations for land cover map users are suggested.
Abstract: 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 higher-resolution 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 cropland-adjacent 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.
44 citations
Cites background from "Taken as a given: evaluating the ac..."
...…(e.g. Foody,410 2002; Frey & Smith, 2007; Olofsson et al., 2013), by evaluating between-map discrepancies411 (e.g. Fritz & See, 2008; Fritz et al., 2011a, 2010), or by comparing map-derived estimates to412 aggregated statistics (e.g. Fritz et al., 2010; Larsen et al., 2015; Yu et al., 2014)....
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...…of map accuracy over such areas is often based on scarce information or43 top-down ”sanity checks” made in comparison to aggregated survey data (Larsen et al., 2015;44 Yu et al., 2014).45 Since it is difficult to fully quantify map errors, it is even more challenging to gauge their46 impact…...
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TL;DR: In this article, the satellite-based 30-m cropland ET estimates (ET30m) corresponded favorably with both tower-based ET observations (ETflux; R2 = 0.69, RMSE’s=※0.54, RMSE
42 citations
TL;DR: 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 as mentioned in this paper.
Abstract: 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.
34 citations
References
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TL;DR: Effectiveness of any control program will depend on the different responses of the crop, pest, and control organism to this pattern of environment, which there are geographic, local, and temporal variations in the environment.
Abstract: Economically important pests usually attack a crop or group of crops over a wide region in which there are geographic, local, and temporal variations in the environment. Effectiveness of any control program will therefore depend on the different responses of the crop, pest, and control organism to this pattern of environment. Usually the environmental heterogeneity is treated as an unavoidable complication in program evaluation, and attempts are made to work with “average” conditions.
3,149 citations
TL;DR: The rapidly expanding literature on habitat management is reviewed with attention to practices for favoring predators and parasitoids, implementation of habitat management, and the contributions of modeling and ecological theory to this developing area of conservation biological control.
Abstract: ▪ Abstract Many agroecosystems are unfavorable environments for natural enemies due to high levels of disturbance. Habitat management, a form of conservation biological control, is an ecologically based approach aimed at favoring natural enemies and enhancing biological control in agricultural systems. The goal of habitat management is to create a suitable ecological infrastructure within the agricultural landscape to provide resources such as food for adult natural enemies, alternative prey or hosts, and shelter from adverse conditions. These resources must be integrated into the landscape in a way that is spatially and temporally favorable to natural enemies and practical for producers to implement. The rapidly expanding literature on habitat management is reviewed with attention to practices for favoring predators and parasitoids, implementation of habitat management, and the contributions of modeling and ecological theory to this developing area of conservation biological control. The potential to int...
2,705 citations
TL;DR: In this article, the authors argue that the true value of functional biodiversity on the farm is often inadequately acknowledged or understood, while conventional intensification tends to disrupt beneficial functions of biodiversity.
1,463 citations
TL;DR: The National Agricultural Statistics Service (NASS) of the US Department of Agriculture (USDA) produces the Cropland Data Layer (CDL) product, which is a raster-formatted, geo-referenced, crop-specific, land cover map as mentioned in this paper.
Abstract: The National Agricultural Statistics Service (NASS) of the US Department of Agriculture (USDA) produces the Cropland Data Layer (CDL) product, which is a raster-formatted, geo-referenced, crop-specific, land cover map. CDL program inputs include medium resolution satellite imagery, USDA collected ground truth and other ancillary data, such as the National Land Cover Data set. A decision tree-supervised classification method is used to generate the freely available state-level crop cover classifications and provide crop acreage estimates based upon the CDL and NASS June Agricultural Survey ground truth to the NASS Agricultural Statistics Board. This paper provides an overview of the NASS CDL program. It describes various input data, processing procedures, classification and validation, accuracy assessment, CDL product specifications, dissemination venues and the crop acreage estimation methodology. In general, total crop mapping accuracies for the 2009 CDLs ranged from 85% to 95% for the major crop categories.
788 citations