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Open accessJournal ArticleDOI: 10.3390/RS13050968

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

04 Mar 2021-Remote Sensing (Multidisciplinary Digital Publishing Institute)-Vol. 13, Iss: 5, pp 968
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.

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Topics: Agricultural land (54%), Land cover (54%)
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Journal ArticleDOI: 10.1016/J.RSE.2021.112445
Abstract: Identifying the location of irrigated croplands and how they change over time is critical for assessing and managing limited water resources to navigate such challenges as local to global water scarcity, increasing demands for food and energy production, and environmental sustainability. Although efforts have been made to map irrigated area for the U.S., multi-year nationwide maps at field-relevant resolutions are still unavailable; existing products suffer from coarse resolution, uncertain accuracy, and/or limited spatial coverage, especially in the eastern U.S. In this study, we present an approach to map the extent of irrigated croplands across the conterminous U.S. (CONUS) for each year in the period of 1997–2017. To scale nationwide, we developed novel methods to generate training datasets covering both the western and eastern U.S. For the more arid western U.S., we built upon the methods of Xie et al. (2019) and further developed a greenness-based normalization technique to estimate optimal thresholds of crop greenness in any year based on those in USDA NASS census years (i.e., 1997, 2002, 2007, 2012, and 2017). For the relatively humid eastern states, we collected data on the current status of center pivot irrigated and non-irrigated fields and extended these sample points through time using various indices and observational thresholds. We used the generated samples along with remote sensing features and environmental variables to train county-stratified random forest classifiers annually for pixel-level classification of irrigated extent each year and subsequently implemented a logic-based post-classification filtering. The produced Lan dsat-based I rrigation D ataset (LANID-US) accurately reconstructed NASS irrigation patterns at both the county and state level while also supplying new annual area estimates for intra-epoch years. Nationwide pixel-level locational assessment further demonstrated an overall accuracy above 90% across years. In the 21-year study period, we found several hotspots of irrigation change including significant gains in the U.S. Midwest, the Mississippi River Alluvial Plain, and the East Coast as well as irrigation declines in the central and southern High Plains Aquifer and the southern California Central Valley, Arizona, and Florida. The resulting 30 m resolution LANID-US products represent the finest resolution account of nationwide irrigation use and dynamics across the United States to date. The developed approach, training data, and products are further extendable to other years (either before 1997 or after 2017) for continuous monitoring of irrigated area over CONUS and are spatially applicable to other regions with similar climate and cropping landscapes.

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4 Citations


Open accessJournal ArticleDOI: 10.1029/2020MS002284
Pan Chen1, Pan Chen2, Pan Chen3, Yongping Yuan4  +6 moreInstitutions (7)
Abstract: The Missouri River Basin (MORB) has experienced a resurgence of grassland conversion to crop production, which raised concerns on water quality. We applied the Soil and Water Assessment Tool (SWAT) to address how this conversion would impact water quality. We designed three crop production scenarios representing conversion of grassland to: (a) continuous corn; (b) corn/soybean rotation; and (c) corn/wheat rotation to assess the impact. The SWAT model results showed: (a) the lower MORB produced high total nitrogen (TN) and total phosphorus (TP) load before conversion (baseline) due mainly to high precipitation and high agricultural activity; (b) the greatest percentage increases of TN and TP occurred in the North and South Dakotas, coinciding with the highest amount of grassland conversion to cropland; and (c) grassland conversion to continuous corn resulted in the greatest increase in TN and TP loads, followed by conversion to corn/soybean and then conversion to corn/wheat. Although the greatest percentage increases of TN and TP occurred in the North and South Dakotas, these areas still contributed relatively low TN and TP to total basin loads after conversion. However, watersheds, predominantly in the lower MORB continued to be "hotspots" that contributed the greatest amounts of TN and TP to the total basin loads-driven by a combination of grassland conversion, high precipitation, and loading from pre-existing cropland. At the watershed outlet, the TN and TP loads were increased by 6.4% (13,800 t/yr) and 8.7% (3,400 t/yr), respectively, during the 2008-2016 period for the conversion to continuous corn scenario.

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2 Citations


Open accessJournal ArticleDOI: 10.1111/GCBB.12877
Madhu Khanna1, Luoye Chen1, Bruno Basso2, Ximing Cai1  +10 moreInstitutions (6)
01 Oct 2021-Gcb Bioenergy
Topics: Marginal land (68%), Ecosystem services (54%)

2 Citations


Journal ArticleDOI: 10.1021/ACS.EST.1C02236
Chongya Jiang1, Kaiyu Guan, Madhu Khanna1, Luoye Chen1  +1 moreInstitutions (1)
Abstract: Utilization of marginal land for growing dedicated bioenergy crops for second-generation biofuels is appealing to avoid conflicts with food production. This study develops a novel framework to quantify marginal land for the Contiguous United States (CONUS) based on a history of satellite-observed land use change (LUC) over the 2008-2015 period. Frequent LUC between crop and noncrop is assumed to be an indicator of economically marginal land; this land is also likely to have a lower opportunity cost of conversion from food crop to bioenergy crop production. We first present an approach to identify cropland in transition using the time series of Cropland Data Layer (CDL) land cover product and determine the amount of land that can be considered marginal with a high degree of confidence vs with uncertainty across the CONUS. We find that the biophysical characteristics of this land and its productivity and environmental vulnerability vary across the land and lie in between that of permanent cropland and permanent natural vegetation/bare areas; this land also has relatively low intrinsic value and agricultural profit but a high financial burden and economic risk. We find that the total area of marginal land with confidence vs with uncertainty is 10.2 and 58.4 million hectares, respectively, and mainly located along the 100th meridian. Only a portion of this marginal land (1.4-2.2 million hectares with confidence and 14.8-19.4 million hectares with uncertainty) is in the rainfed region and not in crop production and, thus, suitable for producing energy crops without diverting land from food crops in 2016. These estimates are much smaller than the estimates obtained by previous studies, which consider all biophysically low-quality land to be marginal without considering economical marginality. The estimate of marginal land for bioenergy crops obtained in this study is an indicator of the availability of economically marginal land that is suitable for bioenergy crop production; whether this land is actually converted to bioenergy crops will depend on the market conditions. We note the inability to conduct field-level validation of cropland in transition and leave it to future advances in technology to ground-truth land use change and its relationship to economically marginal land.

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Topics: Marginal land (72%), Land cover (62%), Land use, land-use change and forestry (61%) ... read more

2 Citations


Open accessJournal ArticleDOI: 10.1109/ACCESS.2021.3116526
Guofan Shao1, Lina Tang2, Hao Zhang1Institutions (2)
29 Sep 2021-IEEE Access
Abstract: Accuracy assessment is essential in all image classification-related fields, ranging from molecular imaging to earth observation. However, existing accuracy metrics are too sensitive to class imbalance or lack explicit interpretations for assessing classification performance. Consequently, their scores may be misleading when they are applied to compare classification algorithms that address different image data sources. These limitations jeopardize the widespread application of deep learning classification methods for classifying different image types. We introduce the metrics of image classification efficacy from medicine and pharmacology to overcome the limitations of accuracy metrics. We include a baseline classification to derive the metrics of image classification efficacy and apply real-world and hypothetical examples to further examine their usefulness. Image classification efficacies can be applied at the map and class levels and for binary and multiclass classifications. The interpretability and comparability of image classification efficacies facilitate reliable classification method evaluation across data sources. We detail the procedures of classification efficacy assessment for image classification researchers and classifier users.

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1 Citations


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46 results found


Open accessJournal ArticleDOI: 10.2307/634969
Abstract: Concepts and Foundations of Remote Sensing Elements of Photographic Systems Basic Principles of Photogrammetry Introduction to Visual Image Interpretation Multispectral, Thermal, and Hyperspectral Sensing Earth Resource Satellites Operating in the Optical Spectrum Digital Image Processing Microwave and Lidar Sensing Appendix A: Radiometric Concepts, Terminology, and Units Appendix B: Remote Sensing Data and Information Resources Appendix C: Sample Coordinate Transformation and Resampling Procedures

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6,523 Citations



Open accessJournal ArticleDOI: 10.1073/PNAS.0704119104
Abstract: Land change science has emerged as a fundamental component of global environmental change and sustainability research. This interdisciplinary field seeks to understand the dynamics of land cover and land use as a coupled human-environment system to address theory, concepts, models, and applications relevant to environmental and societal problems, including the intersection of the two. The major components and advances in land change are addressed: observation and monitoring; understanding the coupled system-causes, impacts, and consequences; modeling; and synthesis issues. The six articles of the special feature are introduced and situated within these components of study.

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Topics: Sustainability science (59%), Land use (57%), Sustainability (56%) ... read more

1,483 Citations


Open accessJournal ArticleDOI: 10.1016/J.RSE.2014.02.015
Abstract: The remote sensing science and application communities have developed increasingly reliable, consistent, and robust approaches for capturing land dynamics to meet a range of information needs. Statistically robust and transparent approaches for assessing accuracy and estimating area of change are critical to ensure the integrity of land change information. We provide practitioners with a set of “good practice” recommendations for designing and implementing an accuracy assessment of a change map and estimating area based on the reference sample data. The good practice recommendations address the three major components: sampling design, response design and analysis. The primary good practice recommendations for assessing accuracy and estimating area are: (i) implement a probability sampling design that is chosen to achieve the priority objectives of accuracy and area estimation while also satisfying practical constraints such as cost and available sources of reference data; (ii) implement a response design protocol that is based on reference data sources that provide sufficient spatial and temporal representation to accurately label each unit in the sample (i.e., the “reference classification” will be considerably more accurate than the map classification being evaluated); (iii) implement an analysis that is consistent with the sampling design and response design protocols; (iv) summarize the accuracy assessment by reporting the estimated error matrix in terms of proportion of area and estimates of overall accuracy, user's accuracy (or commission error), and producer's accuracy (or omission error); (v) estimate area of classes (e.g., types of change such as wetland loss or types of persistence such as stable forest) based on the reference classification of the sample units; (vi) quantify uncertainty by reporting confidence intervals for accuracy and area parameters; (vii) evaluate variability and potential error in the reference classification; and (viii) document deviations from good practice that may substantially affect the results. An example application is provided to illustrate the recommended process.

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Topics: Sampling design (54%), Protocol (science) (52%), Reference data (51%)

1,217 Citations


Journal ArticleDOI: 10.1016/J.RSE.2012.10.031
Abstract: The area of land use or land cover change obtained directly from a map may differ greatly from the true area of change because of map classification error. An error-adjusted estimator of area can be easily produced once an accuracy assessment has been performed and an error matrix constructed. The estimator presented is a stratified estimator which is applicable to data acquired using popular sampling designs such as stratified random, simple random and systematic (the stratified estimator is often labeled a poststratified estimator for the latter two designs). A confidence interval for the area of land change should also be provided to quantify the uncertainty of the change area estimate. The uncertainty of the change area estimate, as expressed via the confidence interval, can then subsequently be incorporated into an uncertainty analysis for applications using land change area as an input (e.g., a carbon flux model). Accuracy assessments published for land change studies should report the information required to produce the stratified estimator of change area and to construct confidence intervals. However, an evaluation of land change articles published between 2005 and 2010 in two remote sensing journals revealed that accuracy assessments often fail to include this key information. We recommend that land change maps should be accompanied by an accuracy assessment that includes a clear description of the sampling design (including sample size and, if relevant, details of stratification), an error matrix, the area or proportion of area of each category according to the map, and descriptive accuracy measures such as user's, producer's and overall accuracy. Furthermore, mapped areas should be adjusted to eliminate bias attributable to map classification error and these error-adjusted area estimates should be accompanied by confidence intervals to quantify the sampling variability of the estimated area. Using data from the published literature, we illustrate how to produce error-adjusted point estimates and confidence intervals of land change areas. A simple analysis of uncertainty based on the confidence bounds for land change area is applied to a carbon flux model to illustrate numerically that variability in the land change area estimate can have a dramatic effect on model outputs.

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Topics: Estimator (55%), Sampling (statistics) (55%), Land cover (55%) ... read more

609 Citations