Accuracy, Bias, and Improvements in Mapping Crops and Cropland across the United States Using the USDA Cropland Data Layer
Accuracy, Bias, and Improvements in Mapping Crops and Cropland across the United States Using the USDA Cropland Data Layer
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DOI：
10.3390/rs13050968
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发表时间：
2021-03-01
期刊：
影响因子：
5
通讯作者：
Gibbs, Holly K.
中科院分区：
文献类型：
--
作者：
Lark, Tyler J.;Schelly, Ian H.;Gibbs, Holly K.
美国农业部（USDA）的农田数据层（CDL）是一个30米分辨率的特定作物土地覆盖图，每年制作一次，用于评估美国境内的作物和农田面积。尽管CDL在监测农业土地利用/土地覆盖方面具有突出的用途和价值，但其性能仍存在很大的不确定性，特别是在国家尺度、汇总类别或跨年度变化的土地利用/土地覆盖测量应用中。为了填补这一空白，我们使用了美国农业部2008年至2016年的州和土地覆盖类别特定准确性统计数据，以全面表征CDL在空间和时间上的表现。我们估计了全国范围内特定作物CDL的面积加权精度以及耕地和非耕地的汇总类别。我们还推导并报告了超类准确性和域内错误率的新指标，这有助于量化和区分绘制汇总土地利用类别的功效（例如，农田）在组成子类之间（即，特定作物）。我们表明，聚合类体现了显着更高的准确性，这样的CDL正确地识别农田从用户的角度来看，97%的时间或更大的所有年份，因为在2008年开始在全国范围内覆盖。我们还量化了特定作物在整个时间内的映射偏差，并使用这些数据来生成独立的偏差调整作物面积估计值，这可能会补充美国农业部其他基于调查和人口普查的作物统计数据。我们的总体研究结果表明，CDLs提供了高度准确的作物和耕地面积的年度措施，如果使用得当，是一个不可或缺的工具，监测农业景观的变化。
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.