Kraatz Simon, Lamb Brian T, Hively W Dean, Jennewein Jyoti S, Gao Feng, Cosh Michael H, Siqueira Paul
USDA ARS Hydrology and Remote Sensing Laboratory, Beltsville, MD 20705, USA.
USGS Lower Mississippi-Gulf Water Science Center, Coram, NY 11727, USA.
Sensors (Basel). 2023 Oct 20;23(20):8595. doi: 10.3390/s23208595.
A general limitation in assessing the accuracy of land cover mapping is the availability of ground truth data. At sites where ground truth is not available, potentially inaccurate proxy datasets are used for sub-field-scale resolution investigations at large spatial scales, i.e., in the Contiguous United States. The USDA/NASS Cropland Data Layer (CDL) is a popular agricultural land cover dataset due to its high accuracy (>80%), resolution (30 m), and inclusions of many land cover and crop types. However, because the CDL is derived from satellite imagery and has resulting uncertainties, comparisons to available in situ data are necessary for verifying classification performance. This study compares the cropland mapping accuracies (crop/non-crop) of an optical approach (CDL) and the radar-based crop area (CA) approach used for the upcoming NASA-ISRO Synthetic Aperture Radar (NISAR) L- and S-band mission but using Sentinel-1 C-band data. CDL and CA performance are compared to ground truth data that includes 54 agricultural production and research fields located at USDA's Beltsville Agricultural Research Center (BARC) in Maryland, USA. We also evaluate non-crop mapping accuracy using twenty-six built-up and thirteen forest sites at BARC. The results show that the CDL and CA have a good pixel-wise agreement with one another (87%). However, the CA is notably more accurate compared to ground truth data than the CDL. The 2017-2021 mean accuracies for the CDL and CA, respectively, are 77% and 96% for crop, 100% and 94% for built-up, and 100% and 100% for forest, yielding an overall accuracy of 86% for the CDL and 96% for CA. This difference mainly stems from the CDL under-detecting crop cover at BARC, especially in 2017 and 2018. We also note that annual accuracy levels varied less for the CA (91-98%) than for the CDL (79-93%). This study demonstrates that a computationally inexpensive radar-based cropland mapping approach can also give accurate results over complex landscapes with accuracies similar to or better than optical approaches.
评估土地覆盖制图准确性的一个普遍限制是地面真值数据的可用性。在没有地面真值的地点，可能不准确的替代数据集被用于大空间尺度下的子区域尺度分辨率调查，即在毗连的美国地区。美国农业部国家农业统计局的农田数据层（CDL）是一个广受欢迎的农业土地覆盖数据集，因其具有较高的准确性（>80%）、分辨率（30米）以及包含多种土地覆盖和作物类型。然而，由于CDL是从卫星图像派生而来且存在不确定性，因此需要与可用的实地数据进行比较以验证分类性能。本研究比较了一种光学方法（CDL）和基于雷达的作物面积（CA）方法的农田制图准确性（作物/非作物），其中基于雷达的作物面积方法用于即将开展的美国国家航空航天局-印度空间研究组织合成孔径雷达（NISAR）的L波段和S波段任务，但使用的是哨兵-1 C波段数据。将CDL和CA的性能与地面真值数据进行比较，这些地面真值数据包括位于美国马里兰州美国农业部贝茨维尔农业研究中心（BARC）的54个农业生产和研究田地。我们还使用BARC的26个建成区和13个森林地点评估了非作物制图的准确性。结果表明，CDL和CA在像素层面上彼此具有良好的一致性（87%）。然而，与地面真值数据相比，CA的准确性明显高于CDL。CDL和CA在2017 - 2021年期间的平均准确率分别为：作物方面，CDL为77%，CA为96%；建成区方面，CDL为100%，CA为94%；森林方面，CDL为100%，CA为100%，CDL的总体准确率为86%，CA为96%。这种差异主要源于CDL在BARC地区对作物覆盖的检测不足，尤其是在2017年和2018年。我们还注意到，CA的年度准确率水平（91 - 98%）比CDL（79 - 93%）的变化更小。这项研究表明，一种计算成本较低的基于雷达的农田制图方法在复杂景观中也能给出准确的结果，其准确性与光学方法相当或更好。
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