...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>ISPRS Journal of Photogrammetry and Remote Sensing >Mapping irrigated cropland extent across the conterminous United States at 30 m resolution using a semi-automatic training approach on Google Earth Engine
【24h】

Mapping irrigated cropland extent across the conterminous United States at 30 m resolution using a semi-automatic training approach on Google Earth Engine

机译：使用Google Earth Engine上的半自动训练方法，以30 m的分辨率绘制全美范围内的灌溉农田范围图

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Accurate and timely information on the distribution of irrigated croplands is crucial to research on agriculture, water availability, land use, and climate change. While agricultural land use has been well characterized, less attention has been paid specifically to croplands that are irrigated, in part due to the difficulty in mapping and distinguishing irrigation in satellite imagery. In this study, we developed a semi-automatic training approach to rapidly map irrigated croplands across the conterminous United States (CONUS) at 30 m resolution using Google Earth Engine. To resolve the issue of lacking nationwide training data, we generated two intermediate irrigation maps by segmenting Landsat-derived annual maximum greenness and enhanced vegetation index using county level thresholds calibrated from an existing coarse resolution irrigation map. The resulting intermediate maps were then spatially filtered to provide a training data pool for most areas except for the upper midwestern states where we visually collected samples. We then used random samples extracted from the training pool along with remote sensing-derived features and climate variables to train ecoregion-stratified random forest classifiers for pixel-level classification. For ecoregions with a large training pool, the procedure of sample extraction, classifier training, and classification was conducted 10 times to obtain stable classification results. The resulting 2012 Landsat-based irrigation dataset (LANID) identified 23.3 million hectares of irrigated croplands in CONUS. A quantitative assessment of LANID showed superior accuracy to currently available maps, with a mean Kappa value of 0.88 (0.75-0.99), overall accuracy of 94% (87.5-99%), and producer's and user's accuracy of the irrigation class of 97.3% and 90.5%, respectively, at the aquifer level. Evaluation of feature importance indicated that Landsat-derived features played the primary role in classification in relatively arid regions while climate variables were important in the more humid eastern states. This methodology has the potential to produce annual irrigation maps for CONUS and provide insights into the field-level spatial and temporal aspects of irrigation.
机译：关于灌溉农田分布的准确，及时的信息对于研究农业，水资源，土地利用和气候变化至关重要。尽管对农业土地的使用已有很好的描述，但对灌溉农田的关注却很少，部分原因是在卫星图像中难以标绘和区分灌溉。在这项研究中，我们开发了一种半自动训练方法，可以使用Google Earth Engine在30 m的分辨率上快速绘制整个美国本土（CONUS）的灌溉农田的地图。为了解决缺乏全国培训数据的问题，我们使用从现有的粗分辨率灌溉图校准的县级阈值，将Landsat衍生的年度最大绿度和增强的植被指数进行了分割，从而生成了两个中间灌溉图。然后对生成的中间图进行空间过滤，以为大多数区域提供训练数据池，除了中西部上州以外，我们在视觉上收集了样本。然后，我们使用从训练池中提取的随机样本以及基于遥感的特征和气候变量来训练生态区分层的随机森林分类器，以进行像素级分类。对于训练库较大的生态区域，进行了10次样本提取，分类器训练和分类的过程，以获得稳定的分类结果。由此产生的基于Landsat的2012年灌溉数据集（LANID）在CONUS中确定了2,330万公顷的灌溉农田。 LANID的定量评估显示，其精度优于当前可用的地图，平均Kappa值为0.88（0.75-0.99），总体精度为94％（87.5-99％），生产者和使用者的灌溉等级准确性为97.3％和90.5％，分别在含水层。对地物重要性的评估表明，在相对干旱的地区，源自Landsat的地物在分类中起主要作用，而在更潮湿的东部州，气候变量很重要。这种方法有可能为CONUS制作年度灌溉图，并提供有关灌溉领域时空方面的见识。

著录项

相似文献

  • 外文文献
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号