.png){kind=icon}
返回
Extending Crop Type Reference Data Using a Phenology-Based Approach
.png){kind=icon}
OA
AI
DOI：10.3389/fsufs.2020.00099.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The combination of high spatial resolution and multi-date satellite imagery offers new opportunities for mapping and monitoring crop types of different agricultural field sizes. However, mapping of crop types at high spatial resolution requires high-quality crop type reference data typically collected from the ground-based surveys to create the maps and/or to assess the map accuracy. The availability of sufficient crop type reference data is limited over large geographic regions because of the time, effort, cost, and accessibility in different parts of the world. To generate large area crop type maps, any existing, but limited reference data must be spatially extended to other regions using appropriate and available non-ground-based sources. There is the potential to classify High Resolution Imagery (HRI) using a phenology-based approach as demonstrated in this paper to generate additional reference data within similar agriculture ecological zones (AEZs) based on the crop characteristics, their types, and their growing season. Therefore, the objective of this study was to evaluate if existing, limited crop type reference data could be extended using this approach. Multi-date, high spatial resolution satellite images were used to spatially extend the limited crop type reference data from one region [called the training region (TR)] to another region [called the test region (TE)] within the same AEZ using a phenology-based Decision Tree (DT) classifier for three different field sizes. The results demonstrate that this phenology-based classification approach can efficiently and effectively extend the limited crop type reference data to other regions in same AEZ for different field sizes.
Keyword:
crop mapping
reference data
high resolution imagery (HRI)
agriculture ecological zones (AEZs)
phenology-based classification
decision tree (DT) classifier
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.1
论文数:
5.9K
被引数:
1.2W
机构
引用论文
.png){kind=icon}
分享收藏
查看更多内容