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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing >A Method for the Analysis of Small Crop Fields in Sentinel-2 Dense Time Series
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A Method for the Analysis of Small Crop Fields in Sentinel-2 Dense Time Series

机译：Sentinel-2密集时间序列中小型庄稼田地分析的方法

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摘要

Satellite image time series (SITS), such as those by Sentinel-2 (S2) satellites, provides a large amount of information due to their combined temporal, spatial, and spectral resolutions. The high revisit frequency and spatial resolution of S2 result in: 1) increase in the probability of acquiring cloud-free images and 2) availability of detailed information for analyzing small objects. These characteristics are of interest in precision agriculture, where temporally dense SITS can benefit the understanding of crop behaviors. In the past, information about agricultural practices has been collected over large regions and focused on mixed/aggregated crops due to the poor tradeoff between the spatial and temporal resolutions. Products have been generated at low spatial resolution and daily basis or at high spatial resolution and weekly/monthly basis. They are meaningful for large agricultural fields, whereas they are limited when fields show a small average size. In this context, S2 characteristics allow for both high spatial and temporal resolution products. However, no existing automatic method effectively separates small fields from each other in an unsupervised way and deals with data irregularly sampled in time. Thus, this article presents a method suitable for the analysis of small crop fields in S2 dense SITS that accounts for S2 characteristics. The method fuses spatio-temporal information, analyzes data spatio-temporal evolution, and extracts relevant spatio-temporal information. The effectiveness of the proposed method was corroborated by experiments carried out on S2-SITS acquired over an area located in Barrax, Spain.
机译：卫星图像时间序列（坐标），例如由Sentinel-2（S2）卫星，由于它们的组合时空，空间和光谱分辨率提供了大量信息。 S2的高回归频率和空间分辨率导致：1）获取无云图像的概率和2）用于分析小物体的详细信息的可用性。这些特征对精密农业有兴趣，在时间上密集的坐姿可以有利于对作物行为的理解。过去，在大地区收集了有关农业实践的信息，并且由于空间和时间决议之间的权衡差，因此集中在混合/汇总作物上。产品已在低空间分辨率和日间基础上或以高空间分辨率和每周/每月进行生成。它们对大型农业领域有意义，而当场平均大小较小时它们是有限的。在这种情况下，S2特性允许高空间和时间分辨率的产品。然而，没有现有的自动方法以无监督的方式有效地将小字段彼此分开，并处理不规则地采样的数据。因此，本文介绍了一种适用于S2密集的小型裁剪场分析的方法，该方法坐在S2特征的情况下。该方法融合了时空信息，分析了数据时空演变，提取了相关的时空信息。所提出的方法的有效性是通过在位于西班牙巴拉克斯的一个地区获得的S2-Sits的实验来证实。

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