ABSTRACT

Agricultural production and food security highly depend on crop growth and condition throughout the growing season. Timely and spatially explicit information on crop phenology can assist in informed decision making and agricultural land management. Remote sensing can be a powerful tool for agricultural assessment. Remotely sensed data is ideally suited for both large-scale and field-level analyses due to the wide variability of datasets with diverse spatiotemporal resolution. To derive crop-specific phenometrics, we fused time series from Landsat 8 and Sentinel 2 with Moderate-resolution Imaging Spectroradiometer (MODIS) data. Using a linear regression approach, synthetic Landsat 8 and Sentinel 2 data were created based on MODIS imagery. This fusion-process resulted in synthetic imagery with radiometric characteristics of original Landsat 8 and Sentinel 2 data. We created four different time series using synthetic data as well as a mix of original and synthetic data. The extracted time series of phenometrics consisting of both synthetic and original data showed high detail in the final phenomaps which allowed intra-field level assessment of crops. In-situ field reports were used for validation. Our phenometrics showed only a few days of deviation for most crops and datasets. The proposed data integration method can be applied in areas where data from a single high-resolution source is scarce.

摘要

在整个生长期，农业生产和粮食安全高度取决于作物的生长和状况。关于作物物候的及时和明确的空间信息可以帮助做出明智的决策和农业土地管理。遥感可以成为进行农业评估的有力工具。由于具有不同时空分辨率的数据集差异很大，因此遥感数据非常适合大规模分析和现场分析。为了得出特定于作物的物候信息，我们将Landsat 8和Sentinel 2的时间序列与中等分辨率成像光谱仪（MODIS）数据融合在一起。使用线性回归方法，基于MODIS影像创建了合成的Landsat 8和Sentinel 2数据。这种融合过程产生了具有原始Landsat 8和Sentinel 2数据辐射特征的合成图像。我们使用综合数据以及原始数据和综合数据创建了四个不同的时间序列。提取的由综合和原始数据组成的物候统计的时间序列在最终的物候中显示出很高的细节，从而可以对作物进行田间水平评估。使用现场报告进行验证。对于大多数农作物和数据集，我们的物候测量表明只有几天的偏差。所提出的数据集成方法可以应用于缺少来自单个高分辨率源的数据的区域。提取的由综合和原始数据组成的物候统计的时间序列在最终的物候中显示出很高的细节，从而可以对作物进行田间水平评估。使用现场报告进行验证。对于大多数农作物和数据集，我们的物候测量表明只有几天的偏差。所提出的数据集成方法可以应用于缺少来自单个高分辨率源的数据的区域。提取的由综合和原始数据组成的物候统计的时间序列在最终的物候中显示出很高的细节，从而可以对作物进行田间水平评估。使用现场报告进行验证。对于大多数农作物和数据集，我们的物候测量表明只有几天的偏差。所提出的数据集成方法可以应用于缺少来自单个高分辨率源的数据的区域。