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全文摘要次数: 121 全文下载次数: 245
引用本文:

DOI:

10.11834/jrs.20210322

收稿日期:

2020-07-31

修改日期:

2021-04-02

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基于集合卡尔曼滤波方法的高时空分辨率山区地表反照率反演
郑凯旋1, 林兴稳1, 闻建光2, 郝大磊2
1.浙江师范大学地理与环境科学学院;2.中国科学院空天信息创新研究院遥感科学国家重点实验室
摘要:

高分辨率地表反照率遥感产品以其空间分辨率高的优点,目前正成为区域能量平衡和气候变化研究的主要数据源。现行的高分辨率地表反照率遥感反演算法及数据产品均假设地表平坦且均一,缺乏对地表异质性和地形复杂性的考虑,将适用于平坦地表的反演算法应用于山区将存在一定的误差。改进的直接算法为反演山区高分辨率地表反照率提供了可能,将直接反演算法与山地辐射传输模型结合,可以反演山区地表反照率产品。但该算法受到下垫面积雪、云污染等影响,反演的影像时域不连续,且存在着较多的缺失值,无法构建时空连续的地表反照率产品来支撑山区地表能量平衡相关研究。针对这一问题,本文以高分四号(GF-4)卫星数据为例,首先基于改进的直接反演算法反演山区地表反照率,利用地面辐射通量站点观测数据和MODIS BRDF/Albedo产品构建先验知识背景场,采用集合卡尔曼滤波方法对反演的山区地表反照率进行时空填补,构建了时空连续的地表反照率反演方法,并生产了2016-2017年的山区地表反照率产品。研究结果表明,反演的时空连续高分辨率地表反照率产品与地面站点观测数据的一致性较好。不同坡度地面站点的验证结果显示,反演的时空连续地表反照率产品在平坦地表下RMSE小于0.01,坡度较大的站点下RMSE为0.0163。本文描述的山区地表反照率时空填补技术也可以应用到其他定量遥感产品,为这些产品在山区地表下的填补技术提供有效参考。

Time Series High-Resolution Albedo retrieval over rugged terrain Based on the Ensemble Kalman Filter Algorithm
Abstract:

Land surface albedo is a key parameter to describe the surface energy budget. An increasing need for fine-scale albedo products is promoted in regional applications of radiative forcing and coarse-scale albedo product validation. However, the long term fine-scale albedo products over mountainous areas are not available thus far. The topographic slope, aspect, and land cover types make the sloping surface more heterogeneous than the flat surface. Existing fine-scale albedo estimation algorithms may carry the uncertainties due to the complex topography. What’s more, the fine –scale albedo observations are often unavailable due to cloud contamination, which makes it difficult to obtain time series albedo estimations. To overcome these problems, in this study we adopt the improved Angular Bin (AB) algorithm and Ensemble Kalman Filter Algorithm (EnKF) to estimate time-series fine-scale satellite-based albedo over rugged terrain. The preliminary approach of the new built albedo estimation over mountainous areas was carried out in the Heihe River Basin (BRB) by using the Chinese GF-4 satellite data. The validation results against ground measurements over various land cover types and topographic slopes show that our algorithm is effective for the selected land surfaces and can achieve root-mean-square errors (RMSEs) of not more than 0.03. When compared with the referenced albedo product retrieved by direct retrieval algorithm, the GF-4 albedo products show a good performance with the RMSE smaller than 0.02. The retrieved long time series GF-4 albedo can improve the understanding of scale effects among different spatial resolution albedo products and can help to upscale in ground-based albedo measurements to coarse-scale during the multi-scale validation workflow. This algorithm also provide an example for other satellite-based remote sensing products retrieval over rugged terrain.

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