Two Disaggregation Algorithms to Estimate Soil Moisture at Moderate (1 km and 300 m) and at High Resolution (60 m): Applications over the North of Africa
Two Disaggregation Algorithms to Estimate Soil Moisture at Moderate (1 km and 300 m) and at High Resolution (60 m): Applications over the North of Africa
复制标题
估算中分辨率（1 km 和 300 m）和高分辨率（60 m）土壤湿度的两种分解算法：在非洲北部的应用
DOI：
10.1109/m2garss57310.2024.10537394
复制
发表时间：
2024
期刊：
影响因子：
--
通讯作者：
C. López
中科院分区：
文献类型：
--
作者：
M. Pablos;G. Portal;A. Camps;M. Vall;C. López
巴塞罗那专家中心（BEC）已经成为使用分解算法生成高分辨率土壤湿度（SM）地图的国际参考。十多年前，人们开发了一种半经验方法来产生1公里处的土壤湿度和海洋盐度（SMOS） SM。这种方法经过多年的改进，可以获得无云地图，并经过修改，进一步提高了空间分辨率，最高可达300米。最近，已经开发了一种机器学习方法来推导欧洲航天局（ESA）在60米的气候变化倡议（CCI） SM。由于在解聚过程中增加了多光谱信息，降尺度SM地图的总体精度与粗尺度SM地图相似，但提供了SM空间变异性的额外信息。在这方面，本文介绍了在非洲北部的三种不同应用，以举例说明高分辨率SM数据的附加价值。
The Barcelona Expert Center (BEC) has become an international reference in the generation of high resolution soil moisture (SM) maps using disaggregation algorithms. More than a decade ago, a semi-empirical approach was developed to produce Soil Moisture and Ocean Salinity (SMOS) SM at 1 km. This method has been refined over the years to obtain cloud free maps, and modified to further improve the spatial resolution up to 300 m. More recently, a machine-learning approach has been developed to derive European Space Agency (ESA)’s Climate Change Initiative (CCI) SM at 60 m.Thanks to the multi-spectral information added during the disaggregation process, the downscaled SM maps have an overall accuracy similar to the coarse ones, but provide additional information about the SM spatial variability. In this regard, three different applications over the north of Africa are presented here to exemplify the added-value of SM data at high resolution.