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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing. >Toward Global Soil Moisture Monitoring With Sentinel-1: Harnessing Assets and Overcoming Obstacles
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Toward Global Soil Moisture Monitoring With Sentinel-1: Harnessing Assets and Overcoming Obstacles

机译：使用Sentinel-1进行全球土壤湿度监测：利用资产和克服障碍

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摘要

Soil moisture is a key environmental variable, important to, e.g., farmers, meteorologists, and disaster management units. Here, we present a method to retrieve surface soil moisture (SSM) from the Sentinel-1 (S-1) satellites, which carry C-band Synthetic Aperture Radar (CSAR) sensors that provide the richest freely available SAR data source so far, unprecedented in accuracy and coverage. Our SSM retrieval method, adapting well-established change detection algorithms, builds the first globally deployable soil moisture observation data set with 1-km resolution. This paper provides an algorithm formulation to be operated in data cube architectures and high-performance computing environments. It includes the novel dynamic Gaussian upscaling method for spatial upscaling of SAR imagery, harnessing its field-scale information and successfully mitigating effects from the SAR’s high signal complexity. Also, a new regression-based approach for estimating the radar slope is defined, coping with Sentinel-1’s inhomogeneity in spatial coverage. We employ the S-1 SSM algorithm on a 3-year S-1 data cube over Italy, obtaining a consistent set of model parameters and product masks, unperturbed by coverage discontinuities. An evaluation of therefrom generated S-1 SSM data, involving a 1-km soil water balance model over Umbria, yields high agreement over plains and agricultural areas, with low agreement over forests and strong topography. While positive biases during the growing season are detected, the excellent capability to capture small-scale soil moisture changes as from rainfall or irrigation is evident. The S-1 SSM is currently in preparation toward operational product dissemination in the Copernicus Global Land Service.
机译：土壤湿度是关键的环境变量，对例如农民，气象学家和灾难管理部门来说很重要。在这里，我们提出了一种从Sentinel-1（S-1）卫星中检索表层土壤水分（SSM）的方法，这些卫星带有C波段合成孔径雷达（CSAR）传感器，可提供迄今为止最丰富的免费可用SAR数据源，准确性和覆盖范围空前。我们的SSM检索方法采用完善的变化检测算法，建立了第一个可在全球范围内部署且分辨率为1 km的土壤湿度观测数据集。本文提供了可在数据立方体体系结构和高性能计算环境中运行的算法公式。它包括用于SAR图像空间放大的新型动态高斯放大方法，利用其场尺度信息并成功减轻了SAR高信号复杂度的影响。此外，定义了一种新的基于回归的估计雷达斜率的方法，以应对Sentinel-1的空间覆盖不均匀性。我们在意大利的3年S-1数据立方体上采用S-1 SSM算法，获得了一致的模型参数和产品掩码集，不受覆盖范围不连续性的干扰。据此评估生成的S-1 SSM数据，其中包括翁布里亚地区1公里的土壤水平衡模型，在平原和农业地区的一致性较高，而在森林和地形强的地区一致性较低。尽管在生长季节发现了正偏差，但很明显，由于降雨或灌溉，它具有捕获小规模土壤水分变化的出色能力。 S-1 SSM目前正准备在哥白尼全球土地服务中推广运营产品。

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