dc.description.abstract
Spaceborne remote sensing has been profiting from technological advances in numerous fields and has entered the era of Big Data. The growing sector of civilian data providers and the European Copernicus Earth observation programme with its Sentinel satellite constellation provide an unprecedented rich source of geophysical data. While fuelling science as well as public and private endeavours, the produced data volumes of some Terabytes per day constitute a major challenge and place high demands on processingand storagefacilities. When aiming for global data processing, an efficient handling of remote sensing data is of vital importance, demanding a well-suited definition of spatial grids for the data's storage and manipulation. For high-resolution image data, regular grids defined by map projections have been identified as practicable, cognisant of their drawbacks due to geometric distortions and data inflation. The here newly defined metric named grid oversampling factor (GOF) estimates local data oversampling appearing during projection of generic satellite images to a regular raster grid. With this, an optimised grid system named Equi7Grid is defined that minimises image distortions and data oversampling, with a global mean oversampling of 2% (compared to 35% for the widely used global Plate Carree projection). The Equi7Grid consists of 7 continental subgrids featuring a coordinate and tiling system, based on Equidistant Azimuthal projections. This choice is opposed to previous studies that suggested equal-area projections, which were found to be disadvantageous due to critical raster image distortions in the course of this study. One application of satellite remote sensing is to provide data on Soil Moisture (SM). SM is a key environmental variable, important to e.g. farmers, meteorologists, and disaster management units. In climatology, knowledge on SM is essential for the assessment of the global water-, energy-, and carboncycles. This study presents a method able to retrieve Surface Soil Moisture (SSM) from the Sentinel-1 satellites, which carry C-band Synthetic Aperture Radar (S-1 CSAR) sensors that provide the richest freely available SAR data source so far, unprecedented in accuracy and coverage. The SSM retrieval method, which adapts well-established change detection algorithms, builds the first globally deployable soil moisture observation dataset with 1km resolution and is suitable to be operated in data cube architectures like the Equi7Grid and High Performance Computing (HPC) environments. It includes the novel Dynamic Gaussian Upscaling (DGU) 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. For a single remote sensing system, there always exists a trade-off between spatial and temporal resolution of the observations, leading to missed dynamics either in the spatial or temporal domain. Harnessing the Equi7Grid data cube's features of a common data space and the inherent possibility to access directly both space and time domain, this scale gap in remote sensing of SM is closed with a novel data fusion approach. Through temporal filtering of the joint signal of spatio-temporally complementary radar sensors, a kilometre-scale, daily soil water content product is obtained, named SCATSAR-SWI. With 25 km MetopASCAT SSM and 1km Sentinel-1 SSM serving as input, the SCATSAR-SWI is globally applicable and achieves daily full coverage over operated areas. For evaluation, both the S-1 SSM retrieval algorithm as well as the SCATSAR-SWI data fusion algorithm, are employed on a 3 year data cube over Italy, and SM data is thereby compared against in-situ measurements, reference data from ASCAT SSM, a 1km soil moisture model, and rainfall observations. The experiments for the Sentinel-1 SSM yield a consistent set of model parameters and product masks, unperturbed by coverage discontinuities. The SSM shows high agreement over plains and agricultural areas and low agreement over forests and strong topography. While positive biases during the growing season are detected, excellent capability to capture small-scale soil moisture changes as such from rainfall or irrigation is evident. For the SCATSAR-SWI, the experiments yield comprehensively high agreement with all reference datasets. However, while the Sentinel-1 signal appears to be attenuated, the ASCATs signal dynamics are fully transferred to the SCATSAR-SWI and benefit from the Sentinel-1 parametrisation. Finally, the SCATSAR-SWI shows excellent capability to reproduce rainfall observations over Italy. In the end, the insights gained during the conducted experiments and investigations has lead to the realisation of an optimised data cube architecture, and to the successful production of a soil moisture product ingesting satellite measurements observed at complementary spatio-temporal scales. The here defined grid and algorithms build the basis for the upcoming operational Sentinel-1 SSM and SCATSAR-SWI production in the frame of the Copernicus Global Land Services (CGLS).
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