Design of a Local Nested Grid for the Optimal Combined Use of Landsat 8 and Sentinel 2 Data

Abstract

Earth Observation (EO) imagery is difficult to find and access for the intermediate user, requiring advanced skills and tools to transform it into useful information. Currently, remote sensing data is increasingly freely and openly available from different satellite platforms. However, the variety of images in terms of different types of sensors, spatial and spectral resolutions generates limitations due to the heterogeneity and complexity of the data, making it difficult to exploit the full potential of satellite imagery. Addressing this issue requires new approaches to organize, manage, and analyse remote-sensing imagery. This paper focuses on the growing trend based on satellite EO and the analysis-ready data (ARD) to integrate two public optical satellite missions: Landsat 8 (L8) and Sentinel 2 (S2). This paper proposes a new way to combine S2 and L8 imagery based on a Local Nested Grid (LNG). The LNG designed plays a key role in the development of new products within the European EO downstream sector, which must incorporate assimilation techniques and interoperability best practices, automatization, systemization, and integrated web-based services that will potentially lead to pre-operational downstream services. The approach was tested in the Duero river basin (78,859 km2) and in the groundwater Mancha Oriental (7279 km2) in the Jucar river basin, Spain. In addition, a viewer based on Geoserver was prepared for visualizing the LNG of S2 and L8, and the Normalized Difference Vegetation Index (NDVI) values in points. Thanks to the LNG presented in this paper, the processing, storage, and publication tasks are optimal for the combined use of images from two different satellite sensors when the relationship between spatial resolutions is an integer (3 in the case of L8 and S2).

S2Looking: A Satellite Side-Looking Dataset for Building Change Detection

“…By varying the camera angle on subsequent passes, it is also possible to revisit the impacted region more often to monitor recovery and look for signs of further events. For military applications, scroll imaging by satellites is frequently used to obtain a steady fix on war zones [57]. However, a challenge arising from using off-nadir angles that needs to be properly met by new change-detection algorithms is that there is a lack of geometric consistency between tall objects in the gathered satellite images.…”

INSPECTORMAP: A Spatial Data Infrastructure Applied to the Maintenance of Solar Plants Using Free Satellite Imagery

“…For this reason, a Python script has been developed so that it allows: (i) reporting the available Landast-8 and Sentinel-2 Level-2 products along the photovoltaic plants by a proper query [24,25] to the downloading platforms, (ii) the automatic download of such information, (iii) the registration of the downloaded products in the system catalog, (iv) the automatic calculation of various spectral indexes for monitoring the status of the photovoltaic plants, (v) the registration of these spectral indexes in the system catalog, and finally (vi) the publication of the spectral indexes in the SDI viewer through a map server. Likewise, the integration of Landsat-8 and Sentinel-2 images has been carried out according to an LNG developed by the authors [20].…”

“…Behind the server-side of INSPECTORMAP (Back End, Figure 1) there is a Python script that, in combination with a PostgreSQL database, allows characterizing the areas to be studied as well as downloading and processing on a GeoServer the corresponding set of Landsat-8 and Sentinel-2 images. Furthermore, this script integrates an LNG [20] to ensure a more efficient integration of both data sets. From the downloaded images, the script automatically calculates the NDVI and NDWI spectral indices to analyze the presence of vegetation and water bodies, as explained in the previous section.…”

“…These are easy-to-use web tools that allow to automate the download, storage, and processing of data without the need for a computer with a large graphic and storage capacity, since everything is done through the cloud. Likewise, the latest advances in the Open Data Cube (ODC) standards [20,21] allow the integration and fusion of different satellite platforms and missions, improving and enhancing the analysis of different satellite products together.…”