Test QGIS Cloud OGC Services

Test QGIS Cloud OGC Services

Test QGIS Cloud OGC Services:

This dataset contains both large (A0) printable maps of the Torres Strait broken into six overlapping regions, based on a clear sky, clear water composite Sentinel 2 composite imagery and the imagery used to create these maps. These maps show satellite imagery of the region, overlaid with reef and island boundaries and names. Not all features are named, just the more prominent features. This also includes a vector map of Ashmore Reef and Boot Reef in Coral Sea as these were used in the same discussions that these maps were developed for. The map of Ashmore Reef includes the atoll platform, reef boundaries and depth polygons for 5 m and 10 m.

This dataset contains all working files used in the development of these maps. This includes all a copy of all the source datasets and all derived satellite image tiles and QGIS files used to create the maps. This includes cloud free Sentinel 2 composite imagery of the Torres Strait region with alpha blended edges to allow the creation of a smooth high resolution basemap of the region.

The base imagery is similar to the older base imagery dataset: Torres Strait clear sky, clear water Landsat 5 satellite composite (NERP TE 13.1 eAtlas, AIMS, source: NASA).

Most of the imagery in the composite imagery from 2017 - 2021.

Method: The Sentinel 2 basemap was produced by processing imagery from the World_AIMS_Marine-satellite-imagery dataset (not yet published) for the Torres Strait region. The TrueColour imagery for the scenes covering the mapped area were downloaded. Both the reference 1 imagery (R1) and reference 2 imagery (R2) was copied for processing. R1 imagery contains the lowest noise, most cloud free imagery, while R2 contains the next best set of imagery. Both R1 and R2 are typically composite images from multiple dates.

The R2 images were selectively blended using manually created masks with the R1 images. This was done to get the best combination of both images and typically resulted in a reduction in some of the cloud artefacts in the R1 images. The mask creation and previewing of the blending was performed in Photoshop. The created masks were saved in 01-data/R2-R1-masks. To help with the blending of neighbouring images a feathered alpha channel was added to the imagery. The processing of the merging (using the masks) and the creation of the feathered borders on the images was performed using a Python script (src/local/03-merge-R2-R1-images.py) using the Pillow library and GDAL. The neighbouring image blending mask was created by applying a blurring of the original hard image mask. This allowed neighbouring image tiles to merge together.

The imagery and reference datasets (reef boundaries, EEZ) were loaded into QGIS for the creation of the printable maps.

To optimise the matching of the resulting map slight brightness adjustments were applied to each scene tile to match its neighbours. This was done in the setup of each image in QGIS. This adjustment was imperfect as each tile was made from a different combinations of days (to remove clouds) resulting in each scene having a different tonal gradients across the scene then its neighbours. Additionally Sentinel 2 has slight stripes (at 13 degrees off the vertical) due to the swath of each sensor having a slight sensitivity difference. This effect was uncorrected in this imagery.

Single merged composite GeoTiff: The image tiles with alpha blended edges work well in QGIS, but not in ArcGIS Pro. To allow this imagery to be used across tools that don't support the alpha blending we merged and flattened the tiles into a single large GeoTiff with no alpha channel. This was done by rendering the map created in QGIS into a single large image. This was done in multiple steps to make the process manageable.

The rendered map was cut into twenty 1 x 1 degree georeferenced PNG images using the Atlas feature of QGIS. This process baked in the alpha blending across neighbouring Sentinel 2 scenes. The PNG images were then merged back into a large GeoTiff image using GDAL (via QGIS), removing the alpha channel. The brightness of the image was adjusted so that the darkest pixels in the image were 1, saving the value 0 for nodata masking and the boundary was clipped, using a polygon boundary, to trim off the outer feathering. The image was then optimised for performance by using internal tiling and adding overviews. A full breakdown of these steps is provided in the README.md in the 'Browse and download all data files' link.

The merged final image is available in export\TS_AIMS_Torres Strait-Sentinel-2_Composite.tif.

Change Log: 2023-03-02: Eric Lawrey Created a merged version of the satellite imagery, with no alpha blending so that it can be used in ArcGIS Pro. It is now a single large GeoTiff image. The Google Earth Engine source code for the World_AIMS_Marine-satellite-imagery was included to improve the reproducibility and provenance of the dataset, along with a calculation of the distribution of image dates that went into the final composite image. A WMS service for the imagery was also setup and linked to from the metadata. A cross reference to the older Torres Strait clear sky clear water Landsat composite imagery was also added to the record.

22 Nov 2023: Eric Lawrey Added the data and maps for close up of Mer. - 01-data/TS_DNRM_Mer-aerial-imagery/ - preview/Torres-Strait-Mer-Map-Landscape-A0.jpeg - exports/Torres-Strait-Mer-Map-Landscape-A0.pdf Updated 02-Torres-Strait-regional-maps.qgz to include the layout for the new map.

Source datasets: Complete Great Barrier Reef (GBR) Island and Reef Feature boundaries including Torres Strait Version 1b (NESP TWQ 3.13, AIMS, TSRA, GBRMPA), https://eatlas.org.au/data/uuid/d2396b2c-68d4-4f4b-aab0-52f7bc4a81f5

Geoscience Australia (2014b), Seas and Submerged Lands Act 1973 - Australian Maritime Boundaries 2014a - Geodatabase [Dataset]. Canberra, Australia: Author. https://creativecommons.org/licenses/by/4.0/ [license]. Sourced on 12 July 2017, https://dx.doi.org/10.4225/25/5539DFE87D895

Basemap/AU_GA_AMB_2014a/Exclusive_Economic_Zone_AMB2014a_Limit.shp The original data was obtained from GA (Geoscience Australia, 2014a). The Geodatabase was loaded in ArcMap. The Exclusive_Economic_Zone_AMB2014a_Limit layer was loaded and exported as a shapefile. Since this file was small no clipping was applied to the data.

Geoscience Australia (2014a), Treaties - Australian Maritime Boundaries (AMB) 2014a [Dataset]. Canberra, Australia: Author. https://creativecommons.org/licenses/by/4.0/ [license]. Sourced on 12 July 2017, http://dx.doi.org/10.4225/25/5539E01878302 Basemap/AU_GA_Treaties-AMB_2014a/Papua_New_Guinea_TSPZ_AMB2014a_Limit.shp The original data was obtained from GA (Geoscience Australia, 2014b). The Geodatabase was loaded in ArcMap. The Papua_New_Guinea_TSPZ_AMB2014a_Limit layer was loaded and exported as a shapefile. Since this file was small no clipping was applied to the data.

AIMS Coral Sea Features (2022) - DRAFT This is a draft version of this dataset. The region for Ashmore and Boot reef was checked. The attributes in these datasets haven't been cleaned up. Note these files should not be considered finalised and are only suitable for maps around Ashmore Reef. Please source an updated version of this dataset for any other purpose. CS_AIMS_Coral-Sea-Features/CS_Names/Names.shp CS_AIMS_Coral-Sea-Features/CS_Platform_adj/CS_Platform.shp CS_AIMS_Coral-Sea-Features/CS_Reef_Boundaries_adj/CS_Reef_Boundaries.shp CS_AIMS_Coral-Sea-Features/CS_Depth/CS_AIMS_Coral-Sea-Features_Img_S2_R1_Depth5m_Coral-Sea.shp CS_AIMS_Coral-Sea-Features/CS_Depth/CS_AIMS_Coral-Sea-Features_Img_S2_R1_Depth10m_Coral-Sea.shp

Murray Island 20 Sept 2011 15cm SISP aerial imagery, Queensland Spatial Imagery Services Program, Department of Resources, Queensland This is the high resolution imagery used to create the map of Mer.

Marine satellite imagery (Sentinel 2 and Landsat 8) (AIMS), https://eatlas.org.au/data/uuid/5d67aa4d-a983-45d0-8cc1-187596fa9c0c - World_AIMS_Marine-satellite-imagery

Data Location: This dataset is filed in the eAtlas enduring data repository at: data\custodian\2020-2029-AIMS\TS_AIMS_Torres-Strait-Sentinel-2-regional-maps. On the eAtlas server it is stored at eAtlas GeoServer\data\2020-2029-AIMS.

A continuous dataset of Land Surface Temperature (LST) is vital for climatological and environmental studies. LST can be regarded as a combination of seasonal mean temperature (climatology) and daily anomaly, which is attributed mainly to the synoptic-scale atmospheric circulation (weather). To reproduce LST in cloudy pixels, time series (2002-2019) of cloud-free 1km MODIS Aqua LST images were generated and the pixel-based seasonality (climatology) was calculated using temporal Fourier analysis. To add the anomaly, we used the NCEP Climate Forecast System Version 2 (CFSv2) model, which provides air surface temperature under both cloudy and clear sky conditions. The combination of the two sources of data enables the estimation of LST in cloudy pixels.

Data structure

The dataset consists of geo-located continuous LST (Day, Night and Daily) which calculates LST values of cloudy pixels. The spatial domain of the data is the Eastern Mediterranean, at the resolution of the MYD11A1 product (~1 Km). Data are stored in GeoTIFF format as signed 16-bit integers using a scale factor of 0.02, with one file per day, each defined by 4 dimensions (Night LST Cont., Day LST Cont., Daily Average LST Cont., QA). The QA band stores information about the presence of cloud in the original pixel. If in both original files, Day LST and Night LST there was NoData due to clouds, then the QA value is 0. QA value of 1 indicates NoData at original Day LST, 2 indicates NoData at Night LST and 3 indicates valid data at both, day and night. File names follow this naming convention: LST_ 

Airborne LiDAR data were acquired over the East River Watershed on June 8, 2015 to August 10, 2015. The area covered was approximately 4933 square kilometers with an average point density of 10-12 points per square meter to comply with USGS's QL1 standard. Additional products include the LiDAR point cloud and derived products (including the digital elevation map, top-of-canopy elevation). The attached LIDAR acquisition report accompanies the delivered LiDAR data and documents contract specifications, data acquisition procedures, acquisition parameters (e.g., flight line trajectories, coverage maps), processing methods, and analysis of the final dataset including LiDAR accuracy and density. The metadata can be accessed by using GIS software (QGIS, ArcGIS) or remote sensing software (ENVI).

QGIS-Cloud Webmap mit thematischen Layern bzgl. hydrologischen und geowissenschaftlichen OGD - Daten in Wien

This data can be imported to GIS software, such as Quantum GIS or ESRI. Guinea, Liberia, Mali and Sierra Leone. OpenStreetMap Ebola Response

The dataset contains a map of the main classes of agricultural land use (dominant crop types and other land use types) in Germany for the year 2022. It complements a series of maps that are produced annually at the Thünen Institute beginning with the year 2017 on the basis of satellite data. The maps cover the entire open landscape, i.e., the agriculturally used area (UAA) and e.g., uncultivated areas. The map was derived from time series of Sentinel-1, Sentinel-2, Landsat 8 and additional environmental data. Map production is based on the methods described in Blickensdörfer et al. (2022).

All optical satellite data were managed, pre-processed and structured in an analysis-ready data (ARD) cube using the open-source software FORCE - Framework for Operational Radiometric Correction for Environmental monitoring (Frantz, D., 2019), in which SAR and environmental data were integrated.

The map extent covers all areas in Germany that are defined as agricultural land, grassland, small woody features, heathland, peatland or unvegetated areas according to ATKIS Basis-DLM (Geobasisdaten: © GeoBasis-DE / BKG, 2020).

Version v201:
Post-processing of the maps included a sieve filter as well as a ruleset for the reduction of non-plausible areas using the Basis-DLM and the digital terrain model of Germany (Geobasisdaten: © GeoBasis-DE / BKG, 2015).

Version v202:
Additional post-processing was performed to detect and mask additional non-plausible areas that were not adequately covered by the first post-processing (e.g., areas with sparse vegetation, montane forests) based on the „Ökosystematlas Deutschland“ (© Statistisches Bundesamt, Deutschland, 2024). As a consequence, the current version includes a new class “Small woody features on other land”. Furthermore, the class "permanent grassland" was refinded. Each pixel that was classified as "cultivated grassland" in at least five years (between 2017 and 2022) was translated to "permanent grassland" in the annual maps.

The maps are available as cloud optimized GeoTiffs, which makes downloading the full dataset optional. All data can directly be accessed in QGIS, R, Python or any supported software of your choice using the URL that will be provided on request. By doing so the entire map area or only the regions of interest can be accessed. QGIS legend files for data visualization can be downloaded separately.

Class-specific accuracies for each year are proveded in the respective tables. We provide this dataset "as is" without any warranty regarding the accuracy or completeness and exclude all liability.

References:

Blickensdörfer, L., Schwieder, M., Pflugmacher, D., Nendel, C., Erasmi, S., & Hostert, P. (2022). Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany. Remote Sensing of Environment, 269, 112831.

BKG, Bundesamt für Kartographie und Geodäsie (2015). Digitales Geländemodell Gitterweite 10 m. DGM10. https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/dgm10.pdf (last accessed: 28. April 2022).

BKG, Bundesamt für Kartographie und Geodäsie (2020). Digitales Basis-Landschaftsmodell.
https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/basis-dlm.pdf (last accessed: 28. April 2022).

Frantz, D. (2019). FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond. Remote Sensing, 11, 1124.

Statistisches Bundesamt, Deutschland (2024). Ökosystematlas Deutschland
https://oekosystematlas-ugr.destatis.de/ (last accessed: 08.02.2024).

_
National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat data (2017 to 2021) © 2024 by Schwieder, Marcel; Tetteh, Gideon Okpoti; Blickensdörfer, Lukas; Gocht, Alexander; Erasmi, Stefan; licensed under CC BY 4.0.

Funding was provided by the German Federal Ministry of Food and Agriculture as part of the joint project “Monitoring der biologischen Vielfalt in Agrarlandschaften” (MonViA, Monitoring of biodiversity in agricultural landscapes).

Use a GIS platform (arcgis or QGIS) to access the files

The actual evapotranspiration and interception (ETIa) is the sum of the soil evaporation (E), canopy transpiration (T), and evaporation from rainfall intercepted by leaves (I). The value of each pixel represents the ETIa in a given year.

Data publication: 2023-09-13

Supplemental Information:

No data value: -9999

Unit : mm/year

Scale Factor : 0.1

Map code : L1-AETI-A

Scale factor: The pixel value in the downloaded data must be multiplied by

New dekadal data layers are released approximately 5 days after the end of a dekad. A higher quality version of the same data layer is uploaded after 6 dekads have passed. This final version of the dekadal dataset has a higher quality because gap filling and interpolation processes, where needed, have been based on more data observations. This implies that other temporal aggregations (monthly, seasonal, annual), and layers that depend on those, are updated as well. Practically this means that a final annual aggregation of the most recent full calendar year can only be produced after the end of February. Likewise, the final monthly aggregation of the most recent calendar months can only be produced 2 full months later.

Citation:

FAO WaPOR database, License: CC BY-NC-SA 4.0, [Date accessed: Day/Month/Year]

Contact points:

Resource Contact: WaPOR

Metadata Contact: WaPOR

Data lineage:

The calculation is based on the WaPOR-ETLook model described in the Wapor methodology document.

The annual total is obtained by taking the ETIa in mm/day, multiplying by the number of days in a dekad, and summing the dekads of each year. See the methodology of the evapotranspiration data components (E, T and I) for further information.

Data component are developed through collaboration with eLEAF. More information can be found on the WaPOR Website.

Resource constraints:

Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)

Online resources:

Download the data from File-Browser

Download the data from Google Cloud Storage

The GIS database contains the data of aufeis (naled) in the Yana River basin (Russia) from historical and nowadays sources, and complete ArcGIS 10.1/10.2 and Qgis 3* projects to view and analyze the data. All data and projects have WGS 1984 coordinate system (without projection). ArcGIS and Qgis projects contain two layers, such as Aufeis_kadastr (historical aufeis data collection, point objects) and Aufeis_Landsat (satellite-derived aufeis data collection, polygon objects). Historical data collection is created based on the Cadastre of aufeis (naled) of the North-East of the USSR (1958). Each aufeis was digitized as point feature by the inventory map (scale 1:2 000 000), or by topographic maps. Attributive data was obtained from the Cadastre of aufeis. According to the historical data, there were 381 aufeis with a total area 731.6 km² within the studied basin. Present-day aufeis dataset was created by Landsat-8 OLI images for the period 2013-2017. Each aufeis was delineated by satellite images as polygon. Cloud-free Landsat images are obtained immediately after snowmelt season (e.g. between May, 15 and June, 18), to detect the highest possible number of aufeis. Critical values of Normalized Difference Snow Index (NDSI) were used for semi-automated aufeis detection. However, a detailed expert-based verification was performed after automated procedure, to distinguish snow-covered areas from aufeis and cross-reference historical and satellite-based data collections. According to Landsat data, the number of aufeis reaches 571, with their total area about 432 km². The difference between the Cadastre (1958) and the satellite-derived data may indicate significant changes of aufeis formation environments.

The GIS database contains the data of aufeis (naleds) in the Indigirka River basin (Russia) from historical and nowadays sources, and complete ArcGIS 10.1/10.2 and Qgis 3* projects to view and analyze the data. All data and projects have WGS 1984 coordinate system (without projection). ArcGIS and Qgis projects contain two layers, such as Aufeis_kadastr (historical aufeis data collection, point objects) and Aufeis_Landsat (satellite-derived aufeis data collection, polygon objects).

Historical data collection is created based on the Cadastre of aufeis (naleds) of the North-East of the USSR (1958). Each aufeis was digitized as point feature by the inventory map (scale 1:2 000 000), or by topographic maps. Attributive data was obtained from the Cadastre of aufeis. According to the historical data, there were 896 aufeis with a total area 2063.6 km² within the studied basin.

Present-day aufeis dataset was created by Landsat-8 OLI images for the period 2013-2017. Each aufeis was delineated by satellite images as polygon. Cloud-free Landsat images are obtained immediately after snowmelt season (e.g. between May, 15 and June, 18), to detect the highest possible number of aufeis. Critical values of Normalized Difference Snow Index (NDSI) were used for semi-automated aufeis detection. However, a detailed expert-based verification was performed after automated procedure, to distinguish snow-covered areas from aufeis and cross-reference historical and satellite-based data collections. According to Landsat data, the number of aufeis reaches 1213, with their total area about 1287 km².

The difference between the Cadastre (1958) and the satellite-derived data may indicate significant changes of aufeis formation environments.

Detailed information about the methods can be found in the publication to which this dataset is a supplement.

Ortofotmapy Lublina z lat 2003-2022 w usłudze WMS oraz plikach GeoTIFF w standardzie COG (Cloud Optimized GeoTIFF).

Sposób dodania GeoTIFF COG w programie QGIS.

Aby dodać plik GeoTIFF COG w programie QGIS, należy

1. Otworzyć Menadżer Warstw i wybrać Raster
2. Zaznaczamy opcję: Dane online
3. Protokół: bez zmian
4. URI: wklejany link z Otwartych Danych, np: https://gis.lublin.eu/pobierzdane/orto/2022.tif

https://otwartedane.lublin.eu/grafika/qgis-cog.png" alt="Dodanie GeoTIFF COG do programu QIGS" title="Dodanie GeoTIFF COG do programu QIGS">

Replication code and data for the paper "Satellite observations reveal inequalities in the progress and effectiveness of recent electrification in sub-Saharan Africa" published in One Earth (DOI: 10.1016/j.oneear.2020.03.007)

This repository hosts:

A JavaScript file to process remotely-sensed data into Google Earth Engine (step 1)

A R script, to be run after the successful completion of the Google Earth Engine processing (step 2)

Supporting files to run the analysis (e.g. a shapefile of provinces, validation data, etc.)

Create a Google account, if you do not have one, and require access to Earth Engine https://signup.earthengine.google.com.

Make sure your Google Drive has enough cloud storage space available.

Clone the repository.

Run the JavaScript file in Google Earth Engine and wait that the data processing is complete (can take >24 hours)

Run the R script, which will reproduce the analysis and the figures contained in the paper.

Open the QGIS project files to replicate maps with the appropriate layout.

Source code-related issues should be opened directly on GitHub. Broader questions of the methods should be addressed to giacomo.falchetta@feem.it

File in formato Cloud Optimized Geotiff che rappresenta la radiazione solare totale su Ferrara in Wh/m2 durante il mese di Giugno. Risoluzione 1x1 m. Le mappe sono derivate mediante l’utilizzo di algoritmi implementati in GRASS GIS, in particolare r.sun (https://grass.osgeo.org/grass83/manuals/r.sun.html). I raster contengono la somma per pixel mensile della Radiazione Solare Totale giornaliera in Wh/m2/month. Il file puo essere visualizzato all'interno di un software GIS come QGIS e caricato come Layer Raster con protocollo HTTP(S), Cloud. - Input: Modello digitale delle superfici (DSM) https://dati.comune.fe.it/dataset/dsm-2022; LINKE turbidity maps https://www.soda-pro.com/help/general-knowledge/linke-turbidity-factor. Validazione: La radiazione totale calcolata e’ stata comparata con la stazione meteorologica di Aguscello (FE) (https://meteonetwork.eu/it/weather-station/ero308-stazione-meteorologica-di-aguscello) dal 2019 al 2023.

PROZA (PROgnose ZAanstad) is een databank met milieurelevante verkeersgegevens van wegen in Zaanstad en een beperkt aantal daar buiten. De 'PROZA'-databank is een set van binnen GIS (bijv. ArcView GIS, QGIS of ArcGIS Explorer) te raadplegen databestanden met RMG2006 en CAR relevante verkeersgegevens. De entiteit waarvoor deze verkeergegevens zijn opgeslagen is een wegvak, normaliter conform de definitie van het Nationaal Wegenbestand (NWB).

File in formato Cloud Optimized Geotiff che rappresenta la radiazione solare totale su Ferrara in Wh/m2 durante il mese di Settembre. Risoluzione 1x1 m. Le mappe sono derivate mediante l’utilizzo di algoritmi implementati in GRASS GIS, in particolare r.sun (https://grass.osgeo.org/grass83/manuals/r.sun.html). I raster contengono la somma per pixel mensile della Radiazione Solare Totale giornaliera in Wh/m2/month. Il file puo essere visualizzato all'interno di un software GIS come QGIS e caricato come Layer Raster con protocollo HTTP(S), Cloud. - Input: Modello digitale delle superfici (DSM) https://dati.comune.fe.it/dataset/dsm-2022; LINKE turbidity maps https://www.soda-pro.com/help/general-knowledge/linke-turbidity-factor. Validazione: La radiazione totale calcolata e’ stata comparata con la stazione meteorologica di Aguscello (FE) (https://meteonetwork.eu/it/weather-station/ero308-stazione-meteorologica-di-aguscello) dal 2019 al 2023.

File in formato Cloud Optimized Geotiff che rappresenta la radiazione solare totale su Ferrara in Wh/m2 durante il mese di Ottobre. Risoluzione 1x1 m. Le mappe sono derivate mediante l’utilizzo di algoritmi implementati in GRASS GIS, in particolare r.sun (https://grass.osgeo.org/grass83/manuals/r.sun.html). I raster contengono la somma per pixel mensile della Radiazione Solare Totale giornaliera in Wh/m2/month. Il file può essere visualizzato all'interno di un software GIS come QGIS e caricato come Layer Raster con protocollo HTTP(S), Cloud. - Input: Modello digitale delle superfici (DSM) https://dati.comune.fe.it/dataset/dsm-2022; LINKE turbidity maps https://www.soda-pro.com/help/general-knowledge/linke-turbidity-factor. Validazione: La radiazione totale calcolata e’ stata comparata con la stazione meteorologica di Aguscello (FE) (https://meteonetwork.eu/it/weather-station/ero308-stazione-meteorologica-di-aguscello) dal 2019 al 2023.

File in formato Cloud Optimized Geotiff che rappresenta la radiazione solare per il mese di Gennaio su Ferrara in Wh/m2/day. Risoluzione 1x1 m realizzato da Fondazione Bruno Kessler (FBK) nell'ambito del progetto USAGE.. Le mappe sono derivate mediante l’utilizzo di algoritmi implementati in GRASS GIS, in particolare r.sun (https://grass.osgeo.org/grass83/manuals/r.sun.html). I raster contengono la somma per pixel mensile della Radiazione Solare Totale giornaliera in Wh/m2/month. Il file puo essere visualizzato all'interno di un software GIS come QGIS e caricato come Layer Raster con protocollo HTTP(S), Cloud. - Input: Modello digitale delle superfici (DSM) https://dati.comune.fe.it/dataset/dsm-2022; LINKE turbidity maps https://www.soda-pro.com/help/general-knowledge/linke-turbidity-factor. Validazione: La radiazione totale calcolata e’ stata comparata con la stazione meteorologica di Aguscello (FE) (https://meteonetwork.eu/it/weather-station/ero308-stazione-meteorologica-di-aguscello) dal 2019 al 2023.