MontadoDB: A Comprehensive Dataset of Pasture Parameters in the Sourthern Region of Portugal
Description
This data comprises 705 samples, including collection date and day of the year (Date and DOY), sample region (Sample), geographical coordinates (Lat and Long), and the parameters collected from the laboratory: Pasture Measure Content, Crude Protein, Neutral Detergent Fiber, Fresh Weigth, Dry Matter (PMC (%), PB (g/100g), NDF (g/100g), Kg MS/ha), with its BoxCox transformation (PMC (%)_BoxCox, PB (g/100g)_BoxCox, NDF (g/100g)_BoxCox, Kg MS/ha_BoxCox) for data normalization. Multispectral and weather data were collected for each sample coordinate. The spectral data comes from Google Earth Engine on four satellites (Landsat-8, Sentinel-2, Sentinel-3, and MODIS) and their respective collections and tiers. - Sentinel-2 Surface reflectance : B1, B2, B3, B4, B5, B6, B7, B8, B8A, B9, B11, B12 - Sentinel-2 Top-of-atmosphere: B1, B2, B3, B4, B5, B6, B7, B8, B8A, B9, B10, B11, B12 - Landsat-8 Surface reflectance tier 1: SR_B1, SR_B2, SR_B3, SR_B4, SR_B5, SR_B6, SR_B7 - Landsat-8 Top-of-atmosphere (tier 1 / 1 + realtime): B1, B2, B3, B4, B5, B6, B7, B8, B9, B10, B11 - Landsat-8 Raw (tier 1 / 1 + realtime): B1, B2, B3, B4, B5, B6, B7, B8, B9, B10, B11 - Sentinel-3: Oa02, Oa03, Oa04, Oa05, Oa06, Oa07, Oa08, Oa09, Oa11, Oa12, Oa13, Oa14, Oa15, Oa16, Oa17, Oa18, Oa19, Oa20, Oa21 - MODIS: NDVI, EVI The spectral bands columns are preceded by a column that indicates the date of the collected image. Every column name highlights the type of satellite, its collection, and tier, e.g., Landsat-8_SR_1, which refers to the Landsat-8 satellite, surface reflectance collection, and tier 1. The weather data can be acquired from Open weather and Open Meteo APIs, fetching the following weather conditions: Maximum and Minimum Temperature acquired (TEMP_MAX (°C), TEMP_MIN (°C)), Average Solar Radiation (RAD_SOL (J/M2)), Average rainfall recorded during the day (RAIN (mm)), Average Wind Speed (WIND_SPD (km/h)), Average Estimated Soil Evapotranspiration (EVAPOT (mm)), Average Atmospheric Pressure (PRES_ATM (hPa)) and Average Relative Humidity (HUM_REL (%)). The "dataset" folder includes eight files: "Experimental_data.csv", "Sentinel-2.csv", "Sentinel-3.csv", "Landsat-8.csv", "Modis.csv", "Complete_data.csv", "Weather_data.csv", and "Satellite_info.json". "Experimental_data.csv" and "Weather_data.csv" contain only samples and weather data, respectively. "Each satellite CSV includes Forage parameters, spectral bands, and weather data. The "Complete_data.csv" merges all the other files. "Satellite_info.json" contains auxiliary satellite data for GEE and dataset build. The "scripts" folder contains two necessary modules, one for GEE functions - "multispectral_api.py", and another for Weather API calls - "weather_api.py", and a notebook Python file ("build_dataset.ipynb") that uses both modules to fetch the data and build the dataset.
Files
Steps to reproduce
If you want to extract multispectral and weather data using APIs, please follow the next steps: 1. On-site https://developers.google.com/earth-engine/guides/service_account create your private key to access the Google Earth Engine API to acquire hyperspectral data. 2. On-site https://openweathermap.org/appid, create your weather key to acquire weather parameters in Open Weather Map API, a paid service. If you want to use the Open Meteo free API, skip this step. 3 - Insert the Google Earth Engine credentials in the "build_dataset.ipynb" file. 4 - Insert the Open Weather Map key in the file "weather_api.py" at the indicated place. Only necessary if step 2 is done. 5 - Follow the Python notebook instructions.
Institutions
- Universidade Federal de Mato Grosso do Sul