A Dataset with Pasture Chemical Analysis and Satellite Hyperspectral and Climate Data

Published: 15 September 2023| Version 1 | DOI: 10.17632/8tjgtkktky.1
Guilherme Defalque


During 12 months in two paddocks with Brachiaria brizantha cv, Marandu forage, forage collection was carried out (cut flush to the ground) with gardening shears. The data were collected from April 6, 2022, until March 1, 2023, in a paddock with animals (4 Nelore breed animals, with +/- 203kg) and another paddock without animals. Two collection types were performed: every 15 days in a square of 1m² randomly released in both paddocks and containment cages of 1 cubic meter, collected every 30 days, and placed in the paddock with animals. In each sample, coordinates (latitude and longitude) were collected using GPS. The laboratory chemical analysis was performed on all samples to estimate forage parameters: Crude Protein (CP), Acid Detergent Fiber (ADF), Neutral Detergent Fiber (NDF), Dry Matter content (DM), Biomass Content, Mineral Metter (MM) and, Total Digestible Nutrients (TDN). For each GPS coordinate, satellite hyperspectral and climate data were acquired. The hyperspectral data were collected using Google Earth Engine API, based on Sentinel-2 hyperspectral images. Twenty sentinel-2 bands (B01, B02, B03, B04, B05, B06, B07, 8A, B09, B11, and B12) were acquired, and eight well-known spectral indices (NDVI, NDWI, EVI, LAI, DVI, GCI, GEMI, and SAVI) were calculated and integrated into the dataset. Environmental data were acquired in two ways: using two weather APIs (Open Wheater MAP and Open-Meteo) and data from an existing meteorological station at the sample collection site. The climate data acquired are Maximum and Minimum Temperature Acquired during the day (TEMP_MAX, TEMP_MIN), Average of Solar Radiation during the day (RAD_SOL), Average average registered during the day (RAIN), Average wind speed registered during the day (WIND_SPD), Average evapotranspiration estimated of the soil during the day (EVAPOT), Average Atmospheric Pressure registered during the day (PREST_ATM) and, Average Relative humidity registered during the day (HUM_REL). Finally, data related to the day of collection (date and Day of Year - DOY), sample coordinates (latitude and longitude), and sample type (ID that identifies the type of sample): Paddock with animals: - Q1 - Q4: square 1 - 4, - G1 - G4: cage 1 - 4, Paddock without animals: - S1 or S2: square 1 or 2 Were integrated into the complete dataset. In the Folder called "data" there are three files: “Field_Experiment_Data.csv”, “Field_Hypespectral_API_Climate_Data.csv,” and “Complete_DataSet.csv”. The firs file, contains just pasture chemical analysis values; in the second, hyperspectral and climate data acquired on APIs are included, and in the last, climate data obtained from the weather station is integrated. In the "src" folder, the Python script to acquire hyperspectral and climate data from APIs is called “Search_Images_and_Weather_Data.ipynb”. The file "weatherapi.py" receives data to estimate weather information from both APIs.


Steps to reproduce

The complete dataset with chemical analysis, hypespectral, climate data acquired by APIs and weather data acquired by meteorological station is in the file "Complete_DataSet.csv". If you want to extract hyperspectral and climate 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 acess 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. 3 - Insert the Google Earth Engine credentials in the file "Search_Images_and_Weather_Data.ipynb". 4 - Insert the Open Weather Map key in the file "weather.py" at the indicated place. 5 - Put the files "Search_Images_and_Weather_Data.ipynb", "weatherapi.py", and "Field_Experiment_Data.csv" in the same folder and run the script Search_Images_and_Weather_Data.


Universidade Federal de Mato Grosso do Sul


Agricultural Science, Animal Science, Internet of Things, Beef Cattle, Satellite Remote Sensing, Precision Agriculture, Pasture Nutrition