This dataset provides two years of detailed in-situ measurements of grassland biophysical parameters, including LAI, fPAR, soil moisture, chlorophyll content, and biomass. It supports the ScaleAgData objective of improving agri-environmental monitoring by providing high-quality ground reference data. The dataset complements Earth Observation products by capturing fine-scale grassland dynamics, including management events like mowing. It contributes to validating and calibrating remote sensing models for biomass estimation and land productivity assessment. The aim is to enable more accurate, scalable monitoring tools for sustainable grassland management and policy support.

This dataset provides comprehensive in-situ measurements of key biophysical and agri-environmental variables collected over two complete years. It includes high-resolution observations of:

• Leaf Area Index (LAI)

• Fraction of absorbed photosynthetically active radiation (fPAR)

• Soil moisture

• Chlorophyll content

• approximate height using a plate meter and measuring ruler

• Wet and dry biomass

• Geo-tagged photographic documentation of the monitored plots during each acquisition session

The data were collected across eight different managed grasslands in the South Tyrol and Trento Provinces, North part of Italy. It follows consistent protocols aimed at capturing seasonal dynamics and climate variability effects on grassland productivity and health. The accompanying photos offer visual reference for field conditions, supporting qualitative assessments.

This dataset is intended to serve as a high-quality ground reference to complement and validate remote sensing products, enhance Earth Observation data applications, and support the development of precision agriculture services and agri-environmental monitoring frameworks.

Keywords: Leaf Area Index, fPAR, soil moisture, chlorophyll, biomass, grasslands, remote sensing validation, in-situ observations, ground truth, agri-environmental monitoring.

Intended Use:

• Calibration and validation of satellite-derived vegetation indices

• Development of machine learning models combining remote sensing and ground data

• Research on grassland productivity, climate response, and land use impacts

• Supporting European initiatives like the Common Agricultural Policy (CAP) monitoring, Farm to Fork, and the Agriculture of Data partnership

Funding: Horizon Europe, ScaleAgData project (Grant Agreement No. 101086355)