Combining satellite-sensed and ground data and the BASGRA model to predict grass yield in high-latitude regions

Abstract

Context

In high-latitude regions, variable weather conditions during the growing season and in winter cause considerable variation in forage grass productivity. Tools for predicting grassland status and yield, such as field measurements, satellite image analysis and process-based simulation models, can be combined in decision support for grassland management. Here, we calibrated and validated the BASic GRAssland (BASGRA) model against dry matter and Leaf area index data from temporary grasslands in northern Norway.

Objective

The objective of this study was to compare the performance of model versions calibrated against i) only region-specific ground data, ii) both region-specific ground and Sentinel-2 satellite data and, iii) field trial data from other regions.

Methods

Ground and satellite sensed data including biomass dry matter, leaf area index, and autumn and spring ground cover from 2020 to 2022 were acquired from 13 non-permanent grassland fields at four locations. These data were input to BASGRA calibrations together with soil and daily weather data, and information about cutting and nitrogen fertilizer application regimes. The effect of the winter season was taken into account in simulations by initiating the simulations either in autumn or in early spring.

Results

Within datasets, initiating the model in spring resulted in higher dry matter prediction accuracy (normalised RMSE 22.3–54.0 %) than initiating the model in autumn (normalised RMSE 41.1–93.4 %). Regional specific calibrations resulted in more accurate biomass predictions than calibrations from other regions while using satellite sensing data in addition to ground data resulted in only minor changes in biomass prediction accuracy.

Conclusion

All regional calibrations against data from northern Norway changed model parameter values and improved dry matter prediction accuracy compared with the reference calibration parameter values. Including satellite-sensed data in addition to ground data in calibrations did not further increase prediction accuracy compared with using only ground data.

Implications

Our findings show that regional data from farmers’ fields can substantially improve the performance of the BASGRA model compared to using controlled field trial data from other regions. This emphasises the need to account for regional diversity in non-permanent grassland when estimating grassland production potential and stress impact across geographic regions. Further use of satellite data in grassland model calibrations would probably benefit from more detailed assessments of the effect of grass growth characteristics and light and cloud conditions on estimates of grassland leaf area index and biomass from remote sensing.