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

IF 5.6 1区 农林科学 Q1 AGRONOMY Field Crops Research Pub Date : 2024-10-11 DOI:10.1016/j.fcr.2024.109610
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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.
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结合卫星传感数据、地面数据和 BASGRA 模型预测高纬度地区的牧草产量
背景在高纬度地区,生长季节和冬季多变的天气条件会导致牧草产量的巨大差异。预测草地状况和产量的工具,如实地测量、卫星图像分析和基于过程的模拟模型,可以结合起来为草地管理提供决策支持。在此,我们根据挪威北部临时草地的干物质和叶面积指数数据对BASic GRAssland (BASGRA)模型进行了校准和验证。这项研究的目的是比较根据以下数据校准的模型版本的性能:i) 特定地区的地面数据;ii) 特定地区的地面数据和哨兵-2卫星数据;iii) 其他地区的实地试验数据。方法从四个地点的 13 块非永久性草场获取地面和卫星传感数据,包括 2020 年至 2022 年的生物量干物质、叶面积指数以及秋季和春季地面覆盖物。这些数据连同土壤和每日天气数据,以及有关收割和氮肥施用制度的信息被输入到 BASGRA 校准中。结果在数据集中,春季启动模型比秋季启动模型(归一化均方根误差为 41.1-93.4 %)的干物质预测精度更高(归一化均方根误差为 22.3-54.0 %)。与来自其他地区的校准相比,特定地区的校准可获得更准确的生物量预测,而除了地面数据外,使用卫星传感数据仅导致生物量预测准确性的微小变化。我们的研究结果表明,与使用其他地区的受控田间试验数据相比,来自农民田间的区域数据可大幅提高BASGRA模型的性能。这强调了在估算跨地理区域的草地生产潜力和压力影响时,需要考虑非永久性草地的区域多样性。在草地模型校准中进一步使用卫星数据可能会受益于更详细地评估草地生长特性以及光和云条件对遥感估计草地叶面积指数和生物量的影响。
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来源期刊
Field Crops Research
Field Crops Research 农林科学-农艺学
CiteScore
9.60
自引率
12.10%
发文量
307
审稿时长
46 days
期刊介绍: Field Crops Research is an international journal publishing scientific articles on: √ experimental and modelling research at field, farm and landscape levels on temperate and tropical crops and cropping systems, with a focus on crop ecology and physiology, agronomy, and plant genetics and breeding.
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