ABSTRACT

Forage supply of savanna grasslands plays a crucial role for local food security and consequently, a reliable monitoring system could help to better manage vital forage resources. To help installing such a monitoring system, we investigated whether in-situ hyperspectral data could be resampled to match the spectral resolution of multi- and hyperspectral satellites; if the type of sensor affected model transfer; and if spatio-temporal patterns of forage characteristics could be related to environmental drivers. We established models for forage quantity (green biomass) and five forage quality proxies (metabolisable energy, acid/neutral detergent fibre, ash, phosphorus). Hyperspectral resolution of the Hyperion satellite mostly resulted in higher accuracies (i.e. higher R², lower RMSE). When applied to satellite data, though, the greater quality of the multispectral Sentinel-2 satellite data leads to more realistic forage maps. By analysing a three-year time series, we found plant phenology and cumulated precipitation to be the most important environmental drivers of forage supply. We conclude that none of the investigated satellites provide optimal conditions for monitoring purposes. Future hyperspectral satellite missions like EnMAP, combining the high information level of Hyperion with the good data quality and resolution of Sentinel-2, will provide the prerequisites for installing a regular monitoring service.

摘要

稀树草原的草料供应对当地粮食安全起着至关重要的作用，因此，可靠的监测系统有助于更好地管理重要的草料资源。为了帮助安装这样一个监测系统，我们研究了原位高光谱数据是否可以重新采样以匹配多光谱和高光谱卫星的光谱分辨率；传感器类型是否影响模型转移；以及牧草特性的时空模式是否可能与环境驱动因素有关。我们建立了草料数量（绿色生物量）和五种草料质量指标（代谢能、酸性/中性洗涤纤维、灰分、磷）的模型。Hyperion 卫星的高光谱分辨率主要导致更高的精度（即更高的 R ²，较低的 RMSE）。然而，当应用于卫星数据时，更高质量的多光谱 Sentinel-2 卫星数据会导致更逼真的草料地图。通过分析三年时间序列，我们发现植物物候和累积降水是草料供应最重要的环境驱动因素。我们得出的结论是，所调查的卫星都没有为监测目的提供最佳条件。未来的高光谱卫星任务，如 EnMAP，将 Hyperion 的高信息水平与 Sentinel-2 的良好数据质量和分辨率相结合，将为安装定期监测服务提供先决条件。