Abstract A large proportion of the global land surface is covered by pasture. The advent of the Sentinel satellites program provides free datasets with good spatiotemporal resolution that can be a valuable source of information for monitoring pasture resources. We combined optical remote sensing data (proximal hyperspectral and Sentinel 2A) with a radiative transfer model (PROSAIL) to estimate leaf Area Index (LAI), and biomass, in a dairy farming context. Three sites in Southern England were used: two pasture farms that differed in pasture type and management, and a set of small agronomy trial plots with different mixtures of grasses, legumes and herbs, as well as pure perennial ryegrass. The proximal and satellite spectral data were used to retrieve LAI via PROSAIL model inversion, which were compared against field observations of LAI. The potential of bands of Sentinel 2A that corresponded with a 10 m resolution was studied by convolving narrow spectral bands (from a handheld hyperspectral sensor) into Sentinel 2A bands (10 m). Retrieved LAI, using these spectrally resampled S2A data, compared well with measured LAI, for all sites, even for those with mixed species cover (although retrieved LAI was somewhat overestimated for pasture mixtures with high LAI). This proved the suitability of 10 m Sentinel 2A spectral bands for capturing LAI dynamics for different types of pastures. We also found that inclusion of 20 m bands in the inversion scheme did not lead to any further improvement in retrieved LAI. Sentinel 2A image based retrieval yielded good agreement with LAI measurements obtained for a typical perennial ryegrass based pasture farm. LAI retrieved in this way was used to create biomass maps (that correspond to indirect biomass measurements by Rising Plate Meter (RPM)), for mixed-species paddocks for a farm for which limited field data were available. These maps compared moderately well with farmer-collected RPM measurements for this farm. We propose that estimates of paddock-averaged and within-paddock variability of biomass are more reliably obtained from a combined Sentinel 2A-PROSAIL approach, rather than by manual RPM measurements. The physically based radiative transfer model inversion approach outperformed the Normalized Difference Vegetation Index based retrieval method, and does not require site specific calibrations of the inversion scheme.

中文翻译：

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使用基于物理的辐射传输模型将 Sentinel-2A 数据应用于牧场生物量监测

摘要 全球陆地表面的很大一部分被牧场覆盖。哨兵卫星计划的出现提供了具有良好时空分辨率的免费数据集，可以成为监测牧场资源的宝贵信息来源。我们将光学遥感数据（近端高光谱和哨兵 2A）与辐射传输模型 (PROSAIL) 相结合，以估计奶牛养殖环境中的叶面积指数 (LAI) 和生物量。使用了英格兰南部的三个地点：两个牧场类型和管理不同的牧场，以及一组小型农学试验地，混合了不同的草、豆类和草药，以及纯多年生黑麦草。近端和卫星光谱数据用于通过 PROSAIL 模型反演反演 LAI，并将其与 LAI 的现场观测进行比较。通过将窄光谱波段（来自手持高光谱传感器）卷积到 Sentinel 2A 波段（10 m）中，研究了与 10 m 分辨率对应的 Sentinel 2A 波段的潜力。使用这些光谱重采样的 S2A 数据检索到的 LAI，与测量的 LAI 相比，对于所有站点，甚至对于那些具有混合物种覆盖的站点（尽管对于具有高 LAI 的牧场混合物，检索到的 LAI 有点高估）。这证明了 10 m Sentinel 2A 光谱带适用于捕获不同类型牧场的 LAI 动态。我们还发现，在反演方案中包含 20 m 波段并没有导致检索到的 LAI 的任何进一步改进。基于 Sentinel 2A 图像的检索与针对典型多年生黑麦草牧场获得的 LAI 测量结果非常一致。以这种方式检索的 LAI 用于创建生物量图（对应于 Rising Plate Meter (RPM) 的间接生物量测量），用于农场的混合物种围场，其可用的田间数据有限。这些地图与该农场的农民收集的 RPM 测量值比较得当。我们建议通过组合 Sentinel 2A-PROSAIL 方法而不是通过手动 RPM 测量更可靠地获得围场平均和围场内生物量变异性的估计值。基于物理的辐射传输模型反演方法优于基于归一化差异植被指数的反演方法，并且不需要对反演方案进行现场特定校准。为一个农场的混合物种围场提供有限的现场数据。这些地图与该农场的农民收集的 RPM 测量值比较得当。我们建议通过组合 Sentinel 2A-PROSAIL 方法而不是通过手动 RPM 测量更可靠地获得围场平均和围场内生物量变异性的估计值。基于物理的辐射传输模型反演方法优于基于归一化差异植被指数的反演方法，并且不需要反演方案的现场特定校准。为一个农场的混合物种围场提供有限的现场数据。这些地图与该农场的农民收集的 RPM 测量值比较得当。我们建议通过组合 Sentinel 2A-PROSAIL 方法而不是通过手动 RPM 测量更可靠地获得围场平均和围场内生物量变异性的估计值。基于物理的辐射传输模型反演方法优于基于归一化差异植被指数的反演方法，并且不需要反演方案的现场特定校准。我们建议通过组合 Sentinel 2A-PROSAIL 方法而不是通过手动 RPM 测量更可靠地获得围场平均和围场内生物量变异性的估计值。基于物理的辐射传输模型反演方法优于基于归一化差异植被指数的反演方法，并且不需要反演方案的现场特定校准。我们建议通过组合 Sentinel 2A-PROSAIL 方法而不是通过手动 RPM 测量更可靠地获得围场平均和围场内生物量变异性的估计值。基于物理的辐射传输模型反演方法优于基于归一化差异植被指数的反演方法，并且不需要反演方案的现场特定校准。