The Value of Sentinel-2 Spectral Bands for the Assessment of Winter Wheat Growth and Development
The Value of Sentinel-2 Spectral Bands for the Assessment of Winter Wheat Growth and Development
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DOI：
10.3390/rs11172050
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发表时间：
2019-09-01
期刊：
影响因子：
5
通讯作者：
Williams, Mathew
中科院分区：
文献类型：
--
作者：
Revill, Andrew;Florence, Anna;Williams, Mathew
叶面积指数（LAI）和叶绿素含量与植物发育和生产力密切相关。这些变量的空间和时间估计对于高效和精确的作物管理至关重要。欧洲航天局 (ESA) Sentinel-2 卫星提供的开放获取数据可提供全球覆盖，平均 5 天重访频率高达 10 米的空间分辨率，可以以前所未有的（即子场）分辨率对这些变量进行估计。过去的研究利用合成数据证明了 Sentinel-2 在估计作物变量方面的潜力。尽管如此，涉及对 Sentinel-2 频段支持农业应用进行稳健分析的研究仍然有限。我们评估了 Sentinel-2 数据在检索冬小麦 LAI、叶片叶绿素含量 (LCC) 和冠层叶绿素含量 (CCC) 方面的潜力。配合破坏性和非破坏性地面测量，我们从安装在无人机 (UAV) 上的传感器获取多光谱数据，测量关键的 Sentinel-2 光谱带（443 至 865 nm）。我们应用高斯过程回归 (GPR) 机器学习来确定信息最丰富的 Sentinel-2 波段来检索每个变量。我们进一步评估了传播观测不确定性时的探地雷达模型性能。当应用性能最佳的探地雷达模型且不存在传播不确定性时，反演结果与地面测量结果高度一致——平均 R-2 和归一化均方根误差 (NRMSE) 分别为 0.89 和 8.8%。当传播不确定性时，平均 R-2 和 NRMSE 分别为 0.82 和 11.9%。当考虑 LAI 和 CCC 估计中的测量不确定性时，信息最丰富的 Sentinel-2 波段的数量从 4 个减少到只有 2 个：红边 (705 nm) 和近红外 (865 nm) 波段。这项研究证明了 Sentinel-2 光谱特征对于检索关键变量的价值，这些变量可以支持更可持续的作物管理实践。
Leaf Area Index (LAI) and chlorophyll content are strongly related to plant development and productivity. Spatial and temporal estimates of these variables are essential for efficient and precise crop management. The availability of open-access data from the European Space Agency's (ESA) Sentinel-2 satellite-delivering global coverage with an average 5-day revisit frequency at a spatial resolution of up to 10 metres-could provide estimates of these variables at unprecedented (i.e., sub-field) resolution. Using synthetic data, past research has demonstrated the potential of Sentinel-2 for estimating crop variables. Nonetheless, research involving a robust analysis of the Sentinel-2 bands for supporting agricultural applications is limited. We evaluated the potential of Sentinel-2 data for retrieving winter wheat LAI, leaf chlorophyll content (LCC) and canopy chlorophyll content (CCC). In coordination with destructive and non-destructive ground measurements, we acquired multispectral data from an Unmanned Aerial Vehicle (UAV)-mounted sensor measuring key Sentinel-2 spectral bands (443 to 865 nm). We applied Gaussian processes regression (GPR) machine learning to determine the most informative Sentinel-2 bands for retrieving each of the variables. We further evaluated the GPR model performance when propagating observation uncertainty. When applying the best-performing GPR models without propagating uncertainty, the retrievals had a high agreement with ground measurements-the mean R-2 and normalised root-mean-square error (NRMSE) were 0.89 and 8.8%, respectively. When propagating uncertainty, the mean R-2 and NRMSE were 0.82 and 11.9%, respectively. When accounting for measurement uncertainty in the estimation of LAI and CCC, the number of most informative Sentinel-2 bands was reduced from four to only two-the red-edge (705 nm) and near-infrared (865 nm) bands. This research demonstrates the value of the Sentinel-2 spectral characteristics for retrieving critical variables that can support more sustainable crop management practices.