Quantifying Uncertainty and Bridging the Scaling Gap in the Retrieval of Leaf Area Index by Coupling Sentinel-2 and UAV Observations
Quantifying Uncertainty and Bridging the Scaling Gap in the Retrieval of Leaf Area Index by Coupling Sentinel-2 and UAV Observations
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DOI：
10.3390/rs12111843
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发表时间：
2020-06
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影响因子：
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通讯作者：
A. Revill;Anna Florence;A. MacArthur;S. Hoad;R. Rees;M. Williams
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文献类型：
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作者：
A. Revill;Anna Florence;A. MacArthur;S. Hoad;R. Rees;M. Williams
叶面积指数 (LAI) 估计可以为作物管理决策提供信息。欧洲航天局的 Sentinel-2 卫星在红边光谱区域进行观测，可以以亚田空间分辨率（10-20 m）监测全球农作物。然而，卫星 LAI 估算需要通过地面测量进行校准。校准面临空间异质性以及现场测量和卫星测量之间尺度不匹配的挑战。无人机 (UAV) 生成高分辨率（厘米级）LAI 估计值，提供中间观测结果，我们在此使用这些观测值来表征不确定性并减少 Sentinel-2 观测值与现场调查之间的空间尺度差异。我们使用一种新型无人机多光谱传感器，该传感器与 Sentinel-2 光谱带相匹配，并与 LAI 地面测量相结合进行飞行。无人机和实地调查在多个日期（与不同的小麦生长阶段一致）进行，这些日期对应于 Sentinel-2 立交桥。我们比较了来自 Sentinel-2 和 UAV 平台的叶绿素红边指数 (CIred-edge) 图。我们使用高斯过程回归机器学习根据地面数据校准 LAI 的无人机模型。使用无人机 LAI，我们评估了一种两阶段校准方法，用于从 Sentinel-2 生成可靠的 LAI 估计值。 Sentinel-2 和无人机 CIred-edge 值之间的一致性随着生长阶段的增加而增加 - R2 范围从 0.32（茎伸长）到 0.75（乳汁发育）。由于小麦冠层更加均匀和封闭，两个平台之间的 CIred-edge 方差在生长季节后期更具可比性。与使用无人机数据的两级校准（平均 R2 = 0.88，平均 NRMSE = 8%）相比，单级 Sentinel-2 LAI 校准（即从地面测量直接校准）表现较差（平均 R2 = 0.29，平均 NRMSE = 17%）。两阶段方法将错误和偏差减少了 50% 以上。通过扩大地面测量规模并提供更具代表性的模型训练样本，无人机观测为增强 Sentinel-2 小麦 LAI 反演提供了有效且可行的方法。我们预计，我们解决空间异质性的无人机校准方法将提高 LAI 和其他耕作作物类型和更广泛的植被覆盖类型的其他生物物理变量的检索精度。
Leaf area index (LAI) estimates can inform decision-making in crop management. The European Space Agency’s Sentinel-2 satellite, with observations in the red-edge spectral region, can monitor crops globally at sub-field spatial resolutions (10–20 m). However, satellite LAI estimates require calibration with ground measurements. Calibration is challenged by spatial heterogeneity and scale mismatches between field and satellite measurements. Unmanned Aerial Vehicles (UAVs), generating high-resolution (cm-scale) LAI estimates, provide intermediary observations that we use here to characterise uncertainty and reduce spatial scaling discrepancies between Sentinel-2 observations and field surveys. We use a novel UAV multispectral sensor that matches Sentinel-2 spectral bands, flown in conjunction with LAI ground measurements. UAV and field surveys were conducted on multiple dates—coinciding with different wheat growth stages—that corresponded to Sentinel-2 overpasses. We compared chlorophyll red-edge index (CIred-edge) maps, derived from the Sentinel-2 and UAV platforms. We used Gaussian processes regression machine learning to calibrate a UAV model for LAI, based on ground data. Using the UAV LAI, we evaluated a two-stage calibration approach for generating robust LAI estimates from Sentinel-2. The agreement between Sentinel-2 and UAV CIred-edge values increased with growth stage—R2 ranged from 0.32 (stem elongation) to 0.75 (milk development). The CIred-edge variance between the two platforms was more comparable later in the growing season due to a more homogeneous and closed wheat canopy. The single-stage Sentinel-2 LAI calibration (i.e., direct calibration from ground measurements) performed poorly (mean R2 = 0.29, mean NRMSE = 17%) when compared to the two-stage calibration using the UAV data (mean R2 = 0.88, mean NRMSE = 8%). The two-stage approach reduced both errors and biases by >50%. By upscaling ground measurements and providing more representative model training samples, UAV observations provide an effective and viable means of enhancing Sentinel-2 wheat LAI retrievals. We anticipate that our UAV calibration approach to resolving spatial heterogeneity would enhance the retrieval accuracy of LAI and additional biophysical variables for other arable crop types and a broader range of vegetation cover types.