Abstract Leaf area index (LAI) is a key variable for characterizing crop growth conditions and estimating crop productivity. Despite continuing efforts to develop LAI estimation algorithms, LAI datasets still need improvement at spatial and temporal resolutions to meet the requirements of agricultural applications. Advancements in data fusion technique and the emergence of new satellite data provide opportunities for LAI data at higher resolutions in both space and time. In this study, we derived new LAI estimations by leveraging novel satellite remote sensing datasets, STAIR fusion (MODIS-Landsat fusion) and Planet Labs' CubeSat data (through a reprocessed pipeline) for a typical agricultural landscape in the U.S. Corn Belt. The STAIR fused data and our reprocessed CubeSat data have both fine spatial resolutions (30 m and 3.125 m, respectively) and high frequencies (daily for both). To reliably estimate LAI from these advanced satellite datasets, we used two methods: inversion of a radiative transfer model (RTM), and empirical relationship with vegetation index (VI) calibrated from field measured LAI. Compared to the ground-truth LAI collected at 36 sites across the study region, reliable approximations were achieved by both LAI estimations based on PROSAIL RTM (STAIR: R2 = 0.69 and root mean squared error (RMSE) = 1.12 (m2 m−2), CubeSat: R2 = 0.76 and RMSE = 1.09 (m2 m−2)), and LAI estimations based on Green Wide Dynamic Range Vegetation Index (GrWDRVI) (STAIR: R2 = 0.75, RMSE = 1.10 (m2 m−2), CubeSat: R2 = 0.76, RMSE = 1.08 (m2 m−2), where validation ground-truth is independent from calibration data). Newly estimated high-resolution LAI data were aggregated at 500 m resolution and compared with MODIS and VIIRS LAI products, revealing substantial uncertainties and biases in these two products. We also demonstrated phenology stage estimation at fine spatial resolutions based on our high-frequency LAI data. The proposed LAI estimation methods at both high spatial resolution and temporal frequency can be applied to the entire U.S. Corn Belt and provide significant advancement to crop monitoring and precision agriculture.

中文翻译：

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使用 Planet Labs CubeSat 和 STAIR 融合数据推导出美国玉米带农业生态系统的高时空分辨率叶面积指数

摘要 叶面积指数 (LAI) 是表征作物生长条件和估计作物生产力的关键变量。尽管不断努力开发 LAI 估计算法，但 LAI 数据集仍然需要在空间和时间分辨率上进行改进以满足农业应用的要求。数据融合技术的进步和新卫星数据的出现为更高分辨率的空间和时间LAI数据提供了机会。在这项研究中，我们利用新型卫星遥感数据集、STAIR 融合（MODIS-Landsat 融合）和 Planet Labs 的 CubeSat 数据（通过重新处理的管道）为美国玉米带的典型农业景观推导出了新的 LAI 估计值。STAIR 融合数据和我们重新处理的 CubeSat 数据都具有良好的空间分辨率（30 m 和 3.125 m，分别）和高频（两者都是每天）。为了从这些先进的卫星数据集可靠地估计 LAI，我们使用了两种方法：辐射传输模型 (RTM) 的反演，以及与从现场测量的 LAI 校准的植被指数 (VI) 的经验关系。与在整个研究区域的 36 个站点收集的地面实况 LAI 相比，基于 PROSAIL RTM（阶梯：R2 = 0.69 和均方根误差 (RMSE) = 1.12 (m2 m−2) 的两种 LAI 估计都实现了可靠的近似值） , CubeSat：R2 = 0.76 和 RMSE = 1.09 (m2 m−2))，以及基于绿色宽动态范围植被指数 (GrWDRVI) 的 LAI 估计（阶梯：R2 = 0.75，RMSE = 1.10 (m2 m−2)，CubeSat ：R2 = 0.76，RMSE = 1.08 (m2 m−2)，其中验证地面实况与校准数据无关）。新估计的高分辨率 LAI 数据在 500 m 分辨率下汇总，并与 MODIS 和 VIIRS LAI 产品进行比较，揭示了这两种产品的大量不确定性和偏差。我们还展示了基于我们的高频 LAI 数据在精细空间分辨率下的物候阶段估计。所提出的高空间分辨率和时间频率的 LAI 估计方法可以应用于整个美国玉米带，并为作物监测和精准农业提供重大进步。