Solar-induced chlorophyll fluorescence (SIF) is a signal directly and functionally related to photosynthetic activity and thus holds great promise for large-scale agricultural monitoring. However, the coarse spatial resolution of existing satellite SIF observations usually consist of mixed SIF signals contributed by different crop types with distinct phenology (modulated by management practices) and varying SIF emission capacities, which impedes effective utilization of existing SIF records for large-scale agricultural applications. This study makes the first effort to overcome this challenge by developing a sub-pixel SIF extraction framework for corn and soybean in the US Corn Belt as a case study. Here we developed a machine learning (ML) based sub-pixel SIF extraction framework using Orbiting Carbon Observatory 2 (OCO-2), whose high-resolution SIF acquired along orbits at nadir enables the identification of relatively pure pixels dominated by single corn or soybean crops, facilitating validation of the developed framework. To achieve this, we first generated artificially mixed SIF pixels from pure pixels randomly weighted by fractional area coverage. We then employed a standard feed forward artificial neural network (ANN) to estimate sub-pixel SIF for corn and soybean respectively, using the following predictors: total mixed SIF, spectral reflectance of corn/soybean (from Moderate Resolution Imaging Spectroradiometer MODIS), and the fractional area coverage of corn/soybean (derived from CropScape-Cropland Data Layer). Our results demonstrated that the estimated sub-pixel SIF could successfully reproduce the original pure SIF values constituting the mixed pixel, with a normalized root mean squared error (NRMSE) of <10% during the peak growing season. We further demonstrated that this ANN-based framework substantially outperforms the parsimonious linear extraction methods. This developed sub-pixel SIF extraction framework was then applied to generate regional-scale SIF maps for corn and soybean at 0.05° in the US Midwest. Although tested for corn and soybean only, the developed framework has the potential to resolve sub-pixel SIF of more endmembers from coarse SIF observations.

太阳诱导的叶绿素荧光（SIF）是与光合作用直接和功能相关的信号，因此对大规模农业监测具有广阔的前景。但是，现有卫星SIF观测值的粗略空间分辨率通常包括由不同作物类型贡献的混合SIF信号，这些作物具有不同的物候（受管理实践调节），并且SIF的发射能力各不相同，这阻碍了将现有SIF记录有效地用于大规模农业应用程序。这项研究通过为美国玉米带开发用于玉米和大豆的亚像素SIF提取框架进行了首次尝试来克服这一挑战。在这里，我们使用轨道碳观测站2（OCO-2）开发了基于机器学习（ML）的亚像素SIF提取框架，其沿天底轨道获取的高分辨率SIF能够识别以单一玉米或大豆作物为主的相对纯净的像素，从而有助于验证已开发框架的有效性。为了实现这一目标，我们首先从纯像素（由小数区域覆盖率随机加权）生成了人工混合的SIF像素。然后，我们使用标准前馈人工神经网络（ANN）分别使用以下预测因子来估计玉米和大豆的亚像素SIF：总混合SIF，玉米/大豆的光谱反射率（来自中等分辨率成像光谱仪MODIS）和玉米/大豆的部分面积覆盖率（来自CropScape-Cropland数据层）。我们的结果表明，估算的子像素SIF可以成功再现构成混合像素的原始纯SIF值，在高峰生长期，均方根均方根误差（NRMSE）小于10％。我们进一步证明了这种基于ANN的框架大大胜过了简约的线性提取方法。然后，将这种发达的亚像素SIF提取框架应用于美国中西部地区0.05°处玉米和大豆的区域尺度SIF图。尽管仅针对玉米和大豆进行了测试，但开发的框架具有从粗略SIF观测结果中解析更多末端成员的亚像素SIF的潜力。然后，将这种发达的亚像素SIF提取框架应用于美国中西部地区0.05°处玉米和大豆的区域尺度SIF图。尽管仅针对玉米和大豆进行了测试，但开发的框架具有从粗略SIF观测结果中解析更多末端成员的亚像素SIF的潜力。然后，将这种发达的亚像素SIF提取框架应用于美国中西部地区0.05°处玉米和大豆的区域尺度SIF图。尽管仅针对玉米和大豆进行了测试，但开发的框架具有从粗略SIF观测结果中解析更多末端成员的亚像素SIF的潜力。