Machine Learning-Based Swath Bias Correction for OCO-3 Snapshot Area Mapping Mode Observations: 2019 - 2025

We trained a random forest classifier on 1,723 manually labeled OCO-3 SAMs spanning August 2019 to February 2025 to distinguish scenes requiring bias correction from those with legitimate spatial gradients in XCO2. The model uses five key diagnostic features identified through systematic feature selection: inter-swath XCO2 discontinuity, scene homogeneity, aerosol optical depths, and spectral alignment corrections. A median-alignment algorithm then removes detected biases by adjusting swath-level offsets while conserving total column CO2. This data set is the result of this approach further detailed in a forth coming publication. All corrected NetCDF files retain a small subset of the original OCO-3 Level 2 Lite variables (sounding_id, latitude, longitude, time, Sounding/operation_mode, Sounding/orbit, Sounding/target_id, xco2, xco2_quality_flag) and metadata, with the addition of two new fields: xco2_swath_bc containing the bias-corrected XCO2 retrievals and swath_bias_corrected providing a binary flag indicating whether swath bias correction was applied (0 = no change, 1 = correction applied).我们基于2019年8月至2025年2月间的1723条人工标注的轨道碳观测卫星3号（OCO-3）SAM数据，训练了随机森林分类器，以区分需要进行偏差校正的场景与二氧化碳柱平均混合比（XCO2）存在合理空间梯度的场景。该模型通过系统性特征选择确定了五项关键诊断特征，分别为条带间XCO2不连续性、场景同质性、气溶胶光学厚度以及光谱对准校正。随后，采用中值对准算法，通过调整条带级偏移量以移除已检测到的偏差，同时保持总柱二氧化碳总量不变。本数据集为该方法的应用成果，相关细节将在即将发表的学术论文中进一步详述。所有经过校正的网络通用数据格式（NetCDF）文件均保留了原始OCO-3二级Lite数据产品中的少量变量（sounding_id、latitude、longitude、time、Sounding/operation_mode、Sounding/orbit、Sounding/target_id、xco2、xco2_quality_flag）与元数据，并新增了两个字段：包含经偏差校正的XCO2反演结果的xco2_swath_bc，以及用于指示是否已施加条带偏差校正的二进制标记字段swath_bias_corrected（0代表未校正，1代表已完成校正）。