Barlow Twins representations and AGB maps examples for Pará state, Brazil

Barlow Twins representations for S2 tile 22MDU Barlow Twins were originally developed for the processing of natural (RGB) images (Zbontar et al., 2021). The spectral-temporal Barlow Twins (Lisaius et al., 2024), on the other hand, were developed to derive representations from pixel-wise spectral-temporal observations. The approach is fully self-supervised and does not use any inputs other than the multi-spectral time series, together with the corresponding cloud masks. For more details, the reader is referred to Atzberger et al. (2025, A scalable, annual aboveground biomass product for monitoring carbon impacts of ecosystem restoration projects, Remote Sensing of Environment). Years 2013-2016 are based on Landsat, 2017-2024 on Sentinel-2 Naming: representation_YEARModelApplied_S2tile_bt_YearBarlowTwinModelTrained_u8.tif, e.g., representations_2024_22MDU_bt_2019_u8.tif AGB maps for S2 tile 22MDU A simple, 6-layer fully connected feedforward neural network was used to predict AGB from the Barlow Twins-derived representations (NNregressor). To calibrate the NNregressor, the RH-derived footprint-level AGBs from 2019 were used as reference (AGB*RH), and all 32 Barlow Twins representations from the same year were used as predictor variables. Please note: The AGB modeling was done for the following tiles together: 22MCT, 22MCU, 22MDT, 22MDU, 22MGB, 22MGC, 22MHB, 22MHC.For more details, the reader is referred to Atzberger et al. (2025, A scalable, annual aboveground biomass product for monitoring carbon impacts of ecosystem restoration projects, Remote Sensing of Environment). Years 2013-2016 are based on Landsat, 2017-2024 on Sentinel-2 Naming: S2tile_YEARModelApplied_BT_YearModelTrained_GP_YearGEDIPoints.tif e.g. 22MDU_2014_BT_2019_GP_2019.tif适用于S2瓦片22MDU的Barlow Twins表征 Barlow Twins最初被开发用于自然（RGB）图像的处理（Zbontar等人，2021）。而谱时空Barlow Twins（Lisaius等人，2024）则旨在从逐像素谱时空观测数据中提取表征。该方法完全自监督，仅使用多光谱时间序列及其对应的云掩膜作为输入。欲了解更多细节，请参阅Atzberger等人（2025）发表于《Remote Sensing of Environment》的论文《A scalable, annual aboveground biomass product for monitoring carbon impacts of ecosystem restoration projects》。 2013-2016年的数据基于Landsat卫星，2017-2024年的数据基于Sentinel-2卫星 命名规则：representation_YEARModelApplied_S2tile_bt_YearBarlowTwinModelTrained_u8.tif，示例：representations_2024_22MDU_bt_2019_u8.tif 适用于S2瓦片22MDU的地上生物量（Aboveground Biomass, AGB）地图 我们采用一个简单的6层全连接前馈神经网络（NNregressor），从Barlow Twins提取的表征中预测地上生物量（AGB）。为校准该神经网络回归器，我们以2019年基于RH得到的足迹级地上生物量作为参考基准（AGB*RH），并以同年生成的全部32个Barlow Twins表征作为预测变量。请注意：本次地上生物量建模共联合处理以下瓦片：22MCT、22MCU、22MDT、22MDU、22MGB、22MGC、22MHB、22MHC。欲了解更多细节，请参阅Atzberger等人（2025）发表于《Remote Sensing of Environment》的论文《A scalable, annual aboveground biomass product for monitoring carbon impacts of ecosystem restoration projects》。 2013-2016年的数据基于Landsat卫星，2017-2024年的数据基于Sentinel-2卫星 命名规则：S2tile_YEARModelApplied_BT_YearModelTrained_GP_YearGEDIPoints.tif，示例：22MDU_2014_BT_2019_GP_2019.tif