Accurate estimation of forest aboveground biomass (AGB) using remote sensing is a requisite for monitoring, reporting and verification (MRV) system of the United Nations Programme on Reducing Emissions from Deforestation and Forest Degradation. However, attaining high accuracy remains a great challenge in the diverse tropical forests. Among available technologies, l-band Synthetic Aperture Radar (SAR) estimates AGB with reasonably high accuracy in the terrestrial tropical forests. Nevertheless, the accuracy is relatively low in the mangrove forests. In this context, the study was carried out to model and map AGB using backscatter coefficients of Advanced Land Observing Satellite-2 (ALOS-2) Phased Array l-band SAR-2 (PALSAR-2) in part of the restored mangrove forest at Mahakam Delta, Indonesia. PALSAR-2 data was acquired with image scene observation during the peak low tide on 30 July 2018 from Japan Aerospace Exploration Agency. The forest parameters namely tree height and diameter at breast height were measured from 71 field plots in September-October 2018. The parameters were used in mangrove allometry to calculate the field AGB. Finally, HV polarized backscatter coefficients of PALSAR-2 were used to model AGB using linear regression. The model demonstrated a comparatively high performance using three distinct methods viz. independent validation (R² of 0.89 and RMSE of 23.16 tons ha⁻¹), random k-fold cross validation (R² of 0.89 and RMSE of 24.59 tons ha⁻¹) and leave location out cross validation (LLO CV) (R² of 0.88 and RMSE of 24.05 tons ha⁻¹). The high accuracy of the LLO CV indicates no spatial overfitting in the model. Thus, the model based on LLO CV was used to map AGB in the study area. This is the first study that successfully obtains high accuracy in modeling AGB in the mangrove forest. Therefore, it offers a significant contribution to the MRV mechanism for monitoring mangrove forests in the tropics and sub-tropics.

使用遥感准确估算森林地上生物量（AGB）是联合国减少砍伐森林和森林退化方案的监测，报告和核查（MRV）系统的必要条件。然而，在各种热带森林中，实现高精度仍然是一个巨大的挑战。在可用技术中，I波段合成孔径雷达（SAR）估计陆地热带森林中的AGB具有相当高的准确度。然而，在红树林中，准确性相对较低。在这种情况下，该研究使用高级陆地观测卫星2（ALOS-2）相控阵l的反向散射系数对AGB进行建模和制图印度尼西亚Mahakam三角洲已恢复的红树林部分带SAR-2（PALSAR-2）。PALSAR-2数据是在日本退潮期间于2018年7月30日从日本航空航天局拍摄的图像现场观察获得的。从2018年9月至10月的71个田地中测量了森林参数，即树高和胸高处的直径。这些参数用于红树林Allometry中以计算田间AGB。最后，使用PALSAR-2的HV极化后向散射系数通过线性回归对AGB进行建模。该模型使用三种不同的方法展示了相对较高的性能。独立验证（R ²为0.89和RMSE为23.16吨公顷^-1），随机k倍交叉验证（R ²为0.89和RMSE为24.59吨公顷^-1）并保留交叉验证（LLO CV）的位置（R ²为0.88，RMSE为24.05吨ha ^-1）。LLO CV的高精度表明模型中没有空间过度拟合。因此，基于LLO CV的模型用于在研究区域中绘制AGB。这是第一个成功地在红树林中对AGB建模的方法中获得了高精度的研究。因此，它为监测热带和亚热带红树林的MRV机制提供了重要的贡献。