Abstract This paper introduces the CASINO (CAnopy backscatter estimation, Subsampling, and Inhibited Nonlinear Optimisation) algorithm for above-ground biomass (AGB) estimation in tropical forests using P-band (435 MHz) synthetic aperture radar (SAR) data. The algorithm has been implemented in a prototype processor for European Space Agency's (ESA's) 7th Earth Explorer Mission BIOMASS, scheduled for launch in late 2022. CASINO employs an interferometric ground cancellation technique to estimate canopy backscatter (CB) intensity. A power law model (PLM) is then used to model the dependence of CB on AGB for a large number of systematically distributed SAR data samples and a small number of calibration areas with a known AGB. The PLM parameters and AGB for the samples are estimated simultaneously within pre-defined intervals using nonlinear minimisation of a cost function. The performance of CASINO is assessed over six tropical forest sites on two continents: two in French Guiana, South America and four in Gabon, Africa, using SAR data acquired during airborne ESA campaigns and processed to simulate BIOMASS acquisitions. Multiple tests with only two randomly selected calibration areas with AGB > 100 t/ha are conducted to assess AGB estimation performance given limited reference data. At 2.25 ha scale and using a single flight heading, the root-mean-square difference (RMSD) is ≤ 27% for at least 50% of all tests in each test site and using as reference AGB maps derived from airborne laser scanning data. An improvement is observed when two flight headings are used in combination. The most consistent AGB estimation (lowest RMSD variation across different calibration sets) is observed for test sites with a large AGB interval and average AGB around 200–250 t/ha. The most challenging conditions are in areas with AGB

中文翻译：

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使用地面抵消 P 波段 SAR 和有限参考数据绘制热带森林地上生物量图

摘要 本文介绍了使用 P 波段 (435 MHz) 合成孔径雷达 (SAR) 数据估计热带森林地上生物量 (AGB) 的 CASINO（CAnopy 反向散射估计、二次采样和抑制非线性优化）算法。该算法已在欧洲航天局 (ESA) 计划于 2022 年底发射的第 7 次地球探索者任务 BIOMASS 的原型处理器中实施。 CASINO 采用干涉地面抵消技术来估计冠层反向散射 (CB) 强度。然后使用幂律模型 (PLM) 对大量系统分布的 SAR 数据样本和少量具有已知 AGB 的校准区域的 CB 对 AGB 的依赖性进行建模。样本的 PLM 参数和 AGB 使用成本函数的非线性最小化在预定义的间隔内同时估计。CASINO 的性能在两大洲的六个热带森林地点进行评估：两个位于南美洲法属圭亚那，四个位于非洲加蓬，使用在空中 ESA 活动期间获取并处理以模拟 BIOMASS 获取的 SAR 数据。在给定参考数据的情况下，仅对两个随机选择的 AGB > 100 t/ha 的校准区域进行了多次测试，以评估 AGB 估计性能。在 2.25 公顷的尺度和使用单个飞行航向时，每个测试地点至少 50% 的所有测试的均方根差 (RMSD) ≤ 27%，并使用从机载激光扫描数据得出的参考 AGB 地图。当两个飞行航向组合使用时，可以观察到改进。对于 AGB 间隔较大且平均 AGB 约为 200–250 t/ha 的测试地点，观察到最一致的 AGB 估计（不同校准组的最低 RMSD 变化）。最具挑战性的条件是在有 AGB 的地区