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摘要:
红树林湿地是生产力最高的生态系统之一，是重要的蓝色碳汇，快速准确地估算红树林的地上生物量（AGB）具有重要意义。结合野外样地实测AGB数据，提取Sentinel-1/2卫星的后向散射系数、波段反射率、植被指数和纹理特征等数据，利用多元逐步回归和偏最小二乘回归（PLSR）的方法对比不同变量组合建模的效果，并基于最优模型对珍珠湾区域红树林AGB进行估算。结果表明：①利用连续PLSR算法筛选的多类型特征组合模型效果最好，决定系数R2为0.88，均方根误差RMSE为16.07 t/hm2，其中，Sentinel-2所有波段的第2主成分在3×3窗口的相关纹理变量（Corp2_3）对模型的贡献率最大；②珍珠湾片区红树林AGB总量和均值分别约为45 956.41 t和48.06 t/hm2，在AGB预测值空间分布上大体呈现中部、东部高，西部稍低的特点，高值区主要分布在养殖虾塘、蚝排等人类活动较为频繁的场所附近，低值区主要分布于半滩涂区域或幼苗较多的位置；③联合合成孔径雷达（SAR）和光学数据能有效提高AGB的反演精度，仅使用Sentinel-1后向散射系数及衍生因子不适用于反演生物量，单纯采用纹理变量的建模效果优于波段反射率和植被指数。
Abstract:Mangrove wetlands are one of the most productive ecosystems and important blue carbon sinks. Accurate estimation of aboveground biomass (AGB) of mangroves holds great significance. By integrating field-measured AGB data with Sentinel-1/2 satellite backscatter coefficient, reflectance, vegetation index, and texture feature data, we employed a stepwise multiple regression and partial least squares regression (PLSR) approach to compare the modeling results of different variable combinations and estimated the AGB of mangroves in the Zhenzhu Bay area, Guangxi, SW China, based on the optimal model. Results indicate that: ① The multi-type feature combination model selected by the continuous PLSR algorithm showed the best performance (the coefficient of determination R2=0.88; the root mean square error RMSE=16.07 t/hm2). Among them, the Corp2_3 (the texture variable correlation of the second principal component PC2 of Sentinel-2A in a 3×3 window) contributed the greatest to the modeling; ② The total and average AGB of mangroves in the Zhenzhu Bay area were approximately 45 956.41 t and 48.06 t/hm2, respectively. In the spatial distribution of the predicted AGB value, it shows an overall higher level in the central and eastern parts of the area and a slightly lower level in the western part. The high-value areas covered mainly those near human activities such as shrimp farming ponds and oyster rafts, while low-value areas mainly in semi-tidal wetlands or areas with more seedlings; ③ The combination of synthetic aperture radar (SAR) and optical data could effectively enhance the accuracy of AGB inversion. Using just Sentinel-1 backscatter coefficient and derived factors is not feasible to invert biomass, and modeling with texture variables could produce better results than those with reflectance and vegetation index.
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表 2 选用的植被指数和计算公式
Table 2. Vegetation index and the equations for the calculation
植被指数 公式 比值植被指数（RVI） RVI=ρnir/ρred 差值植被指数（DVI） DVI=ρnir–ρred 权重植被指数（WDVI） WDVI=ρnir–0.5ρred 红外植被指数（IPVI） IPVI=ρnir/（ρnir+ρred） 垂直植被指数（PVI） PVI=sin（45°）ρnir–cos（45°）ρred 归一化植被指数（NDVI） NDVI=（ρnir–ρred）/（ρnir+ρred） 绿波归一化植被指数（GNDVI） GNDVI=（ρnir–ρgreen）/（ρnir+ρgreen） 土壤调节植被指数（SAVI） SAVI=1.5（ρnir–ρred）/（ρnir+ρred+0.5） 转化土壤调节植被指数（TSAVI） TSAVI=0.5（ρnir–0.5ρred–0.5）/（0.5ρnir+ρred–0.15） 二次修正型土壤调节植被指数（MSAVI2） MSAVI2=0.5[2（ρnir+1）– 
]大气阻抗植被指数（ARVI） ARVI=ρnir–（2ρred–ρblue）/ρnir+（2ρred+ρblue） 陆地叶绿素指数（MTCI） MTCI=（ρre6–ρre5）/（ρre5–ρnir） 哨兵2号红边位置指数（S2REP） S2REP=705+35（（ρnir–ρre7）/2–ρre5）（ρre6–ρre5） 注：ρnir、ρred、ρgreen、ρblue、ρre6、ρre5、ρre7分别为近红外波段、红波段、绿波段、蓝波段、植被红边波段6、植被红边波段5和植被红边波段7的反射率。 
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表 3 红树林AGB建模方案
Table 3. The AGB modeling scheme for mangrove
模型 建模方案 特征数量 特征类型 ① 多元逐步回归+波段反射率 12 Sentinel-2A的B1—B12波段 ② 多元逐步回归+后向散射系数及衍生变量 4 VH、VV、VV+VH、VV/VH ③ 多元逐步回归+植被指数 13 表2中的各类植被指数 ④ 多元逐步回归+纹理特征 96 Sentinel-1的48个纹理变量，Sentinel-2A的48个纹理变量 ⑤ 多元逐步回归+所有变量 125 所有变量 ⑥ PLSR+所有变量 125 所有变量
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表 4 红树林样地AGB分布情况
Table 4. AGB distribution in the sample plots
AGB级别/（t/hm2） 样地数 AGB/（t/hm2） 占比/% ≤40 13 11.45、11.52、11.88、13.63、16.57、19.97、23.85、25.02、26.41、26.54、27.99、36.65、39.97 54.17 40～80 9 42.14、43.45、45.64、48.12、56.42、63.84、65.86、72.10、76.69 37.50 ≥80 2 109.57、157.02 8.33
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表 5 各方案模型构建情况
Table 5. Modeling for different approaches
模型 建模方案 筛选特征 R2 R2adj ① 多元逐步回归+波段反射率 B2、B3 0.35 0.29 ② 多元逐步回归+后向散射系数及衍生变量 无显著特征 ③ 多元逐步回归+植被指数 S2REP 0.28 0.25 ④ 多元逐步回归+纹理特征 MP2_7, Corp2_3, CorVV5 0.58 0.52 ⑤ 多元逐步回归+所有变量 CorVV3, Corp2_3, S2REP, B2 0.72 0.66 ⑥ PLSR+所有变量 Corp2_3, SMVH3, B2, CorVV3, S2REP, SMP1_7 0.88 0.84 注：B2、B3为Sentinel-2A影像第2、3波段的反射率；S2REP为哨兵2号数据红边位置指数；MP2_7为Sentinel-2A第2主成分PC2在7×7窗口的纹理变量均值；Corp2_3为Sentinel-2A第2主成分PC2在3×3窗口的纹理变量相关性；CorVV5为VV极化方式在5×5窗口的纹理变量相关性；CorVV3为VV极化方式在3×3窗口的纹理变量相关性；SMVH3为VH极化方式在3×3窗口的纹理特征变量二阶矩；SMP1_7为Sentinel-2A第1主成分PC1在7×7窗口的纹理变量二阶矩。
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