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Retrieval of Leaf area index and stress conditions for Sundarban mangroves using Sentinel-2 data

机译:使用哨兵-2数据检索Sundarban Comgroves的叶面积指数和压力条件

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摘要

The potential of Sentinel-2 (S2) data in mapping Leaf area index (LAI) of mangroves having heterogeneous species composition, variable canopy density, and complex backgrounds was studied. Out of the three available near-infrared bands in S2, band-8 of 10 m spatial resolution was found to be the most suitable one for deriving the Normalized Difference Vegetation Index (NDVI) for mangroves. The LAI-NDVI relation did not accord apparently with the earlier reports and the underlying complex background effect was validated with Airborne Visible Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) hyperspectral data. It simulated spectral and spatial conditions of S2 by linear mixing of canopy and background that confirmed the effect of background contributions to the canopy reflectance decorrelating the NDVI from LAI. The compensation for diverse backgrounds was accomplished with optimum-scaled NDVI (scNDVI(m)) obtained from the mean of scaled NDVIs derived with different backgrounds in the mangroves. LAI was well correlated with composite NDVI (NDVIcom), derived empirically from the most appropriate NDVI (NDVIS2) and scNDVI(m) where ground observation controlled the threshold arbitration in extracting the range of scNDVI(m). It was shown that an improved LAI estimate with a coefficient of determination (R-2) of 0.69 and root-mean-square error (RMSE) of 0.02 could be obtained with NDVIcom. This method has the advantage of compensating the contaminations due to background reflectance. While the relation between LAI and NDVIcom was found to be consistent, the application of the same methodology in similar mangroves should be site-specific with ample ground observation. The fusion of NDVI and scNDVI obtained from S2 yields better LAI retrieval for mixed mangroves, such as that of Sundarban.
机译:研究了具有异均匀物种组成,可变冠层密度和复杂背景的制图叶片面积指数(LAI)中的哨子-2(S2)数据的潜力。在S2中的三个可用的近红外条带中,发现10米空间分辨率的带-8是最适合推导出用于红树林的归一化差异植被指数(NDVI)的空间分辨率。 Lai-NDVI关系显然与早期的报告显然,并且通过空气传播的可见红外成像光谱仪 - 下一代(Aviris-NG)高光谱数据验证了潜在的复杂背景效果。通过冠层和背景的线性混合模拟S2的光谱和空间条件,这证实了背景贡献对来自LAI的NDVI的冠层反射率的影响。多种背景的补偿是用从级别的NDVIS的平均值获得的最佳缩放的NDVI(SCNDVI(M))来完成,从而衍生在红草树中的不同背景。 Lai与复合NDVI(NDVICOM)相关,凭经验从最合适的NDVI(NDVIS2)和SCNDVI(M)源于接地观察,在提取SCNDVI(M)范围内的阈值仲裁。结果表明,通过NDVICOM可以获得具有0.69的判定系数(R-2)的改进的LAI估计(R-2)和0.02的根平方误差(RMSE)。该方法具有由于背景反射而补偿污染的优点。虽然发现LAI和NDVICOM之间的关系是一致的,但在类似的红树林中的应用应该是特异性地面观察。从S2获得的NDVI和SCNDVI的融合产生更好的LAI检索混合红树林,例如Sundarban。

著录项

  • 来源
    《International journal of remote sensing》 |2020年第4期|1019-1039|共21页
  • 作者单位

    Presidency Univ Dept Phys 86-1 Coll St Kolkata 700073 India;

    Presidency Univ Dept Phys 86-1 Coll St Kolkata 700073 India;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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