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首页> 外文期刊>Remote Sensing of Environment: An Interdisciplinary Journal >Assessing the effects of site heterogeneity and soil properties when unmixing photosynthetic vegetation, non-photosynthetic vegetation and bare soil fractions from Landsat and MODIS data
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Assessing the effects of site heterogeneity and soil properties when unmixing photosynthetic vegetation, non-photosynthetic vegetation and bare soil fractions from Landsat and MODIS data

机译：从Landsat和MODIS数据评估光合植被，非光合植被和裸露土壤组分混合时评估场地异质性和土壤特性的影响

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Vegetation fractional cover is a key metric for monitoring land management, both in pastoral and agricultural settings. Monitoring vegetation fractional cover continuously across large areas needs good remote sensing techniques underpinned by high quality field data to calibrate and validate algorithms. Here Landsat and MODIS surface reflectance data together with 1171 field observations across Australia were used to estimate vegetation fractional cover using a linear unmixing technique. The aim was to estimate the fractions of photosynthetic and non-photosynthetic vegetation (PV and NPV, respectively) and the remaining fraction of bare soil (BS). Landsat surface reflectance was averaged over a 3 x 3 pixel window representing the field area measured and also "degraded" using a 17 x 17 pixel window (similar to 0.26 km(2)) to approximate the coarser MODIS sensor's response. These two Landsat surface reflectances were used to calculate a site heterogeneity metric. Data from two MODIS-derived surface reflectance products with a pixel size of similar to 0.25 km(2) were used: (i) the 16-day nadir BRDF-Adjusted Reflectance product (MCD43A4); and (ii) the MODIS 8-day surface reflectance (MODO9A1). Log transforms and band interaction terms were added to account for non-linearities in the spectral mixing. A cross-validation step was also included to select the optimal number of singular values to avoid over-fitting. For each surface reflectance source we investigated the residuals' correlation with site heterogeneity, soil colour and soil moisture. The best model was obtained when Landsat data for a small region around each observation were used. Root mean square error (RMSE) values of 0.112, 0.162 and 0.130 for PV, NPV and BS, respectively, were obtained. As expected, degrading the Landsat data to similar to 0.26 km2 around each site decreased model goodness of fit to RMSE of 0.119, 0.174 and 0.150, respectively, for the three fractions. Using MODIS surface reflectance data gave worse results than the "degraded" Landsat surface reflectance, with MODO9A1 performing slightly better than MCD43A4. No strong evidence of soil colour or soil moisture influence on model performance was found, suggesting that the unmixing models are insensitive to soil colour and/or that the soil moisture in the top few millimetres of soil, which influence surface reflectance in optical sensors, is decoupled from the soil moisture in the top layer (Le., a few centimetres) as measured by passive microwave sensors or estimated by models. This study outlines an operational combined Landsat/MODIS product to benefit users with varying requirements of spatial resolution and temporal frequency and latency that could be applied to other regions in the world. Crown Copyright (C) 2015 Published by Elsevier Inc. All rights reserved.
机译：植被覆盖率是监测牧场和农业环境中土地管理的一项关键指标。连续监测大面积植被覆盖度需要高质量的野外数据作为基础的良好遥感技术，以校准和验证算法。在这里，Landsat和MODIS的表面反射率数据以及在澳大利亚的1171个现场观测值被用于使用线性分解技术估算植被覆盖度。目的是估计光合和非光合植被的比例（分别为PV和NPV）和剩余裸土的比例（BS）。 Landsat表面反射率是在表示所测量场区域的3 x 3像素窗口上平均的，并使用17 x 17像素窗口（类似于0.26 km（2））进行“退化”，以近似粗糙的MODIS传感器的响应。这两个Landsat表面反射率用于计算站点异质性度量。使用了两种来自MODIS的表面反射产品的数据，这些产品的像素大小近似于0.25 km（2）：（i）16天的最低点BRDF调整反射产品（MCD43A4）; （ii）MODIS 8天表面反射率（MODO9A1）。添加了对数变换和能带相互作用项，以解决频谱混合中的非线性问题。还包括一个交叉验证步骤，以选择最佳数量的奇异值以避免过度拟合。对于每个表面反射源，我们研究了残差与场地异质性，土壤颜色和土壤水分的相关性。当使用每个观测值周围小区域的Landsat数据时，可获得最佳模型。 PV，NPV和BS的均方根误差（RMSE）值分别为0.112、0.162和0.130。不出所料，将每个站点周围的Landsat数据降级为类似于0.26 km2的三个部分，分别使模型拟合度的RMSE分别为0.119、0.174和0.150。使用MODIS表面反射率数据给出的结果要比“降级” Landsat表面反射率差，而MODO9A1的性能略好于MCD43A4。没有发现土壤颜色或土壤水分对模型性能有强烈影响的有力证据，表明解混模型对土壤颜色不敏感和/或影响光学传感器中表面反射率的前几毫米土壤中的土壤水分为通过无源微波传感器测量或通过模型估算，将其与顶层（例如几厘米）的土壤水分分离。这项研究概述了可操作的Landsat / MODIS组合产品，以使对空间分辨率，时间频率和延迟有不同要求的用户受益，这些要求可能会应用于世界其他地区。 Crown版权（C）2015，由Elsevier Inc.出版。保留所有权利。

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