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Towards universal broad leaf chlorophyll indices using PROSPECT simulated database and hyperspectral reflectance measurements

机译:使用PROSPECT模拟数据库和高光谱反射率测量方法获得通用的阔叶叶绿素指数

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Fifty-three leaves were randomly sampled on different deciduous tree species, representing a wide range of chlorophyll contents, free ages, and leaf structural features. Their reflectance was measured between 400 and 800 nm with a 1-nm step, and their chlorophyll content determined by extraction. A larger simulated database (11,583 spectra) was built using the PROSPECT model, in order to test, calibrate, and obtain universal indices, i.e., indices applicable to a wide range of species and leaf structure. To our knowledge, almost all leaf chlorophyll indices published in the literature since 1973 have been tested on both databases. Fourteen canonical types of indices (published ones and new ones) were identified, and their wavelengths calibrated on the simulated database as well as on the experimental database to determine the best wavelengths and, hence, the best performances in chlorophyll estimation for each index types. These indices go from simple reflectance ratios to more sophisticated indices using reflectance first derivatives (using the Savitzky and Golay method). We also tested other nondestructive methods to obtain total chlorophyll concentration: SPAD (Minolta Camera, Osaka, Japan) and neural networks. The validity of the actual PROSPECT model is challenged by our results: Important discordances are found when the indices are calculated with PROSPECT compared to experimental data, especially for some indices and wavelengths. The discordance is even greater when the indices are determined with PROSPECT and applied on the experimental database. A new calibration of PROSPECT is therefore necessary for any study aiming at using simulated spectra to determine or to calibrate indices. The "peak jump" and the multiple-peak feature observed on the first derivative of the reflectances (e.g., in the Red-Edge Inflection Point [REIP] index) have been investigated. It was shown that chlorophyll absorption alone can explain this feature. The peak jump disqualifies the REIP to be a valuable chlorophyll index. A simple modified difference ratio gave the best results among all published indices (cross-validated RMSE = 2.1 μg/cm{sup}2 on the experimental database). After calibration on the experimental database, modified Simple Ratio (mSR) and modified Normalized Difference. (mND) indices gave the best performances (RMSECV= 1.8 (μg/cm{sup}2 on the experimental database). The new Double Difference (DD) index, although not the best on the experimental database (RMSECV = 2.9 μg/cm{sup}2), has the best results on the larger simulated database (RMSE - 3.7 μg/cm{sup}2) and is expected to give good results on larger experimental databases. The best reflectance-based indices give better performances than the current commercial nondestructive device SPAD (RMSECV = 4.5 μg/cm{sup}2). In this leaf-level study, the best indices are very Hear from each other, so that complex methods are useless: REIP-like, neural networks, and derivative-based indices are not necessary and give worst results than simpler properly chosen indices. These conclusions will certainly be different for a canopy-level study, where the derivative-based indices may perform significantly better than the other ones.
机译:从不同的落叶树种中随机抽取53片叶子,代表了广泛的叶绿素含量,自由年龄和叶片结构特征。在400至800 nm之间以1 nm的步长测量其反射率,并通过萃取确定其叶绿素含量。使用PROSPECT模型建立了更大的模拟数据库(11,583个光谱),以便测试,校准和获得通用指标,即适用于广泛的物种和叶片结构的指标。据我们所知,自1973年以来发表在文献中的几乎所有叶绿素指数都在这两个数据库上进行了测试。确定了14种规范类型的索引(已发布的和新的),并在模拟数据库和实验数据库上对它们的波长进行了校准,以确定每种索引类型的最佳波长,从而确定了叶绿素估计的最佳性能。这些指标从简单的反射率到使用反射率一阶导数(使用Savitzky和Golay方法)的更复杂的指标。我们还测试了其他非破坏性方法以获得总叶绿素浓度:SPAD(日本大阪的美能达相机)和神经网络。我们的结果挑战了实际PROSPECT模型的有效性:与实验数据相比,使用PROSPECT计算指标时发现了重要的矛盾,特别是对于某些指标和波长。当使用PROSPECT确定指标并将其应用于实验数据库时,差异甚至更大。因此,对于任何旨在使用模拟光谱来确定或校准指数的研究,都必须对PROSPECT进行新的校准。已经研究了在反射率的一阶导数(例如,在红边拐点[REIP]指数中)观察到的“峰值跃迁”和多峰特征。结果表明,仅叶绿素吸收可以解释这一特征。峰跳使REIP失去了成为有价值的叶绿素指数的资格。一个简单的修改后的差异比在所有已发布的指标中提供了最好的结果(在实验数据库中经过交叉验证的RMSE = 2.1μg/ cm {sup} 2)。在实验数据库上校准后,修改了简单比率(mSR)和修改了归一化差。 (mND)指数提供了最佳性能(RMSECV = 1.8(μg/ cm {sup} 2在实验数据库中)。新的Double Difference(DD)指数,尽管在实验数据库中并非最佳(RMSECV = 2.9μg/ cm {sup} 2),在较大的模拟数据库(RMSE-3.7μg/ cm {sup} 2)上具有最佳结果,并有望在较大的实验数据库上产生良好的结果。目前的商用无损设备SPAD(RMSECV = 4.5μg/ cm {sup} 2)。在这项叶级研究中,最佳指标彼此之间非常相近,因此复杂的方法毫无用处:类似于REIP,神经网络和基于导数的指数不是必须的,并且比选择简单的指数所得出的结果差,对于冠层水平的研究,这些结论肯定会有所不同,因为基于冠层的指数可能会比其他指数好得多。

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