A comparison of empirical and neural network approaches for estimating corn and soybean leaf area index from Landsat ETM+ imagery ☆
A comparison of empirical and neural network approaches for estimating corn and soybean leaf area index from Landsat ETM+ imagery ☆
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DOI：
10.1016/j.rse.2004.06.003
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发表时间：
2004-09
影响因子：
13.5
通讯作者：
C. Walthall;W. Dulaney;Martha C. Anderson;J. Norman;H. Fang;S. Liang
中科院分区：
文献类型：
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作者：
C. Walthall;W. Dulaney;Martha C. Anderson;J. Norman;H. Fang;S. Liang
植物叶密度以叶面积指数（LAI）表示，用于许多生态、气象和农艺模型，并作为精确农业中量化作物空间变异性的一种手段。利用光谱植被指数（SVI）从光学遥感数据反演叶面积指数通常需要特定地点的校准值从表面或使用场景内的图像信息，而无需表面校准，以反演辐射传输模型。评价叶面积指数反演方法进行了使用（1）经验方法，采用归一化差异植被指数（NDVI）和一个新的SVI，使用绿色波长反射率，（2）缩放的NDVI方法，使用没有校准测量，和（3）的混合方法，使用神经网络（NN）和辐射传输模型没有特定地点的校准测量。虽然研究表明，在各种条件下，归一化差异植被指数并不是叶面积指数反演的最佳方法，但它继续用于遥感应用，并在分析中寻求开发基于归一化差异植被指数的改进参数反演算法，这表明它作为“基准”或标准的价值，可以与其他方法进行比较。Landsat-7 ETM+数据7月1日和7月8日从土壤水分实验2002年（SMEX 02）现场活动在核桃溪流域以南的艾姆斯，IA，用于分析。从流域内的一个站点收集的太阳光度计数据被用来大气校正的图像表面反射率。玉米和大豆的叶面积指数验证测量收集接近Landsat-7立交桥的日期。在每个日期内的经验SVI方法和缩放SVI方法获得了可比的结果。混合方法，虽然有前途的，没有占尽可能多的变异性SVI方法。7月8日的大气光学厚度较高，导致表面反射率误差，据信这导致了这一日期的整体表现较差。使用SVI采用绿色波长，改进的方法用于定义图像的最小和最大集群的缩放的NDVI方法，并进一步发展的土壤反射率指数使用的混合NN方法是必要的。更重要的是，结果表明，合理的叶面积指数估计是可能的，使用光学遥感方法没有在现场，特定地点的校准测量。
Plant foliage density expressed as leaf area index (LAI) is used in many ecological, meteorological, and agronomic models, and as a means of quantifying crop spatial variability for precision farming. LAI retrieval using spectral vegetation indices (SVI) from optical remotely sensed data usually requires site-specific calibration values from the surface or the use of within-scene image information without surface calibrations to invert radiative transfer models. An evaluation of LAI retrieval methods was conducted using (1) empirical methods employing the normalized difference vegetation index (NDVI) and a new SVI that uses green wavelength reflectance, (2) a scaled NDVI approach that uses no calibration measurements, and (3) a hybrid approach that uses a neural network (NN) and a radiative transfer model without site-specific calibration measurements. While research has shown that under a variety of conditions NDVI is not optimal for LAI retrieval, its continued use for remote sensing applications and in analysis seeking to develop improved parameter retrieval algorithms based on NDVI suggests its value as a “benchmark” or standard against which other methods can be compared. Landsat-7 ETM+ data for July 1 and July 8 from the Soil Moisture EXperiment 2002 (SMEX02) field campaign in the Walnut Creek watershed south of Ames, IA, were used for the analysis. Sun photometer data collected from a site within the watershed were used to atmospherically correct the imagery to surface reflectance. LAI validation measurements of corn and soybeans were collected close to the dates of the Landsat-7 overpasses. Comparable results were obtained with the empirical SVI methods and the scaled SVI method within each date. The hybrid method, although promising, did not account for as much of the variability as the SVI methods. Higher atmospheric optical depths for July 8 leading to surface reflectance errors are believed to have resulted in overall poorer performance for this date. Use of SVIs employing green wavelengths, improved method for the definition of image minimum and maximum clusters used by the scaled NDVI method, and further development of a soil reflectance index used by the hybrid NN approach are warranted. More importantly, the results demonstrate that reasonable LAI estimates are possible using optical remote sensing methods without in situ, site-specific calibration measurements.