Kang Yanghui, Özdoğan Mutlu, Zipper Samuel C, Román Miguel O, Walker Jeff, Hong Suk Young, Marshall Michael, Magliulo Vincenzo, Moreno José, Alonso Luis, Miyata Akira, Kimball Bruce, Loheide Steven P
Nelson Institute Center for Sustainability and the Global Environment; University of Wisconsin-Madison, 1710 University Avenue, Madison, WI 53726, USA.
Department of Geography, University of Wisconsin-Madison, Madison, WI post code, USA.
Remote Sens (Basel). 2016;8(7):597. doi: 10.3390/rs8070597. Epub 2016 Jul 15.
Leaf Area Index (LAI) is a key variable that bridges remote sensing observations to the quantification of agroecosystem processes. In this study, we assessed the universality of the relationships between crop LAI and remotely sensed Vegetation Indices (VIs). We first compiled a global dataset of 1459 in situ quality-controlled crop LAI measurements and collected Landsat satellite images to derive five different VIs including Simple Ratio (SR), Normalized Difference Vegetation Index (NDVI), two versions of the Enhanced Vegetation Index (EVI and EVI2), and Green Chlorophyll Index (CI). Based on this dataset, we developed global LAI-VI relationships for each crop type and VI using symbolic regression and Theil-Sen (TS) robust estimator. Results suggest that the global LAI-VI relationships are statistically significant, crop-specific, and mostly non-linear. These relationships explain more than half of the total variance in ground LAI observations ( >0.5), and provide LAI estimates with RMSE below 1.2 m/m. Among the five VIs, EVI/EVI2 are the most effective, and the crop-specific LAI-EVI and LAI-EVI2 relationships constructed by TS, are robust when tested by three independent validation datasets of varied spatial scales. While the heterogeneity of agricultural landscapes leads to a diverse set of local LAI-VI relationships, the relationships provided here represent global universality on an average basis, allowing the generation of large-scale spatial-explicit LAI maps. This study contributes to the operationalization of large-area crop modeling and, by extension, has relevance to both fundamental and applied agroecosystem research.
叶面积指数（LAI）是一个关键变量，它将遥感观测与农业生态系统过程的量化联系起来。在本研究中，我们评估了作物叶面积指数与遥感植被指数（VIs）之间关系的普遍性。我们首先编制了一个包含1459个经过现场质量控制的作物叶面积指数测量数据的全球数据集，并收集了陆地卫星图像以得出五个不同的植被指数，包括简单比值（SR）、归一化植被指数（NDVI）、两个版本的增强植被指数（EVI和EVI2）以及绿度叶绿素指数（CI）。基于此数据集，我们使用符号回归和泰尔-森（TS）稳健估计器为每种作物类型和植被指数建立了全球叶面积指数-植被指数关系。结果表明，全球叶面积指数-植被指数关系具有统计学意义、作物特异性且大多为非线性。这些关系解释了地面叶面积指数观测中总方差的一半以上（>0.5），并提供了均方根误差低于1.2 m²/m²的叶面积指数估计值。在这五个植被指数中，EVI/EVI2最为有效，并且由TS构建的作物特异性叶面积指数-EVI和叶面积指数-EVI2关系，在通过三个不同空间尺度的独立验证数据集进行测试时表现稳健。虽然农业景观的异质性导致了一系列不同的局部叶面积指数-植被指数关系，但这里提供的关系平均而言代表了全球普遍性，从而能够生成大面积的空间明确叶面积指数地图。本研究有助于大面积作物建模的实际应用，进而与基础和应用农业生态系统研究都相关。
