Comparison of the Spatial Characteristics of Four Remotely Sensed Leaf Area Index Products over China: Direct Validation and Relative Uncertainties

Abstract

Leaf area index (LAI) is a key input for many land surface models, ecological models, and yield prediction models. In order to make the model simulation and/or prediction more reliable and applicable, it is crucial to know the characteristics and uncertainties of remotely sensed LAI products before they are input into models. In this study, we conducted a comparison of four global remotely sensed LAI products-Global Land Surface Satellite (GLASS), Global LAI Product of Beijing Normal University (GLOBALBNU), Global LAI Map of Chinese Academy of Sciences (GLOBMAP), and Moderate-resolution Imaging Spectrometer (MODIS) LAI, while the former three products are newly developed by three Chinese research groups on the basis of the MODIS land reflectance product over China between 2001 and 2011. Direct validation by comparing the four products to ground LAI observations both globally and over China demonstrates that GLASS LAI shows the best performance, with R 2 = 0.70 and RMSE = 0.96 globally and R 2 = 0.94 and RMSE = 0.61 over China; MODIS performs worst (R 2 = 0.55, RMSE = 1.23 globally and R 2 = 0.03, RMSE = 2.12 over China), and GLOBALBNU and GLOBMAP performs moderately. Comparison of the four products shows that they are generally consistent with each other, giving the smallest spatial correlation coefficient of 0.7 and the relative standard deviation around the order of 0.3. Compared with MODIS LAI, GLOBALBNU LAI is the most similar, followed by GLASS LAI and GLOBMAP. Large differences mainly occur in southern regions of China. LAI difference analysis indicates that evergreen needleleaf forest (ENF), woody savannas (SAV) biome types and temperate dry hot summer, temperate warm summer dry winter and temperate hot summer no dry season climate types correspond to high standard deviation, while ENF and grassland (GRA) biome types and temperate warm summer dry winter and cold dry winter warm summer climate types are responsible for the large relative standard deviation of the four products. Our results indicate that although the three newly developed products have improved the accuracy of LAI estimates, much work remains to improve the LAI products especially in ENF, SAV, and GRA regions and temperate climate zones. Findings from our study can provide guidance to communities regarding the performance of different LAI products over mainland China.

Semi-Automatic Methodology for Fire Break Maintenance Operations Detection with Sentinel-2 Imagery and Artificial Neural Network

“…The chosen metric was the mean value. To validate it is a representative measure to describe an FB, the relative standard deviation was calculated [28] and 0.185 was obtained. Since it is much smaller than 1, it is a reasonable approximation [29].…”

Generation and Evaluation of LAI and FPAR Products from Himawari-8 Advanced Himawari Imager (AHI) Data

“…The latest Collection 6 (C6) of MODIS LAI/FPAR products was released in 2015. These have been comprehensively evaluated with other data sets and validated with ground measurements, giving confidence on their accuracy and consistency with other existing LAI/FPAR products [43][44][45][46][47][48]. The C6 LAI/FPAR product provides LAI/FPAR retrievals at 500 meters pixel size from Terra MODIS (morning overpass), Aqua MODIS (afternoon overpass), and Terra MODIS + Aqua MODIS combined.…”

Assessment of Multi-Source Evapotranspiration Products over China Using Eddy Covariance Observations

“…There are a number of available LUC (e.g., Table S3) and NDVI/LAI/VOD products derived from different data sources (e.g., various satellite images), algorithms, and classification schemes [114,115]. It should be noted, however, that these datasets were produced for specific purposes and applications, including analyses of LUC and vegetation changes and their impacts on the climate, hydrology, and ecosystem, and the developments of various geo-scientific models; thus, obvious discrepancies and even errors in these products have been reported, especially at the regional scale [115][116][117][118][119][120][121][122][123][124][125]. Therefore, without considering the suitability of LUC and NDVI/LAI/VOD products, biases originating from raw data and inconsistencies among the selected products and uncertainties owing to product selection and processing can be of the same magnitude as those from the representation of the processes under investigation [113,121,[126][127][128][129].…”