The leaf area index (LAI) is a critical variable used to characterize the terrestrial ecosystem and model land surface pro- cesses. Remote sensing is an ideal tool for mapping the LAI. How- ever, the quality of current satellite LAI products does not meet the requirements of the user community in terms of estimation accuracy and data consistency. One way to address these issues is to develop LAI integration algorithms that incorporate existing multiple LAI products and prior knowledge. This paper presents a new data integration method based on empirical orthogonal func- tion (EOF) analysis. The proposed EOF integration algorithm can be operated on both fine and coarse spatial resolution to accommo- date the problems arising from a large volume of data. Two runs of multivariate EOF analysis are proposed to address the issue of incompatible temporal resolutions among different data sets. Comparisons with high-spatial-resolution LAI reference maps at 12 sites over North America show that the proposed method can improve LAI product accuracy. After data integration, R 2 increases from 0.75 to 0.81 and the root mean square error (rmse) decreases from 1.04 to 0.71 over moderate-resolution imaging spectroradiometer (MODIS) products. The improvement of R 2 and rmse over Carbon cYcle and Change in Land Observational Products from an Ensemble of Satellites (CYCLOPES) products is not as significant as that over MODIS products. However, the use of a combination of multiple data sources reduces the bias of the LAI estimate from MODIS's 0.3 and CYCLOPES's −0.2 to −0.1.
[Show abstract][Hide abstract] ABSTRACT: Leaf area index (LAI) is a key biophysical variable for environmental process modelling. Remotely sensed data have become the primary source for estimation of LAI at the scales from local to global. A summary of existing LAI data sets and a discussion of their appropriateness for the formerly Soviet Central Asia, especially Kazakhstan, which is known for its huge grassland area (about 2 million km2), are valuable for environmental modelling in this region. The paper gives a brief review of existing global LAI products, such as AVHRR LAI, MODIS LAI, and SPOT-VEGETATION LAI, and shows that validation of these products in Kazakhstan as well as in other countries of the formerly Soviet Central Asia has not been carried out yet. Apart from the global LAI products, there are just a few data sets retrieved by remote sensing methods at subregional and regional scales in Kazakhstan. More research activities are needed to focus on the validation of the available global LAI products over the formerly Soviet Central Asia and developing new LAI data sets suitable for application in environmental modelling at different scales in this region.
Journal of Sensors 01/2012; 2012(1687-725X). DOI:10.1155/2012/582159 · 1.18 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: This paper presents a multiannual comparison at regional scale of currently available 1-km global leaf area index (LAI) products with crop-specific green area index (GAI) retrieved from MODIS 250-m spatial resolution imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS). The crop-specific GAI product benefits from the following extra processing steps: 1) spatial filtering of time series based on pixel purity; 2) transforming the time scale to thermal time; and 3) fitting a canopy structural dynamic model to smooth out the signal. In order to perform a rigorous comparison, these steps were also applied to the 1-km LAI products, namely, MODIS LAI (MCD15) and LAI produced in the CYCLOPES (Carbon cYcle and Change in Land Observational Products from an Ensemble of Satellites) project. A simple indicator was also designed to quantify the increase in temporal smoothness that can thus be obtained. The results confirm that, for winter wheat, the 250-m GAI product provides a more realistic description of the time course of the biophysical variable in terms of reaching higher values, grasping the variability, and providing smoother time series. However, the use of thermal time and pixel purity also improves the temporal consistency and coherence of the 1-km products. Overall, the results of this study suggest that these techniques could be valuable in harmonizing remote sensing data coming from different sources with varying spatial and temporal resolution for enhanced vegetation monitoring.
IEEE Transactions on Geoscience and Remote Sensing 04/2013; 51(4):2119-2127. DOI:10.1109/TGRS.2012.2226731 · 3.51 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Leaf area index (LAI) products at regional and global scales are being routinely generated from individual instrument data acquired at a specific time. As a result of cloud contamination and other factors, these LAI products are spatially and temporally discontinuous and are also inaccurate for some vegetation types in many areas. A better strategy is to use multi-temporal data. In this paper, a method was developed to estimate LAI from time-series remote sensing data using general regression neural networks (GRNNs). A database was generated from Moderate-Resolution Imaging Spectroradiometer (MODIS) and CYCLOPES LAI products as well as MODIS reflectance products of the BELMANIP sites during the period from 2001-2003. The effective CYCLOPES LAI was first converted to true LAI, which was then combined with the MODIS LAI according to their uncertainties determined from the ground-measured true LAI. The MODIS reflectance was reprocessed to remove remaining effects. GRNNs were then trained over the fused LAI and reprocessed MODIS reflectance for each biome type to retrieve LAI from time-series remote sensing data. The reprocessed MODIS reflectance data from an entire year were inputted into the GRNNs to estimate the 1-year LAI profiles. Extensive validations for all biome types were carried out, and it was demonstrated that the method is able to estimate temporally continuous LAI profiles with much improved accuracy compared with that of the current MODIS and CYCLOPES LAI products. This new method is being used to produce the Global Land Surface Satellite LAI products in China.
IEEE Transactions on Geoscience and Remote Sensing 01/2014; 52(1):209-223. DOI:10.1109/TGRS.2013.2237780 · 3.51 Impact Factor
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