MODIS leaf area index products: from validation to algorithm improvement

By Wenze Yang, Bin Tan, Dong Huang, Miina Rautiainen, N. Shabanov, Yujie Wang, J. Privette, K. Huemmrich, R. Fensholt, I. Sandholt and 6 more. IEEE Transactions on Geoscience and Remote Sensing, 2006

364 citations recorded by the source corpus.

Global products of vegetation green Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation absorbed by vegetation (FPAR) are being operationally produced from Terra and Aqua Moderate Resolution Imaging Spectroradiometers (MODIS) at 1-km resolution and eight-day frequency. This paper summarizes the experience of several collaborating investigators on validation of MODIS LAI products and demonstrates the close connection between product validation and algorithm refinement activities. The validation of moderate resolution LAI products includes three steps: 1) field sampling representative of LAI spatial distribution and dynamic range within each major land cover type at the validation site; 2) development of a transfer function between field LAI measurements and high resolution satellite data to generate a reference LAI map over an extended area; and 3) comparison of MODIS LAI with aggregated reference LAI map at patch (multipixel) scale in view of geo-location and pixel shift uncertainties. The MODIS LAI validation experiences, summarized here, suggest three key factors that influence the accuracy of LAI retrievals: 1) uncertainties in input land cover data, 2) uncertainties in input surface reflectances, and 3) uncertainties from the model used to build the look-up tables accompanying the algorithm. This strategy of validation efforts guiding algorithm refinements has led to progressively more accurate LAI products from the MODIS sensors aboard NASA's Terra and Aqua platforms

Subfields: Ecology, Environmental Engineering, Plant Science

View Paper: https://www.semanticscholar.org/paper/9f35082bb19b248136c404a1a73449894b3abcd9

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Citations in the corpus

A Bayesian object based approach for estimating vegetation biophysical and biochemical variables from APEX at sensor radiance data (2013, background): “Assigning the correct crop type and associated a priori data to each object is crucial for a good estimation performance (Houborg & Boegh, 2008; Yang et al., 2006).”
Global Data Sets of Vegetation Leaf Area Index (LAI)3g and Fraction of Photosynthetically Active Radiation (FPAR)3g Derived from Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) for the Period 1981 to 2011 (2013, methodology): “These efforts have resulted in establishing uncertainty estimates for the MODIS LAI and FPAR products [36].”
A comparison of methods for smoothing and gap filling time series of remote sensing observations - application to MODIS LAI products (2012, methodology): “However, the LAI estimates using the back-up algorithm are of lower quality mostly due to residual clouds and poor atmospheric correction (Yang et al., 2006a, b).”
Long-Time-Series Global Land Surface Satellite Leaf Area Index Product Derived From MODIS and AVHRR Surface Reflectance (2016, neutral): “lower quality, mainly because of residual clouds and poor atmospheric correction [14].”
Analysis of Global LAI/FPAR Products from VIIRS and MODIS Sensors for Spatio-Temporal Consistency and Uncertainty from 2012–2016 (2018, methodology): “Because of the spatial scale mismatch between ground measurements (tens of meters) and products (500-m), the “bottom-up” approach proposed by the Committee on Earth Observation Satellites (CEOS) Land Product Validation (LPV) subgroup is adopted in this practice [37,44].”
Theoretical uncertainty analysis of global MODIS, CYCLOPES, and GLOBCARBON LAI products using a triple collocation method (2012, background): “…the vegetation structural and optical parameter space: (1) grasses and cereal crops, (2) shrubs, (3) broadleaf crops, (4) savannas, (5) deciduous broadleaf forests, (6) evergreen broadleaf forests, (7) deciduous needleleaf forests, and (8) evergreen needleleaf forests (Yang et al., 2006b).”