Dong Huang

MODIS leaf area index products: from validation to algorithm improvement

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 NASAs Terra and Aqua platforms

Analysis and optimization of the MODIS leaf area index algorithm retrievals over broadleaf forests

Broadleaf forest is a major type of Earths land cover with the highest observable vegetation density. Retrievals of biophysical parameters, such as leaf area index (LAI), of broadleaf forests at global scale constitute a major challenge to modern remote sensing techniques in view of low sensitivity (saturation) of surface reflectances to such parameters over dense vegetation. The goal of the performed research is to demonstrate physical principles of LAI retrievals over broadleaf forests with the Moderate Resolution Imaging Spectroradiometer (MODIS) LAI algorithm and to establish a basis for algorithm refinement. To sample natural variability in biophysical parameters of broadleaf forests, we selected MODIS data subsets covering deciduous broadleaf forests of the eastern part of North America and evergreen broadleaf forests of Amazonia. The analysis of an annual course of the Terra MODIS Collection 4 LAI product over broadleaf forests indicated a low portion of best quality main radiative transfer-based algorithm retrievals and dominance of low-reliable backup algorithm retrievals during the growing season. We found that this retrieval anomaly was due to an inconsistency between simulated and MODIS surface reflectances. LAI retrievals over dense vegetation are mostly performed over a compact location in the spectral space of saturated surface reflectances, which need to be accurately modeled. New simulations were performed with the stochastic radiative transfer model, which poses high numerical accuracy at the condition of saturation. Separate sets of parameters of the LAI algorithm were generated for deciduous and evergreen broadleaf forests to account for the differences in the corresponding surface reflectance properties. The optimized algorithm closely captures physics of seasonal variations in surface reflectances and delivers a majority of LAI retrievals during a phenological cycle, consistent with field measurements. The analysis of the optimized retrievals indicates that the precision of MODIS surface reflectances, the natural variability, and mixture of species set a limit to improvements of the accuracy of LAI retrievals over broadleaf forests.

Analysis of leaf area index and fraction of PAR absorbed by vegetation products from the terra MODIS sensor: 2000-2005

The analysis of two years of Collection 3 and five years of Collection 4 Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR) data sets is presented in this article with the goal of understanding product quality with respect to version (Collection 3 versus 4), algorithm (main versus backup), snow (snow-free versus snow on the ground), and cloud (cloud-free versus cloudy) conditions. Retrievals from the main radiative transfer algorithm increased from 55% in Collection 3 to 67% in Collection 4 due to algorithm refinements and improved inputs. Anomalously high LAI/FPAR values observed in Collection 3 product in some vegetation types were corrected in Collection 4. The problem of reflectance saturation and too few main algorithm retrievals in broadleaf forests persisted in Collection 4. The spurious seasonality in needleleaf LAI/FPAR fields was traced to fewer reliable input data and retrievals during the boreal winter period. About 97% of the snow covered pixels were processed by the backup Normalized Difference Vegetation Index-based algorithm. Similarly, a majority of retrievals under cloudy conditions were obtained from the backup algorithm. For these reasons, the users are advised to consult the quality flags accompanying the LAI and FPAR product

Validation of Moderate Resolution Imaging Spectroradiometer leaf area index product in croplands of Alpilles, France

[1]xa0This paper presents results of validating the Collection 4 Moderate Resolution Imaging Spectroradiometer (MODIS) leaf area index (LAI) product using LAI data collected in a 3 × 3 km agricultural (grasses and cereal crops) area near Avignon, France, and 30 m resolution Enhanced Thematic Mapper (ETM+) image. Estimates of the accuracy, precision, and uncertainty with which the ETM+ data convey information about LAI underlie the derivation of a 30 m resolution reference LAI map by accounting for both field measurement and satellite observation errors. The 30 m reference LAI was then extrapolated from sampling points to a 58 km2 area without loss in the quality and was degraded to a 1 km resolution LAI map. The latter was taken as a reference to assess the quality of the MODIS LAI product. Comparison of the reference and corresponding MODIS retrievals suggests that Collection 4 MODIS LAI is accurate to within an accuracy of 0.3 with a precision and uncertainty of 0.23 and 0.38, respectively. It was found that the Collection 3 MODIS land cover product, input to the Collection 4 operational LAI algorithm, misclassified the 58 km2 area as broadleaf crops. The use of correct biome type in the operational processing improves the accuracy in LAI by a factor of 2 with an almost unchanged precision and uncertainty. Our results also indicate that the retrieval of LAI from satellite data is an ill-posed problem; that is, small variations in input due to observation errors result in a very low precision of the desired parameter. Any retrieval technique based on a simple model inversion or empirical relationships is unable to generate stable retrievals. The use of information on input errors in the retrieval technique is necessary to generate solutions to the ill-posed problem. The MODIS operational LAI algorithm meets this requirement.

The importance of measurement errors for deriving accurate reference leaf area index maps for validation of moderate-resolution satellite LAI products

The validation of moderate-resolution satellite leaf area index (LAI) products such as those operationally generated from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor data requires reference LAI maps developed from field LAI measurements and fine-resolution satellite data. Errors in field measurements and satellite data determine the accuracy of the reference LAI maps. This paper describes a method by which reference maps of known accuracy can be generated with knowledge of errors in fine-resolution satellite data. The method is demonstrated with data from an international field campaign in a boreal coniferous forest in northern Sweden, and Enhanced Thematic Mapper Plus images. The reference LAI map thus generated is used to assess modifications to the MODIS LAI/fPAR algorithm recently implemented to derive the next generation of the MODIS LAI/fPAR product for this important biome type

Feedbacks of Vegetation on Summertime Climate Variability over the North American Grasslands. Part II: A Coupled Stochastic Model

Abstract A coupled linear model is derived to describe interactions between anomalous precipitation and vegetation over the North American Grasslands. The model is based on biohydrological characteristics in the semiarid environment and has components to describe the water-related vegetation variability, the long-term balance of soil moisture, and the local soil–moisture–precipitation feedbacks. Analyses show that the model captures the observed vegetation dynamics and characteristics of precipitation variability during summer over the region of interest. It demonstrates that vegetation has a preferred frequency response to precipitation forcing and has intrinsic oscillatory variability at time scales of about 8 months. When coupled to the atmospheric fields, such vegetation signals tend to enhance the magnitudes of precipitation variability at interannual or longer time scales but damp them at time scales shorter than 4 months; the oscillatory variability of precipitation at the growing season time scale...

Intraseasonal Interactions between Temperature and Vegetation over the Boreal Forests

Abstract This paper uses statistical and analytical techniques to investigate intraseasonal interactions between temperature and vegetation [surrogated by the normalized difference vegetation index (NDVI)] over the boreal forests. Results indicate that interactions between the two fields may be approximated as a coupled second-order system, in which the variability of NDVI and temperature of the current month is significantly regulated by lagged NDVI anomalies from the preceding two months. In particular, the influence from the one-month lagged NDVI anomalies upon both temperature and vegetation variability is generally positive, but the influence from the second-month lagged NDVI anomalies is often negative. Such regulations lead to an intrinsic oscillatory variability of vegetation at growing-season time scales across the study domain. The regulation of temperature variability by NDVI anomalies is most significant over interior Asia (Siberia), suggesting strong vegetation–atmosphere couplings over these...

Earth Observations from DSCOVR EPIC Instrument

The NOAA Deep Space Climate Observatory (DSCOVR) spacecraft was launched on February 11, 2015, and in June 2015 achieved its orbit at the first Lagrange point or L1, 1.5 million km from Earth towards the Sun. There are two NASA Earth observing instruments onboard: the Earth Polychromatic Imaging Camera (EPIC) and the National Institute of Standards and Technology Advanced Radiometer (NISTAR). The purpose of this paper is to describe various capabilities of the DSCOVR/EPIC instrument. EPIC views the entire sunlit Earth from sunrise to sunset at the backscattering direction (scattering angles between 168.5° and 175.5°) with 10 narrowband filters: 317, 325, 340, 388, 443, 552, 680, 688, 764 and 779 nm. We discuss a number of pre-processingsteps necessary for EPIC calibration including the geolocation algorithm and the radiometric calibration for each wavelength channel in terms of EPIC counts/second for conversion to reflectance units. The principal EPIC products are total ozone O3amount, scene reflectivity, erythemal irradiance, UV aerosol properties, sulfur dioxide SO2 for volcanic eruptions, surface spectral reflectance, vegetation properties, and cloud products including cloud height. Finally, we describe the observation of horizontally oriented ice crystals in clouds and the unexpected use of the O2 B-band absorption for vegetation properties.