A strong positive correlation between vegetation canopy bidirectional reflectance factor (BRF) in the near infrared (NIR) spectral region and foliar mass-based nitrogen concentration (%N) has been reported in some temperate and boreal forests. This relationship, if true, would indicate an additional role for nitrogen in the climate system via its influence on surface albedo and may offer a simple approach for monitoring foliar nitrogen using satellite data. We report, however, that the previously reported correlation is an artifact—it is a consequence of variations in canopy structure, rather than of %N. The data underlying this relationship were collected at sites with varying proportions of foliar nitrogen-poor needleleaf and nitrogen-rich broadleaf species, whose canopy structure differs considerably. When the BRF data are corrected for canopy-structure effects, the residual reflectance variations are negatively related to %N at all wavelengths in the interval 423–855 nm. This suggests that the observed positive correlation between BRF and %N conveys no information about %N. We find that to infer leaf biochemical constituents, e.g., N content, from remotely sensed data, BRF spectra in the interval 710–790 nm provide critical information for correction of structural influences. Our analysis also suggests that surface characteristics of leaves impact remote sensing of its internal constituents. This further decreases the ability to remotely sense canopy foliar nitrogen. Finally, the analysis presented here is generic to the problem of remote sensing of leaf-tissue constituents and is therefore not a specific critique of articles espousing remote sensing of foliar %N.

The aim of this paper is to assess the latest version of the MODIS LAI/FPAR product (MOD15A2H), namely Collection 6 (C6). We comprehensively evaluate this product through three approaches: validation with field measurements, intercomparison with other LAI/FPAR products and comparison with climate variables. Comparisons between ground measurements and C6, as well as C5 LAI/FPAR indicate: (1) MODIS LAI is closer to true LAI than effective LAI; (2) the C6 product is considerably better than C5 with RMSE decreasing from 0.80 down to 0.66; (3) both C5 and C6 products overestimate FPAR over sparsely-vegetated areas. Intercomparisons with three existing global LAI/FPAR products (GLASS, CYCLOPES and GEOV1) are carried out at site, continental and global scales. MODIS and GLASS (CYCLOPES and GEOV1) agree better with each other. This is expected because the surface reflectances, from which these products were derived, were obtained from the same instrument. Considering all biome types, the RMSE of LAI (FPAR) derived from any two products ranges between 0.36 (0.05) and 0.56 (0.09). Temporal comparisons over seven sites for the 2001–2004 period indicate that all products properly capture the seasonality in different biomes, except evergreen broadleaf forests, where infrequent observations due to cloud contamination induce unrealistic variations. Thirteen years of C6 LAI, temperature and precipitation time series data are used to assess the degree of correspondence between their variations. The statistically-significant associations between C6 LAI and climate variables indicate that C6 LAI has the potential to provide reliable biophysical information about the land surface when diagnosing climate-driven vegetation responses.

As the latest version of Moderate Resolution Imaging Spectroradiometer (MODIS) Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR) products, Collection 6 (C6) has been distributed since August 2015. This collection is evaluated in this two-part series with the goal of assessing product accuracy, uncertainty and consistency with the previous version. In this first paper, we compare C6 (MOD15A2H) with Collection 5 (C5) to check for consistency and discuss the scale effects associated with changing spatial resolution between the two collections and benefits from improvements to algorithm inputs. Compared with C5, C6 benefits from two improved inputs: (1) L2G–lite surface reflectance at 500 m resolution in place of reflectance at 1 km resolution; and (2) new multi-year land-cover product at 500 m resolution in place of the 1 km static land-cover product. Global and seasonal comparison between C5 and C6 indicates good continuity and consistency for all biome types. Moreover, inter-annual LAI anomalies at the regional scale from C5 and C6 agree well. The proportion of main radiative transfer algorithm retrievals in C6 increased slightly in most biome types, notably including a 17% improvement in evergreen broadleaf forests. With same biome input, the mean RMSE of LAI and FPAR between C5 and C6 at global scale are 0.29 and 0.091, respectively, but biome type disagreement worsens the consistency (LAI: 0.39, FPAR: 0.102). By quantifying the impact of input changes, we find that the improvements of both land-cover and reflectance products improve LAI/FPAR products. Moreover, we find that spatial scale effects due to a resolution change from 1 km to 500 m do not cause any significant differences.

The National Oceanic and Atmospheric Administration (NOAA) Deep Space Climate Observatory (DSCOVR) spacecraft was launched on 11 February 2015 and in June 2015 achieved its orbit at the first Lagrange point (L1), 1.5 million km from Earth toward the sun. There are two National Aeronautics and Space Administration (NASA) Earth-observing instruments on board: 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 preprocessing steps necessary for EPIC calibration including the geolocation algorithm and the radiometric calibration for each wavelength channel in terms of EPIC counts per second for conversion to reflectance units. The principal EPIC products are total ozone (O₃) amount, scene reflectivity, erythemal irradiance, ultraviolet (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.

The operational Moderate Resolution Imaging Spectroradiometer (MODIS) Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation absorbed by vegetation (FPAR) algorithm has been successfully implemented for Visible Infrared Imager Radiometer Suite (VIIRS) observations by optimizing a small set of configurable parameters in Look-Up-Tables (LUTs). Our preliminary evaluation showed reasonable agreement between VIIRS and MODIS LAI/FPAR retrievals. However, there is a need for a more comprehensive investigation to assure continuity of multi-sensor global LAI/FPAR time series, as the preliminary evaluation was spatiotemporally limited. In this study, we use a multi-year (2012–2016) global LAI/FPAR product generated from VIIRS and MODIS to evaluate for spatiotemporal consistency. We also quantify uncertainty of the product by utilizing available ground measurements. For both consistency and uncertainty evaluation, we account for variations in biome type and temporal resolution. Our results indicate that the LAI/FPAR retrievals from VIIRS and MODIS are consistent at different spatial (i.e., global and site) and temporal (i.e., 8-day, seasonal and annual) scales. The estimate of mean discrepancy (−0.006 ± 0.013 for LAI and −0.002 ± 0.002 for FPAR) meets the stability requirement for long-term LAI/FPAR Earth System Data Records (ESDRs) from multi-sensors as suggested by the Global Climate Observing System (GCOS). It is noteworthy that the rate of retrievals from the radiative transfer-based main algorithm is also comparable between two sensors. However, a relatively larger discrepancy over tropical forests was observed due to reflectance saturation and an unexpected interannual variation of main algorithm success was noticed due to instability in input surface reflectances. The uncertainties/relative uncertainties of VIIRS and MODIS LAI (FPAR) products assessed through comparisons to ground measurements are estimated to be 0.60/42.2% (0.10/24.4%) and 0.55/39.3% (0.11/26%), respectively. Note that the validated LAI were only distributed in low domains (~2.5), resulting in large relative uncertainty. Therefore, more ground measurements are needed to achieve a more comprehensive evaluation result of product uncertainty. The results presented here generally imbue confidence in the consistency between VIIRS and MODIS LAI/FPAR products and the feasibility of generating long-term multi-sensor LAI/FPAR ESDRs time series.