Multiyear trends in Normalized Difference Vegetation Index (NDVI) have been used as metrics of high latitude ecosystem change based on the assumption that NDVI change is associated with ecological ...change, generally as changes in green vegetation amount (green leaf area index LAI or plant cover). Further, no change in NDVI is often interpreted as no change in these variables. Three canopy reflectance models including linear mixture model, the SAIL (Scattering from Arbitrarily Inclined Leaves) model, and the GeoSail model were used to simulate scenarios representing high latitude landscape NDVI responses to changes in LAI and plant cover. The simulations showed inconsistent NDVI responses. Clear increases in NDVI are generally associated with increases in LAI and plant cover. At higher values of LAI, the change in NDVI per unit change in LAI decreases, with very little change in spruce forest NDVI where crown cover is >50% and at the tundra–taiga ecotone with transitions from shrub tundra to spruce woodland. These lower responses may bias the interpretation of greening/browning trends in boreal forests. Variations in water or snow coverage were shown to produce outsized nonbiological NDVI responses. Inconsistencies in NDVI responses exemplify the need for care in the interpretation of NDVI change as a metric of high latitude ecosystem change, and that landscape characteristics in terms of the type of cover and its characteristics, such as the initial plant cover, must be taken into account in evaluating the significance of any observed NDVI trends.
The Earth Observing One (EO-1) satellite has completed 16 years of Earth observations in early 2017. What started as a technology mission to test various new advancements turned into a science and ...application mission that extended many years beyond the satellite's planned life expectancy. EO-1's primary instruments are spectral imagers: Hyperion, the only civilian full spectrum spectrometer (430-2400 nm) in orbit; and the Advanced Land Imager (ALI), the prototype for Landsat-8's pushbroom imaging technology. Both Hyperion and ALI instruments have continued to perform well, but in February 2011 the satellite ran out of the fuel necessary to maintain orbit, which initiated a change in precession rate that led to increasingly earlier equatorial crossing times during its last five years. The change from EO-1's original orbit, when it was formation flying with Landsat-7 at a 10:01am equatorial overpass time, to earlier overpass times results in image acquisitions with increasing solar zenith angles (SZAs). In this study, we take several approaches to characterize data quality as SZAs increased. Our results show that for both EO-1 sensors, atmospherically corrected reflectance products are within 5 to 10% of mean pre-drift products. No marked trend in decreasing quality in ALI or Hyperion is apparent through 2016, and these data remain a high quality resource through the end of the mission.
Using a helicopter-mounted portable spectroradiometer and continuous eddy covariance data we were able to evaluate the photochemical reflectance index (PRI) as an indicator of canopy photosynthetic ...light-use efficiency (LUE) in four boreal forest species during the Boreal Ecosystem Atmosphere experiment (BOREAS). PRI was calculated from narrow waveband reflectance data and correlated with LUE calculated from eddy covariance data. Significant linear correlations were found between PRI and LUE when the four species were grouped together and when divided into functional type: coniferous and deciduous. Data from the helicopter-mounted spectroradiometer were then averaged to represent data generated by the Airborne Visible Infrared Imaging Spectrometer (AVIRIS). We calculated PRI from these data and relationships with canopy LUE were investigated. The relationship between PRI and LUE was weakened for deciduous species but strengthened for the coniferous species. The robust nature of this relationship suggests that relative photosynthetic rates may be derived from remotely-sensed reflectance measurements.
Gross ecosystem production (GEP) can be estimated at the global scale and in a spatially continuous mode using models driven by remote sensing. Multiple studies have demonstrated the capability of ...high resolution optical remote sensing to accurately measure GEP at the leaf and stand level, but upscaling this relationship using satellite data remains challenging. Canopy structure is one of the complicating factors as it not only alters the strength of a measured signal depending on integrated leaf‐angle‐distribution and sun‐observer geometry, but also drives the photosynthetic output and light‐use‐efficiency (ɛ) of individual leaves. This study introduces a new approach for upscaling multiangular canopy level reflectance measurements to satellite scales which takes account of canopy structure effects by using Light Detection and Ranging (LiDAR). A tower‐based spectro‐radiometer was used to observe canopy reflectances over an annual period under different look and solar angles. This information was then used to extract sunlit and shaded spectral end‐members corresponding to minimum and maximum values of canopy‐ɛ over 8‐d intervals using a bidirectional reflectance distribution model. Using three‐dimensional information of the canopy structure obtained from LiDAR, the canopy light regime and leaf area was modeled over a 12 km2 area and was combined with spectral end‐members to derive high resolution maps of GEP. Comparison with eddy covariance data collected at the site shows that the spectrally driven model is able to accurately predict GEP (r2 between 0.75 and 0.91, p < 0.05).
Monthly measurements of leaf area index (LAI) and the fraction of absorbed photosynthetically active radiation (f
APAR
) taken at approximately monthly intervals were collected along three 750 m ...transects in a Kalahari woodland near Mongu in western Zambia. These data were compared with MODIS NDVI (MOD13, Collection 3) and MODIS LAI and f
APAR
products (MOD15, Collection 3) over a 2 year period (2000-2002). MODIS and ground-measured LAI values corresponded well, while there was a significant bias between MODIS and ground-measured f
APAR
even though both MODIS variables are produced from the same algorithm. Solar zenith angle effects, differences between intercepted and absorbed photosynthetically active radiation, and differences in measurement of f
APAR
(photon counts versus energy) were examined and rejected as explanations for the discrepancies between MODIS and ground-measured f
APAR
. Canopy reflectance model simulations produced different values of f
APAR
with the same LAI when canopy cover was varied, indicating that errors in the estimation of canopy cover in the MODIS algorithm due to the land cover classification used are a possible cause of the f
APAR
discrepancy. This is one of the first studies of MODIS land product performance in a time-series context. Despite a bias in f
APAR
, our results demonstrate that the woodland canopy phonology is captured in the MODIS product.
Using geometric shadow and linear mixture models we develop and evaluate an algorithm to infer several important structural parameters of stands of black spruce (Picea mariana), the most common ...boreal forest dominant. We show, first, that stand reflectances for this species can be represented as linear combinations of the reflectances of more elemental radiometric components: sunlit crowns, sunlit background, and shadow. Secondly, using a geometric model, we calculate how the fractions of these radiometric elements covary with each other. Then, using hand-held measurements of the reflectances of the sunlit background, sphagnum moss (Sphagnum spp.), and assuming shadow reflectance to be that of deep, clear lakes, we infer the reflectance of sunlit crowns from the geometric shadow model and low-altitude reflectance measurements acquired by a helicopter-mounted radiometer. Next, we assume that the reflectance for all black spruce stands is simply a linear combination of shadow, sunlit crown, and sunlit background reflectance, weighted in proportion to the relative areal fractions of these pixel elements. We then solve a set of linear equations for the areal fractions of these elements using as input helicopter observations of total stand reflectance. Using this algorithm, we infer the values for the areal proportions of sunlit canopy, sunlit background, and shadow for 31 black spruce stands of varying biomass density, net primary productivity, etc. We show empirically and theoretically that the areal proportions of these radiometric elements are related to a number of stand biophysical characteristics. Specifically, the shadow fraction is increasing with increasing biomass density, average diameter at breast height, leaf area index (LAI), and aboveground net primary productivity (NPP), while sunlit background fraction is decreasing. We show that the end member fractions can be used to estimate biomass with a standard error of $\approx$ 2 kg/m$^2$, LAI with a standard error of 0.58, dbh with a standard error of $\approx$ 2 cm, and aboveground NPP with a standard error of 0.07 $\mathrm{kg}\cdot \mathrm{m}^{-2}\cdot \mathrm{yr}$^{-1}$. We also show that the fraction of sunlit canopy is only weakly correlated with the biophysical variables and are thus able to show why a popular vegetation index, Normalized Difference Vegetation Index (NDVI), does not provide a useful measure of these biophysical characteristics. We do show, however, that NDVI should be related to the fraction of photosynthetically active radiation incident upon and absorbed by the canopy. This work has convinced us that a paradigm shift in the remote sensing of biophysical characteristics is in order--a shift away from direct inference of biophysical characteristics from vegetation indices and toward a two-step process, in which (1) stand-level reflectance is approximated in terms of linear combinations of reflectance-invariant, spectrally distinct components (spectral end members) and mixture decomposition used to infer the areal fractions of these components, e.g., shadow, sunlit crown, and sunlit background, followed by (2) the use of radiative transfer models to compute biophysical characteristic values as a function of the end member fractions.
Sentinel-2 satellite data enables multispectral monitoring of the earth at a high temporal revisit rate. Combining this information with a network of optical ground measurements enables a more ...detailed and a more complete understanding of terrestrial ecosystems. However, independent optical ground measurements often lack consistency, especially when comparing different sites in geographically remote locations. Using the very high temporal and spectral resolution offered by the automated field spectrometer systems FloX and RoX (Fluorescence Box and Reflectance Box, respectively, JB-Hyperspectral Devices GmbH, Duesseldorf, Germany), we investigated continuous time series ranging over three years and in ten different locations across Europe, Africa, America and Asia. The continuous records of ground-measured reflectance were first validated against Sentinel-2 top of canopy (TOC) reflectance to evaluate the consistency of the in-situ network. Our results suggest a good agreement of ground-measured reflectance with Sentinel-2 TOC reflectance in vegetation and snow with R2 around 0.79 in the 833 nm band and R2 up to 0.94 in the bands around 559 nm and 492 nm, demonstrating good consistency across the network. Spatial misalignment of Sentinel-2 pixel-sizes with respect to the different footprint sizes of the ten automated spectrometers on the ground, atmospheric uncertainties, sub-optimal instrument setup and spatial-temporal variable landscape heterogeneity were identified as the most relevant sources of uncertainties in the network. Comparing the Normalized Difference Vegetation Index (NDVI), Transformed Chlorophyll Absorption in Reflectance Index (TCARI) and Enhanced Vegetation Index (EVI) between ground and satellite revealed a decreasing agreement with increasing complexity of index formulation. The best agreement between satellite and ground was exhibited by NDVI with R2 around 0.96 and relative error of 4.3% investigating vegetation and snow across all ten sites. Furthermore, we identified a seasonal pattern in residuals of NDVI between ground and satellite in an alpine ecosystem in northern Italy, which was associated with increased spatial heterogeneity due to the effects of diverse vegetation phenology and snowfall. In contrast, a random distribution of residuals was recognized in a rather uniform oak forest canopy in southern France. Clustering Sentinel-2 pixels with respect to their temporal patterns in NDVI identified similar areas seen as homogenous in the canopy of Torgnon, Italy, and Observatoire de Haute-Provence (OHP), France, each. The very high temporal resolution of NDVI measured on the ground confirmed overlap with matched homogenous areas, but must consider seasonal landscape heterogeneity. Using well-standardized and globally homogenous Sentinel-2 TOC reflectance enabled the assessment of uncertainties in ten field spectrometer sites around the world. The standardization of the automated field spectrometers, their data products and data annotation were essential prerequisites that enabled joint validation against Sentinel-2. Harmonizing optical ground measurements with respect to a satellite is promising for future research to ensure the valid intercomparison and transfer of data products across different sites in a network worldwide.
Display omitted
•Sentinel-2 data used to harmonize 10 automated spectrometer sites around the world.•Good agreement in reflectance bands, very good agreement in VI across platforms.•Ground-based cloud filtering retained usable data more reliably than cloud-masks.•Clustered temporal signatures in S-2 pixels recognized variable homogenous areas.•Seasonality of spatial heterogeneity is important comparing ground and satellite.
Fundamental problems inherent to the existing land cover and biophysical characteristic algorithms are fourfold: (1) their failure to deal physically with global and interannual variations in surface ...reflectance arising from Sun and view angle variations, (2) decoupling of the land cover classification algorithm from the biophysical characteristic inference algorithm with no ability to control biophysical parameter estimation error arising from misclassification, (3) invalid statistical assumptions within classification algorithms used to model reflectance distribution functions, and (4) sole reliance on vegetation indices that can limit performance for several major land cover classes. To address these problems, we develop an integrated, physically based classification and biophysical characteristics estimation algorithm that utilizes canopy reflectance models to account directly for signature variations from Sun angle, topographic, and other variations. Our approach fuses into a single algorithm both land cover classification and biophysical characteristics estimation, permitting one set of physically based canopy reflectance models to be used for both. The use of canopy reflectance models eliminates the need for unrealistic assumptions, such as multivariate‐normal distributions, underlying many classification algorithms. Using the algorithm, we have classified a 10,000 km2 area of the BOREAS southern study area. Our classification shows that low‐productivity wetland conifer is the dominant land cover and that nearly 7% of the area is occupied by boreal fens, a major source of methane. In addition, nearly 23% of the area has been disturbed by either fire or logging in the last 20 years, suggesting an important role for disturbance to the regional carbon budget. A thorough evaluation of the physically based classifier within the southern study area shows accuracies superior to those obtained with conventional statistically based algorithms, implying even better performance when extended over multiple Landsat frames since the physically based approach can account directly for regional variations in reflectance resulting from varying illumination and viewing conditions (topography, Sun angle). The conifer biomass density estimation algorithm is based on our discovery of a convenient natural relationship between crown height and volumetric density that renders the biomass density for black spruce stands independent of tree height, and a function only of sunlit canopy fraction. This permits us to calculate directly the relationship between reflectance and biomass density. An evaluation of the algorithm using ground sites shows our algorithm can estimate black spruce biomass density with a root‐mean‐square error of 2.73 kg/ym2 for correctly classified sites. Our evaluation also demonstrates the importance of correct classification. Rootmean‐square errors for misclassified sites were 3.96 kg/m2. Using this approach we have estimated the biomass density in the BOREAS southern study area for the dominant land cover type in the circumpolar boreal ecosystem, wetland black spruce. These results show a bimodality to the biomass density regional distribution, controlled perhaps by underlying topographic and edaphic factors.
PDF
