The Normalized Difference Vegetation Index (NDVI) time-series data derived from Advanced Very High Resolution Radiometer (AVHRR) have been extensively used for studying inter-annual dynamics of ...global and regional vegetation. However, there can be significant uncertainties in the data due to incomplete atmospheric correction and orbital drift of the satellites through their active life. Access to location specific quantification of uncertainty is crucial for appropriate evaluation of the trends and anomalies. This paper provides per pixel quantification of orbital drift related spurious trends in Long Term Data Record (LTDR) AVHRR NDVI data product. The magnitude and direction of the spurious trends was estimated by direct comparison with data from MODerate resolution Imaging Spectrometer (MODIS) Aqua instrument, which has stable inter-annual sun-sensor geometry. The maps show presence of both positive as well as negative spurious trends in the data. After application of the BRDF correction, an overall decrease in positive trends and an increase in number of pixels with negative spurious trends were observed. The mean global spurious inter-annual NDVI trend before and after BRDF correction was 0.0016 and -0.0017 respectively. The research presented in this paper gives valuable insight into the magnitude of orbital drift related trends in the AVHRR NDVI data as well as the degree to which it is being rectified by the MODIS BRDF correction algorithm used by the LTDR processing stream.
The correction of atmospheric effects on optical remote sensing products is an essential component of Analysis Ready Data (ARD) production lines. The MAJA processor aims at providing accurate time ...series of surface reflectances over land for satellite missions, such as Sentinel-2, Venμs, and Landsat 8. The Centre d’Études Spatiales de la Biosphère (CESBIO) and the Centre National d’Études Spatiales (CNES) share a common effort to maintain, validate, and improve the MAJA processor, using state-of-the-art ground measurement sites, and participating in processor inter-comparisons, such as the Atmospheric Correction Intercomparison Exercise (ACIX). While contributing to the second ACIX-II Land validation exercise, it was found that the candidate MAJA dataset could not adequately be compared to the main reference dataset. MAJA reflectances were corrected for adjacency and topography effects while the reference dataset was not, excluding MAJA from a part of the performance metrics of the exercise. The first part of the following study aims at providing complementary performance assessment to ACIX-II by reprocessing MAJA surface reflectances without adjacency nor topographic correction, allowing for an un-biased full resolution comparison with the reference Sentinel-2 dataset. The second part of the study consists of validating MAJA against surface reflectance measurements time series of up to five years acquired at three automated stations. Both approaches provide extensive insights on the quality of MAJA Sentinel-2 Level 2 products.
The AVHRR (Advanced Very High Resolution Radiometer) series of instruments has frequently been used for vegetation studies. The 25+ year record has enabled important time-series studies. Many ...applications use NDVI (Normalized Difference Vegetation Index), or derivatives of it, as their operational variable. However, most AVHRR datasets have incomplete atmospheric correction, because of which there is considerable, but largely unknown, uncertainty in the significance of differences in NDVI and other short wave observations from AVHRR instruments.
The purpose of this study was to gain better understanding of the impact of incomplete or lack of atmospheric correction in widely-used, publicly available processed AVHRR-NDVI long-term datasets. This was accomplished by comparison with atmospherically corrected AVHRR data at AERONET (AErosol RObotic NETwork) sunphotometer sites in 1999. The datasets included in this study are: TOA (Top Of Atmosphere) that is with no atmospheric correction; PAL (Pathfinder AVHRR Land); and an early version of the new LTDR (Long Term Data Record) NDVI. The other publicly available datasets like GIMMS (Global Inventory Modeling and Mapping studies) and GVI (Global Vegetation Index) have atmospheric error budget similar to that of TOA, because no atmospheric correction is used in either processing stream. Of the three datasets, LTDR was found to have least errors (accuracy
=
0.0064 to −
0.024, precision
=
0.02 to 0.037 for clear and average atmospheric conditions) followed by PAL (accuracy
=
−
0.145 to −
0.035, precision
=
0.0606 to 0.0418), and TOA (accuracy
=
−
0.0791 to −
0.112, precision
=
0.0613 to 0.0684). It was also observed that temporal maximum value compositing technique does not cause significant improvement of precision in regions experiencing persistently high AOT (Aerosol Optical Thickness).
In this paper, we will evaluate the Vermote et al. method, hereafter referred to as VJB, in comparison to the MCD43 MODerate Resolution Imaging Spectroradiometer (MODIS) product, focusing on the ...white sky albedo parameter. We also present and study three different methods based on the VJB assumption, the 4param, 5param Rsqr, and 5param Vsqr. We use daily MODIS Climate Modeling Grid data both from Terra and Aqua platforms from 2002 to 2011 for all the pixels over Europe. We obtain an overall root-mean-square error of 5% when using the VJB method and 6.1%, 5.1%, and 5.3% for the 4param, 5param Rsqr, and 5param Vsqr methods, respectively. The main differences between the methods are located in areas where only few cloud-free snow-free samples were available that correspond mainly to mountainous areas during the winter. We finally conclude that the VJB method has an equivalent performance in deriving the white sky albedo results to the MODIS product with the advantage of daily temporal resolution. Additionally, we propose the 5param Rsqr method as an alternative to the VJB method due to its decreased data processing time.
In terrestrial and marine ecosystems, remote sensing has been used to estimate gross primary productivity (GPP) for decades, but few applications exist for shallow freshwater ecosystems.Here we show ...field-based GPP correlates with satellite and airborne lake color across a range of optically and limnologically diverse lakes in interior Alaska. A strong relationship between in situ GPP derived from stable oxygen isotopes (δ18O) and space-based lake color from satellites (e.g. Landsat-8, Sentinel-2 and CubeSats) and airborne imagery (AVIRIS-NG) demonstrates the potential power of this technique for improving spatial and temporal monitoring of lake GPP when coupled with additional field validation measurements across different systems. In shallow waters clear enough for sunlight to reach lake bottoms, both submerged vegetation (macrophytes and algae) and phytoplankton likely contribute to GPP. The stable isotopes and remotely sensed shallow lake color used here integrate both components. These results demonstrate the utility of lake color as a feasible means for mapping lake GPP from remote sensing. This novel methodology estimates GPP from remote sensing in shallow lakes by combining field measurements of oxygen isotopes with airborne, satellite and CubeSat imagery. This use of lake color for providing insight into ecological processes of shallow lakes is recommended, especially for remote arctic and boreal landscapes.
The Harmonized Landsat/Sentinel-2 (HLS) project aims to generate a seamless surface reflectance product by combining observations from USGS/NASA Landsat-8 and ESA Sentinel-2 remote sensing ...satellites. These satellites’ sampling characteristics provide nearly constant observation geometry and low illumination variation through the scene. However, the illumination variation throughout the year impacts the surface reflectance by producing higher values for low solar zenith angles and lower reflectance for large zenith angles. In this work, we present a model to derive the bidirectional reflectance distribution function (BRDF) normalization and apply it to the HLS product at 30 m spatial resolution. It is based on the BRDF parameters estimated from the MODerate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product (M{O,Y}D09) at 1 km spatial resolution using the VJB method (Vermote et al., 2009). Unsupervised classification (segmentation) of HLS images is used to disaggregate the BRDF parameters to the HLS spatial resolution and to build a BRDF parameters database at HLS scale. We first test the proposed BRDF normalization for different solar zenith angles over two homogeneous sites, in particular one desert and one Peruvian Amazon forest. The proposed method reduces both the correlation with the solar zenith angle and the coefficient of variation (CV) of the reflectance time series in the red and near infrared bands to 4% in forest and keeps a low CV of 3% to 4% for the deserts. Additionally, we assess the impact of the view zenith angle (VZA) in an area of the Brazilian Amazon forest close to the equator, where impact of the angular variation is stronger because it occurs in the principal plane. The directional reflectance shows a strong dependency with the VZA. The current HLS BRDF correction reduces this dependency but still shows an under-correction, especially in the near infrared, while the proposed method shows no dependency with the view angles. We also evaluate the BRDF parameters using field surface albedo measurements as a reference over seven different sites of the US surface radiation budget observing network (SURFRAD) and five sites of the Australian OzFlux network.
Aerosols play a critical role in radiative transfer within the
atmosphere, and they have a significant impact on climate change. In this
paper, we propose and implement a framework for developing an ...aerosol model
using their microphysical properties. Such microphysical properties as the
size distribution, the complex refractive index, and the percentage of
sphericity are derived from the global AERosol RObotic NETwork (AERONET).
These measurements, however, are typically retrieved when almucantar
measurement procedures are performed (i.e., early mornings and late
afternoons with clear sky) and might not have a temporal correspondence to
a satellite overpass time, so a valid validation of satellite-derived
products cannot be carried out. To address this problem of temporal
inconsistency of satellite and ground-based measurements, we developed an
approach to retrieve these microphysical properties (and the corresponding
aerosol model) using the optical thickness at 440 nm, τ440, and
the Ångström coefficient between 440 and 870 nm, α440–870. Such aerosol models were developed for 851 AERONET sites within the last 28 years. Obtained results
suggest that empirically microphysical properties can be retrieved with
uncertainties of up to 23 %. An exception is the imaginary part of the
refractive index ni, for which the derived uncertainties reach up to 38 %. These specific parametric models of aerosol can be used for the studies when
retrieval of microphysical properties is required as well as validation of
satellite-derived products over land. Specifically, we demonstrate the
usefulness of the aerosol models to validate surface reflectance records
over land derived from optical remote sensing sensors. We then quantify the
propagation of uncertainties in the surface
reflectance due to uncertainties with the aerosol model retrieval that is used as a reference from radiative transfer simulations. Results indicate that individual aerosol microphysical properties can impact uncertainties in surface reflectance retrievals between
3.5 × 10−5 to 1 × 10−3 (in reflectance units). The overall impact of
microphysical properties combined yields an overall uncertainty in surface
reflectance < 0.004 (in reflectance units). That corresponds, for
example, to 1 to 3 % of the retrieved surface reflectance in the red
spectral band (620–670 nm) by the Moderate Resolution Imaging
Spectroradiometer (MODIS) instrument. These uncertainty values are well
below the specification (0.005 + 0.05ρ; ρ is the retrieved
surface reflectance) used for the MODIS atmospheric correction.
The Visible Infrared Imaging Radiometer Suite (VIIRS) instrument was launched in October 2011 as part of the Suomi National Polar‐Orbiting Partnership (S‐NPP). The VIIRS instrument was designed to ...improve upon the capabilities of the operational Advanced Very High Resolution Radiometer and provide observation continuity with NASA's Earth Observing System's Moderate Resolution Imaging Spectroradiometer (MODIS). Since the VIIRS first‐light images were received in November 2011, NASA‐ and NOAA‐funded scientists have been working to evaluate the instrument performance and generate land and cryosphere products to meet the needs of the NOAA operational users and the NASA science community. NOAA's focus has been on refining a suite of operational products known as Environmental Data Records (EDRs), which were developed according to project specifications under the National Polar‐Orbiting Environmental Satellite System. The NASA S‐NPP Science Team has focused on evaluating the EDRs for science use, developing and testing additional products to meet science data needs, and providing MODIS data product continuity. This paper presents to‐date findings of the NASA Science Team's evaluation of the VIIRS land and cryosphere EDRs, specifically Surface Reflectance, Land Surface Temperature, Surface Albedo, Vegetation Indices, Surface Type, Active Fires, Snow Cover, Ice Surface Temperature, and Sea Ice Characterization. The study concludes that, for MODIS data product continuity and earth system science, an enhanced suite of land and cryosphere products and associated data system capabilities are needed beyond the EDRs currently available from the VIIRS.
Key Points
Provide an overview description of the VIIRS Land and Cryosphere products.
Dispel the myth that the VIIRS instrument won't provide continuity with MODIS.
Make the case for VIIRS Land and Cryosphere Science Products.
The bi-directional reflectance distribution function (BRDF) has been widely studied across different vegetation types. However, these studies generally report values for only one point in time. We ...were interested in the potential for seasonal and inter-annual variation in BRDF parameters. NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on board the EOS satellites has now been collecting data for 10
years. Since BRDF parameters are reported for the individual spectral bands, these data can be used to examine intra-annual variation. However, MODIS BRDF parameters are not calculated for the various vegetation indices which are derived from the spectral bands. Our objective in this study was to use the 10
years of MODIS data now available to examine seasonal and inter-annual variation in the view angle sensitivity of three vegetation indices; the normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI), and the photochemical reflectance index (PRI) at 3 flux tower sites (Harvard Forest, Howland Forest and Morgan Monroe State Forest). For these 3 sites, only EVI was significantly affected by view angle. There was also a substantial variation in the view angle sensitivity of EVI across seasons and this variation was different for backscatter vs. forward scatter data. It is possible that differences in the scattering of radiation between the spring and the fall are responsible for the seasonal difference in view angle sensitivity. There were also complimentary variations in forward and backscatter view angle sensitivity of EVI across years. The greater view angle sensitivity of EVI, as opposed to NDVI, suggests that greater care must be taken to correct for BRDF effects when using this vegetation index.
► Ten years of MODIS data used for view angle sensitivity analysis. ► EVI was sensitive to view angle but PRI and NDVI were not. ► View angle sensitivity of EVI varied across seasons and years.
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