Search

Differences in the relative spectral response functions of sensors lead to data inconsistencies that should be harmonized before multisensor exploitation. In this paper, we use spectral libraries to simulate satellite data and build models to correct them. We then explore and compare different models for coarse and medium spatial resolution optical sensors, including moderate resolution imaging spectroradiometer, advanced very high resolution radiometer (AVHRR), visible infrared imaging radiometer suite, multispectral instrument aboard Sentinel-2, and Operational Land Imager aboard Landsat 8. We found that optimal correction of different bands depends on the model used. For the green and near infrared bands, a multilinear land cover dependent regression improves the accuracy by up to 80.9%. For the red band, a novel exponential dependence of the spectral band adjustment factor with the normalized difference vegetation index (NDVI) provides an accuracy improvement of up to 72.8%. The best way to correct the NDVI value is to use the corrected NIR and red bands using these models. We apply the proposed methods to 445 BELMANIP2 sites using AVHRR data from the long-term data record from 1982–2017. High NDVI pixels result in 30-year trends varying up to 0.06 when comparing uncorrected to spectrally adjusted NDVI. Further application of these methods to NASA's Harmonized Landsat and Sentinel 2 product shows that for the red band and NDVI, our proposed method provides improved accuracy (54.6% and 62.5%) over the linear spectral adjustment currently used. [ABSTRACT FROM AUTHOR]

The Atmospheric Correction Inter-comparison eXercise (ACIX) is an international initiative with the aim to analyse the Surface Reflectance (SR) products of various state-of-the-art atmospheric correction (AC) processors. The Aerosol Optical Thickness (AOT) and Water Vapour (WV) are also examined in ACIX as additional outputs of AC processing. In this paper, the general ACIX framework is discussed; special mention is made of the motivation to initiate the experiment, the inter-comparison protocol, and the principal results. ACIX is free and open and every developer was welcome to participate. Eventually, 12 participants applied their approaches to various Landsat-8 and Sentinel-2 image datasets acquired over sites around the world. The current results diverge depending on the sensors, products, and sites, indicating their strengths and weaknesses. Indeed, this first implementation of processor inter-comparison was proven to be a good lesson for the developers to learn the advantages and limitations of their approaches. Various algorithm improvements are expected, if not already implemented, and the enhanced performances are yet to be assessed in future ACIX experiments. [ABSTRACT FROM AUTHOR]

The Advanced Very High Resolution Radiometer (AVHRR) sensor provides a unique global remote sensing dataset that ranges from the 1980s to the present. Over the years, several efforts have been made on the calibration of the different instruments to establish a consistent land surface reflectance time-series and to augment the AVHRR data record with data from other sensors, such as the Moderate Resolution Imaging Spectroradiometer (MODIS). In this paper, we present a summary of all the corrections applied to the AVHRR surface reflectance and NDVI Version 4 Product, developed in the framework of the National Oceanic and Atmospheric Administration (NOAA) Climate Data Record (CDR) program. These corrections result from assessment of the geolocation, improvement of cloud masking, and calibration monitoring. Additionally, we evaluate the performance of the surface reflectance over the AERONET sites by a cross-comparison with MODIS, which is an already validated product, and evaluation of a downstream leaf area index (LAI) product. We demonstrate the utility of this long time-series by estimating the winter wheat yield over the USA. The methods developed by Becker-Reshef et al. (2010) and Franch et al. (2015) are applied to both the MODIS and AVHRR data. Comparison of the results from both sensors during the MODIS-era shows the consistency of the dataset with similar errors of 10%. When applying the methods to AVHRR historical data from the 1980s, the results have errors equivalent to those derived from MODIS. [ABSTRACT FROM AUTHOR]

The correction of the atmospheric effects on optical satellite images is essential for quantitative and multi-temporal remote sensing applications. In order to study the performance of the state-of-the-art methods in an integrated way, a voluntary and open-access benchmark Atmospheric Correction Inter-comparison eXercise (ACIX) was initiated in 2016 in the frame of Committee on Earth Observation Satellites (CEOS) Working Group on Calibration & Validation (WGCV). The first exercise was extended in a second edition wherein twelve atmospheric correction (AC) processors, a substantially larger testing dataset and additional validation metrics were involved. The sites for the inter-comparison analysis were defined by investigating the full catalogue of the Aerosol Robotic Network (AERONET) sites for coincident measurements with satellites' overpass. Although there were more than one hundred sites for Copernicus Sentinel-2 and Landsat 8 acquisitions, the analysis presented in this paper concerns only the common matchups amongst all processors, reducing the number to 79 and 62 sites respectively. Aerosol Optical Depth (AOD) and Water Vapour (WV) retrievals were consequently validated based on the available AERONET observations. The processors mostly succeeded in retrieving AOD for relatively light to medium aerosol loading (AOD < 0.2) with uncertainties <0.08, while the overall uncertainty values were typically 0.23 ± 0.15. Better performances were observed for WV retrievals with >90% of the results falling within the suggested empirical specifications and with the Root Mean Square Error (RMSE) being mostly <0.25 g/cm2. Regarding Surface Reflectance (SR) validation two main approaches were followed. For the first one, a simulated SR reference dataset was computed over all of the test sites by using the 6SV (Second Simulation of the Satellite Signal in the Solar Spectrum vector code) full radiative transfer modelling (RTM) and AERONET measurements for the required aerosol variables and water vapour content. The performance assessment demonstrated that the retrievals were not biased for most of the bands. The uncertainties ranged from approximately 0.003 to 0.01 (excluding B01) for the best performing processors in both sensors' analyses. For the second one, measurements from the radiometric calibration network RadCalNet over La Crau (France) and Gobabeb (Namibia) were involved in the validation. The performance of the processors was in general consistent across all bands for both sensors and with low standard deviations (<0.04) between on-site and estimated surface reflectance. Overall, our study provides a good insight of AC algorithms' performance to developers and users, pointing out similarities and differences for AOD, WV and SR retrievals. Such validation though still lacks of ground-based measurements of known uncertainty to better assess and characterize the uncertainties in SR retrievals. • The performance of 12 processors is investigated for Sentinel-2 and Landsat 8 data. • Aerosol Optical Depth retrievals' errors for Sentinel-2 and Landsat 8 were 0.1–0.2. • Water vapour retrievals' errors for Sentinel-2 were 0.2–0.4 g/cm2. • Low standard deviations (<0.04) between on-site and estimated surface reflectance. • Lower surface reflectance uncertainties for SWIR and NIR than visible bands. • Surface reflectance uncertainties < 0.01 for the best performing processors. [ABSTRACT FROM AUTHOR]

Abstract The Harmonized Landsat and Sentinel-2 (HLS) project is a NASA initiative aiming to produce a Virtual Constellation (VC) of surface reflectance (SR) data acquired by the Operational Land Imager (OLI) and Multi-Spectral Instrument (MSI) aboard Landsat 8 and Sentinel-2 remote sensing satellites, respectively. The HLS products are based on a set of algorithms to obtain seamless products from both sensors (OLI and MSI): atmospheric correction, cloud and cloud-shadow masking, spatial co-registration and common gridding, bidirectional reflectance distribution function normalization and spectral bandpass adjustment. Three products are derived from the HLS processing chain: (i) S10: full resolution MSI SR at 10 m, 20 m and 60 m spatial resolutions; (ii) S30: a 30 m MSI Nadir BRDF (Bidirectional Reflectance Distribution Function)-Adjusted Reflectance (NBAR); (iii) L30: a 30 m OLI NBAR. All three products are processed for every Level-1 input products from Landsat 8/OLI (L1T) and Sentinel-2/MSI (L1C). As of version 1.3, the HLS data set covers 10.35 million km2 and spans from first Landsat 8 data (2013); Sentinel-2 data spans from October 2015. The L30 and S30 show a good consistency with coarse spatial resolution products, in particular MODIS Collection 6 MCD09CMG products (overall deviations do not exceed 11%) that are used as a reference for quality assurance. The spatial co-registration of the HLS is improved compared to original Landsat 8 L1T and Sentinel-2A L1C products, for which misregistration issues between multi-temporal data are known. In particular, the resulting computed circular errors at 90% for the HLS product are 6.2 m and 18.8 m, for S10 and L30 products, respectively. The main known issue of the current data set remains the Sentinel-2 cloud mask with many cloud detection omissions. The cross-comparison with MODIS was used to flag products with most evident non-detected clouds. A time series outlier filtering approach is suggested to detect remaining clouds. Finally, several time series are presented to highlight the high potential of the HLS data set for crop monitoring. Highlights • HLS is a surface reflectance record from the Landsat/Sentinel-2 Virtual Constellation. • HLS products cover 10 million km2, and correct for spectral and angular sensors differences. • HLS v1.3 products are consistent with MODIS products with a deviation lower than 11%. • The HLS relative spatial co-registration is around 0.62 pixels. • Sentinel-2 cloud mask is not optimal and is the main HLS v1.3 known issue. [ABSTRACT FROM AUTHOR]

We present a method for simultaneously retrieving aerosol and surface parameters from ground-based and satellite observations collocated in space and time. We show that a combination of down and up-looking observations provides sufficient measurement constraints for characterizing both aerosol and surface properties with minimal assumptions. In order to employ this concept in AERONET processing, the standard inverse algorithm [Dubovik, O. & King, M. D. (2000), A flexible inversion algorithm for retrieval of aerosol optical properties from sun and sky radiance measurements. Journal of Geophysical Research, 105, 20673–20696] has been modified to retrieve surface reflectance in addition to aerosol parameters when co-incident satellite measurements are available. The method was applied to observations of smoke and desert dust over the Mongu (Zambia) and Solar Village (Saudi Arabia) AERONET sites respectively. The AERONET data were complemented by available observations from the MISR, MODIS, and POLDER-2 satellite sensors. The retrieved bidirectional reflectance factor (BRF) and surface albedo comparison shows good agreement between results obtained using observations from different satellites. The robustness of the method is tested by analyzing surface albedo time series retrieved during periods of high aerosol optical depth variability and low seasonal changes in surface reflectance. The analysis shows that the performance of retrieval algorithm is stable under different aerosol loadings. It is shown that much of the observed surface albedo temporal variability could be attributed mostly to the combined uncertainty in satellite radiometric calibration and aerosol vertical distribution for Mongu and to differences in satellite angular sampling on different days for Solar Village. The sensitivity of surface retrievals to assumptions on aerosol vertical distribution and aerosol particle shape are analyzed. It is found that the maximum error in retrieved surface albedo at 0. 44 μm is 0. 035 for aerosol optical depth 0. 85 at 0. 44 μm. For aerosol optical depths lower than ∼0. 7 the error in retrieved surface albedo is less than 0. 02. Analysis of particle shape assumptions on surface retrievals showed that aerosol particle non-sphericity significantly affects the angular shape of BRF, but not the surface albedo. Finally, the sensitivity of AERONET aerosol retrievals to uncertainty in assumed surface reflectance is analyzed by comparing aerosol retrievals obtained with different surface assumptions. It is found that the uncertainty in surface reflectance model employed in the version 1 AERONET operational algorithm is larger than was previously assumed in [Dubovik, O. , Smirnov, A. , Holben, B. N. , King, M. D. , Kaufman, Y. J. , Eck, T. F. , & Slutsker, I. (2000), Accuracy assessment of aerosol optical properties retrieved from AERONET sun and sky radiance measurements. Journal of Geophysical Research, 105, 9791–9806] and may have more significant effect on the retrieved aerosol properties than was documented in that work. In particular, larger errors were encountered for the real part of the refractive index (∼0. 05–0. 07 increase) and maximum of the particle size distribution (∼20% decrease) retrievals for the Mongu case, when the aerosol optical depth was relatively small (∼0. 4 at 0. 44 μm). The retrieved single scattering albedo uncertainties were within the error bars (0. 03) estimated in [Dubovik, O. , Smirnov, A. , Holben, B. N. , King, M. D. , Kaufman, Y. J. , Eck, T. F. , & Slutsker, I. (2000), Accuracy assessment of aerosol optical properties retrieved from AERONET sun and sky radiance measurements. Journal of Geophysical Research, 105, 9791–9806], with the exception of the 0. 44 μm retrievals for the desert dust case when they increased by ∼0. 09 and 0. 07 for low and high aerosol loadings respectively. [Copyright &y& Elsevier]