Aquatic reflectance derived from Sentinel‐2 Multispectral Imager data for inland waters in the conterminous United States

Conterminous United States demonstration and characterization of MODIS-based Landsat ETM + atmospheric correction

ABSTRACTLandsat satellites have the longest history of making global-scale Earth observations at medium spatial resolution of any series of satellites and have been widely used in various remote-sensing fields. However, many remote-sensing applications, including large-area or long-term land-cover monitoring, need Landsat reflectance data that have had accurate atmospheric correction carried out. In this research, a MODIS-based per-pixel atmospheric correction procedure was developed and employed to produce the surface reflectance (SR) product. A total of 510 Landsat-8 Operational Land Imager (OLI) scenes covering the whole of China in 2013 were collected and processed. The mean relative differences between the surface and top-of-atmosphere (TOA) reflectance for China, composited and expressed as percentages, were found to be 67, 47, 18, 13, 4, 4, and 7% for Landsat-8 OLI bands 1, 2, 3, 4, 5, 6, and 7, respectively. Then, the accuracy of MODIS atmospheric products was validated using ground-based sun-radiometer observation network data, including Sun/sky-radiometer Observation Network (SONET) and Aerosol Robotic Network (AERONET) data collected from 14 SONET/AERONET stations. The validation results showed that the MODIS atmospheric products are reliable for China, with an R2 value of 0.78 and a root mean square error (RMSE) value of 0.12 for aerosols, and an R2 value of 0.98 and an RMSE of 0.25 for water vapour. Third, the SR product using our per-pixel atmospheric correction method was evaluated by comparison with the MODIS daily surface reflectance product (MOD09GA) and the United States Geological Survey (USGS) provisional Landsat-8 SR product, with a mean R2 of 0.93 and an RMSE of 0.02 for MOD09GA; and with a mean R2 of 0.97 and an RMSE of 0.01 for the USGS SR product. Finally, the advantage of our per-pixel atmospheric correction method over the per-scene method was investigated by analysis of the spatial variation of the atmospheric parameters within one Landsat scene (about 1.51.5), with a mean standard deviation value of 0.03–0.09 for aerosol. When such aerosol variation was omitted as the per-scene atmospheric correction method, the SR absolute error due to aerosol optical thickness (AOT) spatial variation was about 0.027, 0.018, 0.005, 0.003, 0.002, 0.0007, and 0.003 for the seven reflectance bands of Landsat-8. Therefore, use of Landsat-8 SR products over China with our per-pixel atmospheric correction was proved reliable, and more promising than the per-scene method, especially for the short-wavelength bands.

The surface reflectance is an essential parameter for the quantitative applications using remote sensing satellite data; therefore, it is of great importance for the scientific community to produce standard surface reflectance products using an operational running algorithm and system. There have been various medium- to high-resolution satellites in China, yet there is still a lack of relevant surface reflectance products and systems. In this paper, high-resolution GF-1/GF-2 data from the year 2014 and 2017 were utilized for retrieval of surface reflectance products over land by using an operational atmospheric correction algorithm, adaptive to most multispectral satellites with visible and near-infrared bands (VNIR), namely, the VNIR approach. This method was based on the Second Simulation of a Satellite Signal in the Solar Spectrum, Vector (6SV) code and the look-up tables (LUTs). The surface reflectance products over land were validated against the ground-based atmospherically corrected reflectance over Beijing-Tianjin-Hebei regions and middle and lower regions of the Yangtze River in China. The preliminary validation results showed that the surface reflectance products agreed quiet well with the ground-based corrected reflectance, with the linear regression fitting coefficients being 1.09– 1.03, the correlation coefficients of R2 being 0.97–0.99, and the Root Mean Square Error (RMSE) being 0.01. Simultaneously, the mean reflectance normalized residuals between the surface reflectance products and the ground-based corrected reflectance were 19.7 %, 13.5 %, 8.7 %, and 6.6 %, respectively, indicating that the surface reflectance products over land derived from VNIR atmospheric correction approach had a good accuracy.

Synergistic applications of multi-resolution satellite data have been of a great interest among user communities for the development of an improved and more effective operational monitoring system of natural resources, including vegetation and soil. In this study, we conducted an inter-comparison of two remote sensing products, namely, visible/near-infrared surface reflectances and spectral vegetation indices (VIs), from the high resolution Advanced Thermal Emission and Reflection Radiometer (ASTER) (15 m) and lower resolution Moderate Resolution Imaging Spectroradiometer (MODIS) (250 m – 500 m) sensors onboard the Terra platform. Our analysis was aimed at understanding the degree of radiometric compatibility between the two sensors' products due to sensor spectral bandpasses and product generation algorithms. Multiple pairs of ASTER and MODIS standard surface reflectance products were obtained at randomly-selected, globally-distributed locations, from which two types of VIs were computed: the normalized difference vegetation index and the enhanced vegetation indices with and without a blue band. Our results showed that these surface reflectance products and the derived VIs compared well between the two sensors at a global scale, but subject to systematic differences, of which magnitudes varied among scene pairs. An independent assessment of the accuracy of ASTER and MODIS standard products, in which “in-house” surface reflectances were obtained using in situ Aeronet atmospheric data for comparison, suggested that the performance of the ASTER atmospheric correction algorithm may be variable, reducing overall quality of its standard reflectance product. Atmospheric aerosols, which were not corrected for in the ASTER algorithm, were found not to impact the quality of the derived reflectances. Further investigation is needed to identify the sources of inconsistent atmospheric correction results associated with the ASTER algorithm, including additional quality assessments of the ASTER and MODIS products with other atmospheric radiative transfer codes.

Advanced very high resolution radiometer (AVHRR) data provide the longest available time series of global satellite observations and have been extensively used. The Land Long-Term Data Record (LTDR) project has generated daily surface reflectance and normalized difference vegetation index (NDVI) products from AVHRR. However, residual cloud and aerosol contamination in the LTDR AVHRR surface reflectance and NDVI products significantly limits their applications and results in temporal and spatial inconsistencies in subsequent downstream products. Based on the LTDR AVHRR surface reflectance, a temporally continuous vegetation indices-based land-surface reflectance reconstruction (VIRR) method was refined in this study to generate Global LAnd Surface Satellite (GLASS) AVHRR NDVI and surface reflectance products from 1982 to 2015. The daily LTDR AVHRR surface reflectance data were first aggregated into eight-day intervals. The aggregated surface reflectance data were used to calculate NDVI, and a robust smoothing algorithm was used to reconstruct continuous and smooth NDVI upper envelopes, which were used to identify cloud-contaminated surface reflectance values. Then the surface reflectance time series was reconstructed from cloud-free surface reflectance values by incorporating the upper envelopes of the NDVI time series as constraints. The results show that the refined VIRR method successfully removes NDVI and surface reflectance values contaminated by clouds and can reconstruct temporally continuous NDVI and land-surface reflectance time series. Comparison of the GLASS AVHRR NDVI product with the third-generation Global Inventory Monitoring and Modeling System (GIMMS3g) and the moderate resolution imaging spectroradiometer (MODIS) NDVI products indicates that these NDVI products exhibit similar spatial patterns, but the GIMMS3g NDVI values were clearly higher than the GLASS AVHRR and MODIS NDVI values in tropical forest regions and the 50°N−60°N latitude band, particularly in July. Comparisons with the MODIS NDVI values over the BELMANIP (Benchmark Land Multisite Analysis and Intercomparison of Products) sites demonstrate that the GLASS AVHRR NDVI product provides better performance (RMSE = 0.1007 and Bias = 0.0518) than the GIMMS3g NDVI product (RMSE = 0.1288 and Bias = 0.0852). The temporal profiles of all these NDVI products exhibited consistent seasonal variations, but the temporal smoothness of the GLASS AVHRR NDVI product was superior to that of the GIMMS3g and MODIS NDVI products. The GLASS AVHRR and GIMMS3g NDVI products show consistent trends in most situations, but the trends of the GLASS AVHRR NDVI product were slightly more pronounced than those of the GIMMS3g NDVI product for each biome type. Comparison of the GLASS AVHRR surface reflectance product with MODIS surface reflectance product indicates the GLASS AVHRR and MODIS surface reflectance showed similar seasonal and interannual variations and the GLASS AVHRR surface reflectance was in good agreement with the MODIS surface reflectance, especially in the red band.

The Landsat time-series dataset is one of the most widely used datasets for land surface research due to its long time-series and Land Surface Reflectance (LSR) product. Though the United States Geological Survey (USGS) provides Landsat LSR products for later Landsat 4–5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and Landsat 8 Operational Land Imager (OLI), no early Landsat 1–5 Multispectral Scanner System (MSS) LSR product is generated currently, limiting the research traced back to the 1970s. Atmospheric correction is one of the necessary preprocesses for generating LSR products. However, it is challenging for MSS images, not only because the image quality is lower and bands are different compared with the current sensors, but also because of the multiple effects of other preprocesses, such as radiometric calibration. Based on the Second Simulation of a Satellite Signal in the Solar Spectrum Vector (6SV) model, we propose a novel framework for generating Landsat 1–5 MSS LSR data of China. Ground-based visibility records are introduced to replace the images-based aerosol optical depth (AOD) to effectively generate MSS LSR data of the 1970s. We evaluate the generated MSS LSR data by the cross-validation of the simultaneous observation of MSS and TM sensors in Landsat 4 and Landsat 5 using Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) surface reflectance product as the truth value. The evaluation result shows that the generated MSS LSR data is comparable with the later Landsat TM LSR product, with slightly larger uncertainties. In addition, it shows that the non-atmospheric factors (e.g., the difference of relative spectral responses of TM and MSS, the georegistration errors, the radiometric calibration uncertainty, and image noises) bring larger uncertainties than the atmospheric factors (e.g., the AOD retrieval method by visibility) to the cross-validation results. We apply the MSS LSR data generated by the proposed framework on time series analysis in the regions of interest (ROIs) of the spectral-stable land cover in China for all the MSS sensors. The application demonstrates the potential and promise of the MSS LSR data generated by the proposed framework.

Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product.

Land-surface reflectance, estimated from satellite observations through atmospheric corrections, is an essential parameter for further retrieval of various high level land-surface parameters, such as leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), and surface albedo. Although great efforts have been made, land-surface reflectance products still contain considerable noise caused by, e.g., cloud or mixed-cloud pixels, which results in temporal and spatial inconsistencies in subsequent downstream products. In this study, a new method is developed to remove the residual clouds in the Moderate Resolution Imaging Spectroradiometer (MODIS) land-surface reflectance product and reconstruct time series of surface reflectance for the red, near infrared (NIR), and shortwave infrared (SWIR) bands. A smoothing method is introduced to calculate upper envelopes of vegetation indices (VIs) from the surface reflectance data and the cloud contaminated reflectance data are identified using the time series VIs and the upper envelopes of the time series VIs. Surface reflectance was then reconstructed according to cloud-free surface reflectance by incorporating the upper envelopes of the time series VIs as constraint conditions. The method was applied to reconstruct time series of surface reflectance from MODIS/TERRA surface reflectance product (MOD09A1). Temporal consistency analysis indicates that the new method can reconstruct temporally-continuous time series of land-surface reflectance. Comparisons with cloud-free MODIS/AQUA surface reflectance product (MYD09A1) over the BELMANIP (Benchmark Land Multisite Analysis and Intercomparison of Products) sites in 2003 demonstrate that the new method provides better performance for the red band (R2 = 0.8606 and RMSE = 0.0366) and NIR band (R2 = 0.6934 and RMSE = 0.0519), than the time series cloud detection (TSCD) algorithm (R2 = 0.5811 and RMSE = 0.0649; and R2 = 0.5005 and RMSE = 0.0675, respectively).

Evaluation of atmospheric correction methods for low to high resolutions satellite remote sensing data

Remote sensing of tropical ecosystems: Atmospheric correction and cloud masking matter

Lombok southern sea has a high marine productivity which signifies fertility of a body water. Chl-a is one of the factors associated with fertility in the Lombok southern sea. Remote sensing can be used for mapping the distribution of Chl-a more efficient and accurate to extract the physical parameters of the water. Physical parameters accuracy is derived from remote sensing data depending on atmospheric correction algorithms and algorithms model to calculate the concentration of Chl-a.In this study, Landsat 8 was used to validate the existing estimation concentration algorithm of Chl-a by in-situ data collected in Lombok southern sea. Atmospheric corrected reflectance by 6SV and Flaash, as well as surface reflectance product from USGS were used as input of that algorithm. The algorithm with 6SV-reflectance produced highest accuracy with NMAE of 26.095%.Instead of using existing algorithm, a new algorithm following local characteristics of Lombok southern sea was developed. The developed algorithm based on log Rrs(λ4) and log (Rrs (λ5)) produced high correlation (R2 = 0.551). Chl-a concentration estimation from Landsat 8 data, through atmospheric correction of 6SV produced NMAEof 13.484%.

The Sentinel-2 mission is dedicated to land monitoring, emergency management and security. It serves for monitoring of land-cover change and biophysical variables related to agriculture and forestry. The mission is also used to monitor coastal and inland waters and is useful for risk and disaster mapping. The Sentinel-2 mission is fully operating since June 2017 with a constellation of two polar orbiting satellite units. Both Sentinel-2A and Sentinel-2B are equipped with an optical imaging sensor MSI (Multi-Spectral Instrument) which acquires optical data products with spatial resolution up to 10 m. Accurate atmospheric correction of satellite observations is a precondition for the development and delivery of high quality applications. Therefore the atmospheric correction processor Sen2Cor was developed with the objective of delivering land surface reflectance products. Sen2Cor is designed to process monotemporal single tile Level-1C products, providing Level-2A surface (Bottom-of-Atmosphere) reflectance product together with Aerosol Optical Thickness (AOT), Water Vapour (WV) estimation maps and a Scene Classification (SCL) map for further processing. The paper will give an overview of the Level-2A product content and up-to-date information about the data quality of the Level-2A products generated with Sen2Cor 2.8 in terms of Cloud Screening and Atmospheric Correction. In addition the paper gives an outlook on the next updates of Sen2Cor and their impact on Level-2A Data Quality.

This study presents the first systematic comparison of MAIAC Collection 6 MCD19A1 daily surface reflectance (SR) product with standard MODIS SR (MOD/MYD09). The study was limited to four tiles located in mid-Atlantic United States (H11V05), Canada (H12V03), central Amazon (H11V09), and North-Eastern China (H27V05) and used over 5000 MODIS granules in 2018. Overall, there is a remarkable agreement between the best quality pixels of the two products, in particular in the Red and NIR bands. Over selected tiles, the evaluation found that MAIAC provides from 4 to 25% more high-quality retrievals than MOD09 annually, with the largest difference in tropical regions, confirming results of the previous studies. The comparison of spectral characteristics showed a systematic MAIAC-MOD09 difference increasing from NIR to Blue, typical of biases of a Lambertian assumption in MOD09 algorithm. Over the North-Eastern China, MCD19A1 SR is found more stable at wide range of aerosol optical depth (AOD) variations, whereas MOD09 SR shows a consistent positive bias increasing with AOD and at shorter wavelengths. The observed SR differences can be attributed to differences in cloud detection, aerosol retrieval and in atmospheric correction which is performed using an accurate BRDF-coupled radiative transfer model in MAIAC and a Lambertian surface model in MOD09. While this study is not representative of the global performance because of its limited geographical coverage, it should help the land community to better understand the differences between the two products.

The growing development of medium- to high-resolution satellites in China has led to a considerable increase in the quantitative applications using the data; therefore, it is important to produce standard surface reflectance (SR) products operationally from such data. However, there is still a lack of relevant SR systems and SR products. We applied two atmospheric correction algorithms, adaptive to most multispectral satellites with visible and near-infrared (VNIR) bands, to HJ-1A/B charge-coupled device (CCD) instrument data, namely, the VNIR method and the MODIS-based method, with both methods being based on the Second Simulation of the Satellite Signal in the Solar Spectrum, Vector (6SV) code, and the look-up tables. We evaluated the accuracy of the SR by these two approaches for HJ-1A/B CCD images compared with the AErosol RObotic NETwork (AERONET) corrected reflectance for the period July 2011 to June 2012 over China and surrounding regions. We assessed more than 12 million pixels for the 49 spatial circular subsets, with a radius of 5 km, centered at 12 AERONET sites. The evaluation results indicated that both methods were suitable for operational flow, and that the MODIS-based method had better accuracy than the VNIR method, except for the near-infrared band. This conclusion was also validated by comparison with the normalized difference vegetation index products derived from the MODIS-based SR, VNIR SR, and AERONET SR. Additionally, the MODIS-based method showed superior accuracy when the overpass time of HJ-1A/B was more approximate to that of Terra MODIS.

A new method for atmospheric correction of high-resolution patches over heterogeneous terrain is presented. This highly efficient method allows for the correction of high-resolution surface pressure variations of a patch surrounding an Aeronet site in heterogeneous terrain. The method efficiency stems from the smoothness of the functions used in the atmospheric correction with surface pressure. The smoothness of these functions is exploited to decouple the high-resolution variation of elevation/pressure in the atmospheric correction process, resulting in very few radiative transfer code evaluations independent of the number of high-resolution pixels in the patch. The method allows pressure correction at every point of a high-resolution scene, decreasing the errors in heterogeneous terrains by up to two orders of magnitude for the visible bands and vegetation indices NDVI and EVI. This technique can be applied for calibration and validation of surface reflectance and vegetation index products to provide much greater volume of data for performance evaluation.