Analysis of leaf area index products from combination of MODIS Terra and Aqua data

A prototype product suite, containing the Terra 8-day, Aqua 8-day, Terra-Aqua combined 8- and 4-day products, was generated as part of testing for the next version (Collection 5) of the MODerate resolution Imaging Spectroradiometer (MODIS) leaf area index (LAI) products. These products were analyzed for consistency between Terra and Aqua retrievals over the following data subsets in North America: single 8-day composite over the whole continent and annual time series over three selected MODIS tiles (1200 × 1200 km). The potential for combining retrievals from the two sensors to derive improved products by reducing the impact of environmental conditions and temporal compositing period was also explored. The results suggest no significant discrepancies between large area (from continent to MODIS tile) averages of the Terra and Aqua 8-day LAI and surface reflectances products. The differences over smaller regions, however, can be large due to the random nature of residual atmospheric effects. High quality retrievals from the radiative transfer based algorithm can be expected in 90-95% of the pixels with mostly herbaceous cover and about 50-75% of the pixels with woody vegetation during the growing season. The quality of retrievals during the growing season is mostly restricted by aerosol contamination of the MODIS data. The Terra-Aqua combined 8-day product helps to minimize this effect and increases the number of high quality retrievals by 10-20% over woody vegetation. The combined 8-day product does not improve the number of high quality retrievals during the winter period because the extent of snow contamination of Terra and Aqua observations is similar. Likewise, cloud contamination in the single-sensor and combined products is also similar. The LAI magnitudes, seasonal profiles and retrieval quality in the combined 4-day product are comparable to those in the single-sensor 8-day products. Thus, the combined 4-day product doubles the temporal resolution of the seasonal cycle, which facilitates phenology monitoring in application studies during vegetation transition periods. Both Terra and Aqua LAI products show anomalous seasonality in boreal needle leaf forests, due to limitations of the radiative transfer algorithm to model seasonal variations of MODIS surface reflectance data with respect to solar zenith angle. Finally, this study suggests that further improvement of the MODIS LAI products is mainly restricted by the accuracy of the MODIS observations.     

Land cover in East Asia classified using Terra MODIS and DMSP OLS products

Land cover is classified over East Asia using 250‐m Moderate Resolution Imaging Spectroradiometer (MODIS) land surface reflectance, MODIS snow cover and Operational Linescan System (OLS) human settlement data. The classification method includes a decision tree classification scheme that considers 11 kinds of land surface features derived from the OLS product and the time series of two MODIS products in 2000. The decision tree was defined manually based on the experiment because of insufficient training data, ease of tuning by visual interpretation, and extensibility to further research. The resulting classification is compared to three kinds of reference data, i.e. MODIS land cover product, Chinese digital land cover map, and Chinese census. The land cover classification can be input into a hydrological model applied to the Yellow River in China.     

Automatic sea fog detection over Chinese adjacent oceans using Terra/MODIS data

Sea fog is a problematic weather phenomenon for marine transportation and navigation. Lacking ground observations, sea fog monitoring mainly depends on meteorological and environmental satellites because they provide large swaths of data with good spatial and temporal resolution. The Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Terra and Aqua satellites provides several new features for sea fog detection because of the more available channels.

In addition to the traditional variable-dual channel difference (DCD_IR), which is widely used to detect sea fog and stratus clouds at night, this study uses and analyses several other variables including NDSI (normalized difference snow index), BTD_back (brightness temperature difference in the thermal infrared channel between a sea fog/stratus cloud pixel and nearby clear-sky ocean surface), NWVI (normalized difference near-infrared water vapour index) and D_NWVI (NWVI difference between a possible sea fog/stratus cloud pixel and nearby clear-sky ocean surface), for all seasons. BTD_back, NWVI, and D_NWVI show outstanding ability to discriminate between sea fog and stratus clouds. Automatic sea fog detection algorithms are developed using these variables for both daytime and night time with Terra/MODIS data based on a threshold scheme. During development of the algorithms, a series of data processes are also considered to maintain stable performance of the algorithms over wide areas and in all seasons.

The algorithms are applied to Terra/MODIS data at a semi-operational mode from 2007 to 2013 and show promising results. Validation with data from field campaigns, one buoy station with good maintenance, 18 weather stations, and CALIOP (Cloud Aerosol Lidar with Orthogonal Polarization)/CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) demonstrates the accuracy of the algorithms. The sea fog detection results are highly consistent with fog observations from the field campaign data. The validation with the buoy station data shows an overall accuracy of 90% under all weather conditions, an accuracy of 86% during foggy weather condition, and a KSS (Hanssen–Kuiper Skill Score) of 0.81. The validation with two-year data from 18 weather stations and CALIOP/CALIPSO over the Bohai Sea and Yellow Sea shows accuracy of 76.3% and 77.9%, respectively. The promising results indicate high probability of applying the algorithms in operational systems over the oceans adjacent to China or even wider oceans using Terra/MODIS data.     

基于Terra/MODIS数据的HJ-1B/CCD1交叉定标方法研究

交叉辐射定标是国际上新近发展起来的一种无场地定标方法,它的应用弥补了场地定标成本较高、定标参数更新周期较长的不足。对于我国2008年发射的环境与灾害监测预报小卫星CCD数据而言,探索交叉辐射定标方法的适用性,对及时发现传感器辐射性能的变化,促进CCD遥感数据的定量化应用具有重要意义。本研究以辐射定标精度较高的Terra/MODIS数据为参考,分别使用光线匹配法（RM）和辐射传输模型方法（RTM）对HJ-1B/CCD1数据进行交叉辐射定标,并与相同条件下进行的场地定标结果比较。实验结果表明,使用这两种方法获取的CCD1的第2、3、4波段的定标结果与场地定标结果差异较小,只有第1波段定标结果与场地定标结果差异相对较大,这证明了交叉辐射定标方法的有效性。另外,虽然RTM方法考虑了参考传感器和待定标传感器光谱响应和观测几何的差异,但是由于RTM方法会受到所使用的6S模型本身的误差以及输入的大气参数、地表参数测量误差的影响,该方法并不总是优于RM方法。     

Combining vegetation index and model inversion methods for the extraction of key vegetation biophysical parameters using Terra and Aqua MODIS reflectance data

Accurate estimates of vegetation biophysical variables are valuable as input to models describing the exchange of carbon dioxide and energy between the land surface and the atmosphere and important for a wide range of applications related to vegetation monitoring, weather prediction, and climate change. The present study explores the benefits of combining vegetation index and physically based approaches for the spatial and temporal mapping of green leaf area index (LAI), total chlorophyll content (TC_ab), and total vegetation water content (VWC). A numerical optimization method was employed for the inversion of a canopy reflectance model using Terra and Aqua MODIS multi-spectral, multi-temporal, and multi-angle reflectance observations to aid the determination of vegetation-specific physiological and structural canopy parameters. Land cover and site-specific inversion modeling was applied to a restricted number of pixels to build multiple species- and environmentally dependent formulations relating the three biophysical properties of interest to a number of selected simpler spectral vegetation indices (VI). While inversions generally are computationally slow, the coupling with the simple and computationally efficient VI approach makes the combined retrieval scheme for LAI, TC_ab, and VWC suitable for large-scale mapping operations. In order to facilitate application of the canopy reflectance model to heterogeneous forested areas, a simple correction scheme was elaborated, which was found to improve forest LAI predictions significantly and also provided more realistic values of leaf chlorophyll contents.The inversion scheme was designed to enable biophysical parameter retrievals for land cover classes characterized by contrasting canopy architectures, leaf inclination angles, and leaf biochemical constituents without utilizing calibration measurements. Preliminary LAI validation results for the Island of Zealand, Denmark (57°N, 12°E) provided confidence in the approach with root mean square (RMS) deviations between estimates and in-situ measurements of 0.62, 0.46, and 0.63 for barley, wheat, and deciduous forest sites, respectively. Despite the independence on site-specific in-situ measurements, the RMS deviations of the automated approach are in the same range as those established in other studies employing field-based empirical calibration.Being completely automated and image-based and independent on extensive and impractical surface measurements, the retrieval scheme has potential for operational use and can quite easily be implemented for other regions. More validation studies are needed to evaluate the usefulness and limitations of the approach for other environments and species compositions.     

Validation of collection of 6 MODIS/Terra and MODIS/Aqua gross primary production in an alpine meadow of the Northern Tibetan Plateau

Moderate Resolution Imaging Spectroradiometer (MODIS) continuously monitors gross primary production (GPP), which is an extremely important component of carbon cycling, at the global scale. Uncertainties about MODIS GPP limit our ability to accurately quantify GPP at the regional scales. The Collection 6 MODIS/Terra and MODIS/Aqua GPP products (i.e. MOD17A2H and MYD17A2H) were compared with the estimated GPP (GPP_EC) by eddy covariance measurements in an alpine meadow in the Northern Tibetan Plateau during three consecutive growing seasons of 2005–2007. The Collection 6 MODIS/Terra and MODIS/Aqua fractional photosynthetically active radiation (FPAR) products (i.e. MOD15A2H and MYD15A2H) were also validated. The MOD17A2H and MYD17A2H products tended to overestimate GPP_EC by 2.17% and 7.35% in 2005–2007, respectively, although these differences were not significant. The MOD15A2H and MYD15A2H products also tended to overestimate ground-based FPAR (FPAR_G) by 20.31% and 24.73% in 2005–2007, respectively. The overestimation of FPAR resulted in about 17.51–23.97% overestimation of GPP_EC. The default maximum light-use efficiency (ε_max) of 0.86 g C MJ^?1 only underestimated the ground-based ε_max (0.88 g C MJ^?1) by 2.27%, which in turn resulted in about 2.13–2.72% underestimation of GPP_EC. The meteorology data errors only caused about 0.48–1.06% underestimation of GPP_EC. Therefore, although MODIS Collection 6 GPP had a very high accuracy, the input parameters had relative greater errors in the alpine meadow of the Northern Tibetan Plateau. The differences between MODIS GPP and GPP_EC mainly resulted from FPAR, followed by ε_max and meteorological data.     

东北-内蒙古地区基于MODIS单、双卫星积雪数据及常规积雪观测结合的积雪日数研究

结合Terra和Aqua卫星的积雪产品,获取2001~2008年东北-内蒙古地区逐年积雪日数分布,并利用此数据对比Terra卫星积雪数据获取的逐年积雪日数。结果表明随海拔的升高,双星与单颗卫星积雪日数差异呈现明显增加的趋势。整个东北-内蒙古地区双星积雪日数平均高出单颗卫星积雪日15 d,但与台站积雪日数对比发现,双星积雪日数平均仍然偏低27 d。这说明,利用Terra和Aqua双卫星积雪监测数据能明显改善山区云对遥感监测的影响,同时也可以减少降雪初期和消融期由于积雪消融较快带来的积雪漏测,但不足以消除云等因素的影响。考虑到获取的2001~2006年台站年积雪日数与MODIS年积雪日数与有良好的统计关系,利用两者建立的线性统计关系修正整个东北-内蒙古地区的MODIS积雪日数,能够很好地消除云等因素带来的MODIS双卫星积雪日数偏小的问题,修正后台站与双星积雪日数之间的绝对误差由原来的27 d减小到18 d。     

An evaluation of satellite dust-detection algorithms in the Middle East region

In the last 15 years, the frequency, spatial extent, and intensity of dust storms have increased and it is one of the main continuously occurring environmental hazard in the Middle East region. Since dust storms generally cover a large spatial extent and are highly dynamic, satellite Earth Observation (EO) is a key tool for detecting their occurrence, identifying their origin, and monitoring their transport and state. A variety of satellite dust detection algorithms have been developed to identify dust emissions sources and dust plumes once entrained in the atmosphere. This paper evaluates the performance of five widely applied dust detection algorithms: the Brightness Temperature Difference (BTD), D-parameter, Normalized Difference Dust Index (NDDI), Thermal-Infrared Dust Index (TDI) and the Middle East Dust Index (MEDI). These algorithms are applied to Moderate Resolution Imaging Spectroradiometer (MODIS) data to detect dust-contaminated pixels during three significant dust events in 2007 in the Middle East region that originated from sources in Iraq, Syria and Saudi Arabia. The results indicate that all methods have a comparable performance in detecting dust-contaminated pixels during the three dust events with an average detection rate (between all algorithms) of 85%. However, substantial differences exist in their ability to distinguish dust from clouds and the land surface, which resulted in large errors of commission. Direct validation of these algorithms with observations from seven Aerosol Robotic Network (AERONET) stations in the region found an average false detection rate (between all algorithms) of 89.6%. Although the algorithms performed well in detecting the dust-contaminated pixels their high false detection rate means it is challenging to apply these algorithms in operational context.     

Development of a new dust index NDLI for Asian dust extraction system based on Aqua MODIS data and monitoring of trans-boundary Asian dust events in Japan

Heavy Asian dust events occur due to the strong wind in the Gobi deserts and are occasionally carried to Korea, Japan, and North America. They cause problems in human lives, such as respiratory diseases, transportation disturbances due to reduce visibility, and other disruptions in social activities. Remote sensing technology is useful for detecting and monitoring such airborne dust and understanding the distributions and movements of dust. To understand the Asian dust events, in this study, a new dust index is developed for the efficient detection of airborne Asian dust, which is a composite of two Moderate Resolution Imaging Spectroradiometer (MODIS) indices: Brightness Temperature Difference (BTD) and Normalized Difference Dust Index (NDDI). Our proposed Normalized Dust Layer Index (NDLI) detects dust more efficiently. To identify the characteristics of annual Asian dust events in Japan, a statistical time-series analysis of data from the years 2010, 2013 and 2014 is performed, and it is found that the dust events in 2014 were relatively calmer than those in 2013. An evaluation that was based on ground observations over different sites in Japan indicated that the proposed method performed well. Finally, we integrated our NDLI product into the trans-boundary air pollution satellite image database (TAPSIDB) system for monitoring Asian dust events.     

Validation of GOES and MODIS active fire detection products using ASTER and ETM+ data

In this study we implemented a comprehensive analysis to validate the MODIS and GOES satellite active fire detection products (MOD14 and WFABBA, respectively) and characterize their major sources of omission and commission errors which have important implications for a large community of fire data users. Our analyses were primarily based on the use of 30 m resolution ASTER and ETM+ imagery as our validation data. We found that at the 50% true positive detection probability mark, WFABBA requires four times more active fire area than is necessary for MOD14 to achieve the same probability of detection, despite the 16× factor separating the nominal spatial resolutions of the two products. Approximately 75% and 95% of all fires sampled were omitted by the MOD14 and WFABBA instantaneous products, respectively; whereas an omission error of 38% was obtained for WFABBA when considering the 30-minute interval of the GOES data. Commission errors for MOD14 and WFABBA were found to be similar and highly dependent on the vegetation conditions of the areas imaged, with the larger commission errors (approximately 35%) estimated over regions of active deforestation. Nonetheless, the vast majority (> 80%) of the commission errors were indeed associated with recent burning activity where scars could be visually confirmed in the higher resolution data. Differences in thermal dynamics of vegetated and non-vegetated areas were found to produce a reduction of approximately 50% in the commission errors estimated towards the hours of maximum fire activity (i.e., early-afternoon hours) which coincided with the MODIS/Aqua overpass. Lastly, we demonstrate the potential use of temporal metrics applied to the mid-infrared bands of MODIS and GOES data to reduce the commission errors found with the validation analyses.     

MODIS satellite data evaluation for sand and dust storm monitoring in Saudi Arabia

ABSTRACT

The impacts of wind-blown desert sand and dust are a major concern of environmental and climate study due to their global extent. This article investigates the sand and dust storms detection in Saudi Arabia using Moderate Resolution Imaging Spectroradiometer (MODIS) data, both from Terra and Aqua satellite systems for the years 2002–2011. Normalized Difference Dust Index (NDDI) is applied for the detection of sand and dust storms whilst MODIS band 31 is applied to discriminate atmospheric sand and dust from that present on the ground. In addition, the data from Meteosat satellite, AERONET station, and meteorological stations are used to validate NDDI-based sand and dust storm events. The results of the study show that NDDI can successfully identify and differentiate sand and dust storms from clouds whilst MODIS band 31 can discriminate aerial and surface sand and dust over Saudi Arabia. The results also show that the multi-source data, that is MODIS, Meteosat, AERONET, and meteorological stations, can be very valuable for tracking sand and dust storm events. As no such attempt in the past has been made in Saudi Arabia, it is envisaged that the results of this study will be helpful in planning remote-sensing data for the climate change study in the region.     

An estimation of the optimum temporal resolution for monitoring vegetation condition on a nationwide scale using MODIS/Terra data

Monitoring vegetation condition is an important issue in the Mediterranean region, in terms of both securing food and preventing fires. The recent abundance of remotely sensed data, such as the daily availability of MODIS imagery, raises the issue of appropriate temporal sampling when monitoring vegetation: under‐sampling may not accurately describe the phenomenon under consideration, whilst over‐sampling would increase the cost of the project without additional benefit. The aim of this work is to estimate the optimum temporal resolution for vegetation monitoring on a nationwide scale using 250 m MODIS/Terra daily images and composites. Specific objectives include: (i) an investigation into the optimum temporal resolution for monitoring vegetation condition during the dry season on a nationwide scale using time‐series analysis of Normalized Difference Vegetation Index, NDVI, datasets, (ii) an investigation into whether this temporal resolution differs between the two major vegetation categories of natural and managed vegetation, and (iii) a quality assessment of multi‐temporal NDVI composites following the proposed optimum temporal resolution. A time‐series of daily NDVI data is developed for Greece using MODIS/Terra 250 m imagery. After smoothing to remove noise and cloud influence, it is subjected to temporal autocorrelation analysis, and its level of significance is the adopted objective function. In addition, NDVI composites are created at various temporal resolutions and compared using qualitative criteria. Results indicate that the proposed optimum temporal resolution is different for managed and natural vegetation. Finally, quality assessment of the multi‐temporal NDVI composites reveals that compositing at the proposed optimum temporal resolution could derive products that are useful for operational monitoring of vegetation.     

Evaluation of MODIS SWIR and NIR-SWIR atmospheric correction algorithms using SeaBASS data

Using the NASA maintained ocean optical and biological in situ data that were collected during 2002-2005, we have evaluated the performance of atmospheric correction algorithms for the ocean color products from the Moderate Resolution Imaging Spectroradiometer (MODIS) on Aqua. Specifically, algorithms using the MODIS shortwave infrared (SWIR) bands and an approach using the near-infrared (NIR) and SWIR combined method are evaluated, compared to the match-up results from the NASA standard algorithm (using the NIR bands). The in situ data for the match-up analyses were collected mostly from non-turbid ocean waters. It is critical to assess and understand the algorithm performance for deriving MODIS ocean color products, providing science and user communities with the important data quality information. Results show that, although the SWIR method for data processing has generally reduced the bias errors, the noise errors are increased due mainly to significantly lower sensor signal-noise ratio (SNR) values for the MODIS SWIR bands, as well as the increased uncertainties using the SWIR method for the atmospheric correction. This has further demonstrated that future ocean color satellite sensors will require significantly improved sensor SNR performance for the SWIR bands. The NIR-SWIR combined method, for which the non-turbid and turbid ocean waters are processed using the NIR and SWIR method, respectively, has been shown to produce improved ocean color products.     

An object-oriented daytime land-fog-detection approach based on the mean-shift and full lambda-schedule algorithms using EOS/MODIS data

A new algorithm is presented for land-fog detection using daytime imagery from the Earth Observing System (EOS) Moderate Resolution Imaging Spectroradiometer (MODIS) data. MODIS images constitute an ideal data source for fog detection due to their outstanding spatial and spectral resolution. In this article, a parameter named the Normalized Difference Fog Index (NDFI) is proposed, based on analysing the spectral character of fog and cloud by utilizing the Streamer radiative-transfer model and MODIS data. A mean-shift segmentation method is used to preliminary segment the NDFI image, and a full lambda-schedule algorithm is then iteratively applied to merge adjacent segments based on the combination of spectral and spatial information. Then, some properties (e.g. mean value of brightness temperature) are calculated for each segment, and each object is identified as either fog or not. The algorithm's performance is evaluated against ground-based measurements over China in winter, and the algorithm is proved to be effective in detecting fog accurately based on three cases.     

An evaluation of MODIS and SeaWiFS bio-optical algorithms in the Baltic Sea

An extensive bio-optical data set from field measurements was used to evaluate the performance of standard Moderate Resolution Imaging Spectroradiometer (MODIS) and Sea-viewing Wide Field-of-view Sensor (SeaWiFS) ocean color (in-water) algorithms in the Baltic Sea, which represents an example of optically complex Case 2 waters with high concentration of colored dissolved organic matter (CDOM). The data set includes coincident measurements of radiometric quantities, chlorophyll a concentration (Chl a), and absorption coefficient of CDOM, which were taken on 25 cruises between 1993 and 2001. The data cover a wide range of variability with Chl a in surface waters from about 0.3 to 100 mg m⁻³. All the MODIS pigment algorithms examined as well as the SeaWiFS OC4v4 algorithm showed a systematic and large overestimation in chlorophyll retrievals. The mean systematic and random errors based on our entire data set exceeded 150% or even 200% in some cases, making these standard algorithms inadequate for pigment determinations in the Baltic. Although new parameterization of the standard pigment algorithms based on our field measurements in the Baltic resulted in a significant reduction of errors, the overall performance of such regionally tuned algorithms remained unsatisfactory. For example, the mean normalized bias (MNB) for the regionally tuned MODIS chlor_a_2 algorithm was reduced to 26% (from over 200% for the standard algorithm), but the root mean square (RMS) error was still large (>100%). The MODIS K_490 algorithm for estimating the diffuse attenuation coefficient of downwelling irradiance showed the best performance among all the algorithms examined. With the new coefficients based on our field data, the regional version of this algorithm showed an acceptable level of errors, MNB=4% and RMS=30%. In addition to the apparent problems of the standard in-water bio-optical algorithms, we found that the atmospheric correction currently in use for MODIS and SeaWiFS imagery usually fails to retrieve upwelling radiances emerging from the Baltic Sea. The match-up comparisons of the coincident in situ and satellite determinations of normalized water-leaving radiances showed generally poor agreement, especially in the blue spectral region. It appears that new approaches for ocean color algorithms are required in the Baltic Sea.     

Comparison of algorithms and classifiers for stride detection using wearables

Neural Computing and Applications - Sensor-based systems for diagnosis or therapy support of motor dysfunctions need methodologies of automatically stride detection from movement sequences. In this...     

Comparison of uncertainty quantification process using statistical and data mining algorithms

Uncertainty quantification has always been an important topic in model reduction and simulation of complex systems. In this aspect, global sensitivity anal     

Early detection system of drought in East Asia using NDVI from NOAA/AVHRR data

Abstract

The influence of surface bidirectional reflectance factors, including shadowing, and of atmospheric aerosol variability are modelled for their effects on the remote sensing of desert targets from space in the 0·?μm region at high spatial resolution. The white sand reflectance data of Salomonson are used as the basis for the simulation. The effects of the surface bi-directional reflectance and atmospheric aerosol on the nadir-normalized reflectance measured at the satellite are discussed individually and jointly. The net influence of these two factors is shown to depend on the magnitude of other parameters, such as the surface reflectance and solar zenith angle.     

A new area-specific bio-optical algorithm for the Bay of Biscay and assessment of its potential for SeaWiFS and MODIS/Aqua data merging

Based on a feed-forward and error-back-propagated neural network (NN), a new bio-optical algorithm is developed for the Bay of Biscay. It is designed as a set of NNs individually dedicated to the retrieval of the phytoplankton chlorophyll (chl), and total suspended matter (tsm) from Sea-viewing Wide Field-of-View Sensor (SeaWiFS) and Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua data. The retrieved versus in situ measured concentrations of chl and tsm correlation coefficients for chl proved to be ～0.8 (SeaWiFS) and 0.72 (MODIS), and for tsm 0.71 (SeaWiFS) and 0.74 (MODIS). The developed NN-based bio-optical algorithms are employed to assess the compatibility of SeaWiFS and MODIS data on chl and tsm in the coastal zone of the Bay of Biscay (case 2 waters). The value of the ratio between the concentration of chl and tsm derived from the same-day SeaWiFS and MODIS data (the overflight time difference, Δt is ≤2.5 hours) has in most cases values of approximately 1, however, in specific cases it varies appreciably. These results indicate that, unlike the reportedly very successful cases of merging of SeaWiFS and MODIS data on chl in open ocean waters (case 1 waters), the merging of chl (and tsm) data from these sensors collected over case 2 waters needs to be supervised at a level of a few pixels. At the same time, when averaged over the entire coastal zone of the Bay of Biscay, the retrieved monthly mean chl and tsm concentrations from SeaWiFS and MODIS practically coincide throughout the years (2002–2004) of contemporaneous operation of these two satellite sensors. Thus, even in the case of such dynamic and optically complex case 2 waters that are inherent in the Bay of Biscay, the potentials for ocean colour data merging are very good. The merging efficiency is assessed and illustrated via documenting the spatio-temporal dynamics of bottom sediment re-suspension in the bay occurring in winter – the season of heaviest cloudiness over the bay.     

Radiometric calibration evaluation for RSBs of Suomi-NPP/VIIRS and Aqua/MODIS based on the 2015 Dunhuang Chinese Radiometric Calibration Site in situ measurements

The Dunhuang Chinese Radiometric Calibration Site (CRCS), used for the vicarious calibration (VC) of reflective solar bands (RSBs), was determined as the primary radiometric calibration site for Chinese space-borne optical sensors and was also selected in 2008 by the Working Group on Calibration and Validation of the Committee on Earth Observation Satellites as one of the instrumented reference sites. In August 2015, an in situ measurement was carried out at the Dunhuang site to evaluate the RSB radiometric calibration of the Visible Infrared Imaging Radiometer Suite (VIIRS) on Suomi National Polar-orbiting Partnership (NPP) and Moderate Resolution Imaging Spectroradiometer (MODIS) on Aqua based on the reflectance-based method. A portable spectroradiometer was used in the experiment to obtain the surface reflectance, and the atmospheric parameters were obtained by sun photometers and radiosonde. A Dunhuang surface bidirectional reflectance distribution function model obtained during the field missions in 2008 and 2013 was implemented. Two days of in situ measurement data including 2 days of VIIRS data and 1 day of MODIS data were used for this evaluation. The results show that the radiometric calibration accuracy is within ±2% for most NPP/VIIRS and Aqua/MODIS RSBs based on the Dunhuang site. It should be noted that there is a relatively large difference in the NPP/VIIRS day–night band (DNB) and Aqua/MODIS band 7 results at the central wavelength of 2.1 μm, with biases of – 4.78% and – 5.71%, respectively. One factor contributing to the difference is the atmospheric transmittance calculation in these bands using the 6S radiative transfer model. If Moderate Resolution Atmospheric Transmission model is used for atmospheric transmittance correction, part of the bias of the MODIS band 7 and VIIRS DNB can be eliminated. However, the consistency of the VIIRS M11 and MODIS B7 is 3.47%, which is larger than that of the other bands.