Cloud reflectance variations in channel-3

Abstract

Using photographic terminology for channel 3 pictures in sunshine, one notes that most ice clouds appear black and that cloud shadows are equally dark, but water droplet clouds appear in all shades. These shades also vary greatly with the direction of sunshine relative to the line of sight because scatter is almost entirely by diffraction. Droplets and ice crystals larger than about 10 fan absorb the incident radiation almost completely and it does not penetrate through clouds unless there exist plenty of unobstructed ray paths through the clouds. The reflection from a water surface is almost metallic in intensity so that glint completely saturates the radiometer. There is no evidence of comparable reflection from ice. All snow-covered surfaces, including sea ice, appear black. Stratus cloud shows large variations in reflectance depending on the state of the convection in it which brings very small droplets to the surface. Small particle size causes some contrails and orographic cirrus to appear white although most appear black; old cumulonimbus tops develop pale areas when gravitational settling leaves predominantly very small crystals at the top while still active areas remain black.     

A method for the construction of cloud trajectories from series of satellite images

Knowledge of the trajectories of atmospheric structures is useful in meteorology, in particular for the study of the persistence of clouds at mesoscale and for studies of the large-scale atmospheric circulation. For this purpose, a method for the construction of cloud trajectories has been developed and is presented in this article. This method extends traditional techniques used to calculate cloud motion vectors from satellite images, representing the wind at a single instant and the motion over a short time interval (typically ½h), to the measurement of the motion of the same clouds over a long duration, up to 60 h. Trajectories of clouds have been constructed from series of Meteosat images in the thermal IR, the WV (water vapour) and the VISible channels. Similarly, pure WV structures have been tracked in the WV channel. The duration of a trajectory is mainly related to the lifetime of the tracked structure, but also dependent on its spatial scale. A lesser image quality or a larger time interval between images reduces this duration. The use of severe quality tests enables reliable tracking of a limited number of clouds or WV structures, whereas more tolerant tests lead to larger groups of less precise trajectories nevertheless suitable for the study of large-scale motions.     

Use of NARX neural networks for Meteosat Second Generation SEVIRI very short-term cloud mask forecasting

In this article, a novel technique based on artificial neural networks (NN) is proposed for cloud coverage short-term forecasting (nowcasting). In particular, the capabilities of multi-layer perceptron NN and time series analysis with nonlinear autoregressive with exogenous input NN are explored and applied to the European meteorological system ‘Meteosat Second Generation’ with its payload Spinning Enhanced Visible and InfraRed Imager. The general neural architecture consists of a first stage addressing the prediction of the radiance images at six bands (0.6, 0.8, 1.6, 3.9, 6.2 and 10.8 μm). In a second stage a cloud masking algorithm, always based on NN, is applied to the predicted images for the cloud coverage nowcasting. The scheme was compared with the most basic forecast algorithm for the prediction: the persistent model. Two test areas characterized by different climatology have been considered for the performance analysis. The results show that about 85% of the changes occurring in the time window were recognized by the proposed technique.     

Trends of night-time hourly cloud-cover values over Manila Observatory: ground-based remote-sensing observations using a digital camera for 13 months

ABSTRACT

Night-time cloud detection provides data sets of cloud-cover percentage. Although night-time cloud-cover data sets from satellite-based instruments are common, these data sets do not have relatively high temporal resolution. To quantify local temporal cloud-cover variability and to attain long-term cloud-cover measurements, ground-based instruments would be the appropriate apparatus. In this study, a digital camera is used to continuously gather images of the night sky at 5-min intervals over Manila Observatory (14.64° N, 121.07° E). For the first time in Manila, ground-based remote-sensing data gathered from October 2015 to October 2016 are analysed for hourly cloud cover. The results indicate that wet season has relatively higher cloud-cover values (median >40%) as compared to the dry season (median <40%). Moreover, cloud-cover values are observed to decrease during the night. For the wet season, August having the highest cloud-cover values has the highest value of change of hourly cloud-cover percentage (?0.82% h^?1). For the dry season, February having the lowest cloud-cover values has the highest value of change of hourly cloud-cover percentage (?1.04% h^?1).     

On the applicability of the Heliosat-2 method to assess surface solar irradiance in the Intertropical Convergence Zone,French Guiana

Measurements of daily means of surface solar irradiance made at four ground stations in French Guiana are compared to estimates from the HelioClim-3 database produced by the Heliosat-2 method applied to Meteosat satellite images. The bias ranges from 12 W m^?2 (6% of the mean of measurements) to 23 W m^?2 (12%), depending on the stations. The root mean square difference ranges between 23 W m^?2 (11%) and 35 W m^?2 (18%). The correlation coefficient (r) is close to 0.9. Better results are observed during the rainy season than during the dry season. Uncertainties are mainly due to the presence of clouds, large viewing angles of the Meteosat satellite, and limitations of the atmospheric transmittance model under the tropical atmospheric conditions. It is concluded that the Heliosat-2 method provides new knowledge about solar radiation in French Guiana.     

Augmenting satellite precipitation estimation with lightning information

We have used lightning information to augment the precipitation estimation from remotely sensed imagery using an artificial neural network cloud classification system (PERSIANN-CCS). Co-located lightning data are used to segregate cloud patches, segmented from Geostationary Operational Environmental Satellite (GOES)-12 infrared (IR) data, into either electrified patches (ECPs) or nonelectrified patches (NECPs). A set of features is extracted separately for the ECPs and NECPs. Features for the ECPs include a new feature corresponding to the number of flashes that occur within a 15 minute window around the time of the nominal scan of the satellite IR images of the cloud patches. The cloud patches are classified and clustered using a self-organizing maps (SOM) neural network. Then, brightness temperature and rain rate (T–R) relationships are derived for different clusters. Rain rates are estimated for the cloud patches based on their representative (T–R) relationship. The equitable threat scores (ETS) of the daily and hourly precipitation estimates at a range of rain rate thresholds show that incorporating lightning information can improve categorical precipitation estimation in the winter and fall seasons. In the winter, the ETS improvement is almost 15% for the daily and 12% for the hourly rainfall estimates (at thresholds below 15 mm hour^?1). During the same period, there is also a drop in the false alarm ratio (FAR) and a corresponding increase in the probability of detection (POD) at most threshold levels. During the summer and spring seasons, no categorical significant improvements have been noted, except for the BIAS scores for the hourly rainfall estimates at higher thresholds (above 5 mm hour^?1) in the summer months. A quantitative evaluation in terms of the root mean squared error (RMSE) and correlation coefficient (CORR) shows that the incorporation of lightning data does improve rainfall estimation over all seasons with the most improvement (around 11–13% CORR improvement) occurring during the winter. We speculate that during the winter, more of the ice processes are packed into a thinner stratiform layer with lower cloud tops and freezing levels. Hence, more of the ice contributes to precipitation on the ground. We also expect that information from lightning, related to the ice microphysics processes, provides surrogate information about the rain rate.     

Cloud reflectivity profile classification using MSG/SEVIRI infrared multichannel and TRMM data

This work analyses the capability of utilizing cloud-top multispectral radiation to extract information about the vertical reflectivity profile of clouds. Reflectivity profiles and cloud type classification were collected using the Tropical Rainfall Measuring Mission (TRMM) 2A25 algorithm and brightness temperature multispectral channels (3.9, 6.2, 8.7, 10.8, and 12 μm) from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) aboard the Meteosat Second Generation (MSG) satellite. The analysis was performed on four cloud types: convective, warm, and stratiform with and without bright band, using a four-channel combination (10.8–3.9, 6.2–10.8, 8.7–10.8, and 10.8–12.0 μm). The study was applied over Tropical Africa at the MSG subsatellite point, in August 2006. Sixteen individual profile types were detected: three warm, four convective, three stratiform without bright band, and six stratiform with bright band. These cloud profile types were examined using cloud-top multichannel brightness temperature differences. The channel combination results demonstrated that the information obtained from cloud-top radiation enables us to detect specific individual characteristics within the cloud reflectivity profile. The channel combinations employed in this study were effective in identifying warm and cold cloud types. In the 10.8–3.9 and 8.7–10.8 μm channels, brightness temperature differences were indicated in the detection of warm clouds, while the 6.2–10.8 μm channel was noted to be very efficient in classifying cold clouds. Cold clouds types were much more difficult to classify because they possess a similar multichannel signature, which caused ambiguity in the classification. In order to reduce this uncertainty, it was necessary to use texture information (space variability) to acquire a clearer distinction between different cloud types. The survey analysis showed good performance in classifying cloud types, with an accuracy of about 77.4% and 73.5% for night and day, respectively.     

Generation of high-resolution East Antarctic landfast sea-ice maps from cloud-free MODIS satellite composite imagery

A method to generate high spatio-temporal resolution maps of landfast sea ice from cloud-free MODIS composite imagery is presented. Visible (summertime) and thermal infrared (wintertime) cloud-free 20-day MODIS composite images are used as the basis for these maps, augmented by AMSR-E ASI sea-ice concentration composite images (when MODIS composite image quality is insufficient). The success of this technique is dependent upon efficient cloud removal during the compositing process. Example wintertime maximum (~ 374,000 km²) and summertime minimum (~ 112,000 km²) fast-ice maps for the entire East Antarctic coast are presented. The summertime minimum map provides the first high-resolution indication of multi-year fast-ice extent, which may be used to help assess changes in Antarctic sea-ice volume. The 2σ errors in fast-ice extent are estimated to be ± 2.98% when ≥ 90% of the fast-ice pixels in a 20-day period are classified using the MODIS composite, or ± 8.76 otherwise (when augmenting AMSR-E or the previous/next MODIS composite image is used to classify > 10% of the fast ice). Imperfect composite image quality, caused by persistent cloud, inaccurate cloud masking or a highly dynamic fast-ice edge, was the biggest impediment to automating the fast-ice detection procedure.     

Cloud top temperature based precipitation intensity estimation using INSAT-1D data

Precipitation intensity associated with each cloud type is an important parameter as it suggests the respective contribution of different categories of clouds to rainfall. The present paper aims at demonstrating a technique of establishing a relationship between the various sub-zero Celsius temperature ranges of clouds with cold top temperatures and their precipitation intensities. Indian Satellite (INSAT-1D) infrared and visible band data were utilized for this purpose. A regression analysis was performed and various cases statistically analysed. The results indicated that nearly all clouds with their tops colder than 275K contribute to precipitation. On combining the results of precipitation intensity with cloud type, it has been found that during the monsoon season the contribution of different clouds to rainfall over an area decreases in the study area: the maximum being from cumulonimbus (1.75-1.49 mm h^-1), followed by nimbostratus (0.97- 0.86 mm h^-1) and altocumulus/altostratus (0.97-0.86 mm h^-1). The contributions from other cloud types, such as status, cumulus or stratocumulus, is insignificant for this study region during the period under observation. However, the precipitation estimates for other clouds like cirrus, cirrostratus and cirrocumulus are unpredictable from the scheme presented here due to several inherent limitations. The image processing technique of level slicing was also utilized to yield fast and reliable images depicting the regions of varying rainfall. Although the analysis has some limitations, it clearly illustrates the relationship between the cloud top temperature and precipitation intensities which may be utilized in actual practice.     

The effect of subpixel clouds on remote sensing

Abstract

The effect of subpixel clouds on remote sensing of the surface reflectivity and of the vegetation index was numerically simulated for a thin layer of rectangular clouds. The simulation is for a 4 × 4km² field of view (e.g. the low resolution NOAA-AVHRR images). A fixed cloud reflectivity was used as well as an empirical cloud model for eight cloud types. In the empirical models the cloud reflectivity varied with cloud fraction. A cloud fraction of 20 percent, in a pixel with surface reflectance of 005, can increase the apparent surface reflectance by 002 for a model of strato-cumulus and by 0.08 for a fixed cloud reflectance of R_cequals;0.5. By using measured cloud fraction probability distributions in several climatic regions (not including a tropical climate) it was found that the cloud effect can be eliminated if the best remote sensing case out of about four independent cases is chosen. This corresponds to 8-16 observation days. In order to estimate the cloud effect on remote sensing of a particular area it is necessary to measure the probability distribution of the cloud fraction and the dependence of the cloud reflectance on the cloud size. Subpixel clouds were shown to affect the variation of upward radiance across an image. Therefore the standard deviation of the radiances over a uniform area can be used to sense the presence of the residual cloud effect.     

Derivation of a threshold function for the Advanced Very High Resolution Radiometer 3.75 µm channel and its application in automatic cloud discrimination over snow/ice surfaces

The distinct contrast between the reflectance of solar radiation in Advanced Very High Resolution Radiometer (AVHRR) channel 3 (3.75?µm) by clouds and by bright surfaces provides an effective means of cloud discrimination over snow/ice surfaces. A threshold function for the top-of-atmosphere (TOA) albedo in channel 3 (r ₃) is derived and used to develop an improved method for cloud discrimination over snow/ice surfaces that makes explicit use of TOA r ₃. Corrections for radiance anisotropy and temperature effects are required to derive accurate values of r ₃ from satellite measurements and to utilize the threshold function. It has been used to retrieve cloud cover fractions from National Oceanic and Atmospheric Administration (NOAA)-14 AVHRR data over the Arctic Ocean and over the North Slope of Alaska (NSA) Atmospheric Radiation Measurement (ARM) site in Barrow, Alaska. The retrieved cloud fractions are in good agreement with SHEBA (Surface HEat Budget of the Arctic Ocean) surface visual observations and with NSA cloud radar and lidar observations, respectively. This method can be utilized to improve cloud discrimination over snow/ice surfaces for any satellite sensor with a channel near 3.7?µm.     

Cloud and cloud shadow masking for Sentinel-2 using multitemporal images in global area

ABSTRACT

Sentinel-2 data provided the opportunity for complementary data to existing missions including Landsat and SPOT. In this study, multitemporal cloud masking (MCM) used to detect cloud and cloud shadow masking for Landsat 8 was improved to detect cloud and cloud shadow for Sentinel-2 data. This improvement takes advantages of the spectral similarity between Landsat 8 and Sentinel-2. To assess the reliability of the new MCM algorithm, several data selected from different environments such as sub-tropical South, tropical, and sub-tropical North were evaluated. Moreover, these data have heterogeneous land cover and variety of cloud types. In visual assessment, the algorithm can detect cloud and cloud shadow accurately. In the statistical assessment, the user’s and producer’s accuracies of sample in sub-tropical environments of cloud masking was 99% and 96%, respectively, and cloud shadow masking was 99% and 98%, respectively. In addition, the user’s and producer’s accuracies of sample in tropical environments of cloud masking was 100% and 95%, respectively, and cloud shadow masking was 100% and 92%, respectively. Compared to Fmask, MCM has higher accuracies in most of the results of cloud and cloud shadow masking in both sub-tropical and tropical environments. The results showed that the improved-MCM algorithm can detect cloud and cloud shadow for Sentinel-2 data accurately in all scenarios and the accuracies are significantly high.     

An improved method to estimate reference cloud-free images for the visible band of geostationary satellites

Geostationary images have been used frequently in the past 50 years to derive geophysical information. As a complement to all-sky observations, clear-sky counterparts play an important role in the derivation of cloud properties. We investigated ways to improve estimates of top-of-atmosphere (TOA) visible clear-sky images, over the full spatial and temporal resolution of Meteosat First Generation (MFG) satellites. Estimation was based on TOA measurements in MFG’s visible channel, collected for a specific time of the day over the span of several days. In addition, a cloud climatology aided estimation.

Parameter optimization and the introduction of a spatial filter over ocean resulted in a bias of ?1.0 to ?2.0 digital counts (DC) and a root mean square error (RMSE) of 2.0–3.0 DC when averaged over the complete field of view. This excludes the Spring period which has up to ?3.5 DC bias and up to 5.5 DC RMSE. Reasons for these exceptional differences were found in rapid greenness change, affecting reflectances over vegetated surfaces, and dust storms, with an effect over tropical land and ocean surfaces. Similarly, sea ice and snow affected polar regions seasonally. Applied to 24 years of MFG imagery, we successfully used improved clear-sky estimates to stably detect clouds. Additionally, these clear-sky estimates may prove useful for characterization of instrument degradation as well as cloud feedback studies.     

Statistical characterization of the sea ice extent during different winter scenarios in the Gulf of Riga (Baltic Sea) using optical remote-sensing imagery

This study focuses on the statistical characterization of ice conditions (extent, sea ice occurrence probability (SIOP), and length of ice season) in the Gulf of Riga, Baltic Sea, using remote-sensing data. The optical remote-sensing data with 250 m resolution acquired by a Moderate Resolution Imaging Spectroradiometer (MODIS) during 2002–2011 were used for statistical characterization of sea ice. A method based on bimodal histogram analysis of remote-sensing reflectance data was developed to discriminate ice from water. In general, ice extent information obtained from MODIS data agrees with the official ice chart data (synthetic aperture radar (SAR) and in situ measurements) and multi-sensor product containing data from microwave and infrared instruments (R² >0.83). However, in case of severe winters and extremely mild winters there are differences in the dates when maximum ice extent is registered. MODIS data can be used for detailed analysis of ice extent in specific basins of Baltic Sea. Depending on the year, the ice season length in the Gulf of Riga ranged from 68 to 146 days, and the maximum ice extent varied greatly from 329 to 15,350 km². SIOP and number of ice days increased significantly in areas where the depth is less than 15 m. Based on negative-degree days and ice cover characteristics (SIOP and ice season length), three winter scenarios were defined: severe (2003, 2006, 2010, and 2011), medium (2004 and 2005), and mild (2007, 2008, and 2009).     

Synergism between NOAA-AVHRR and Meteosat data for studying vegetation development in semi-arid West Africa

Abstract

Rainfall estimates, based on cold cloud duration estimated from Meteosat data, are compared with vegetation development depicted by data of the normalized difference vegetation index (NDVI) from the National Oceanic and Atmospheric Administration's (NOAA) advanced very high resolution radiometer (AVHRR) for part of the Sahel. Decadal data from the 1985 and 1986 growing seasons are examined to determine the synergism of the datasets for rangeland monitoring. There is a general correspondence between the two datasets with a marked lag between rainfall and NDVI of between 10 and 20 days. This time lag is particularly noticeable at the beginning of the rainy season and in the more northern areas where rainfall is the limiting factor for growth. Principal component analysis was used to examine deviations from the general relationship between rainfall and the NDVI. Areas of low NDVI values for a given input of rainfall were identified: at a regional scale, they give an indication of areas of low production potential and possible degradation of ecosystems. This study demonstrates in a preliminary way the synergism of such datasets derived from satellite--borne sensors with coarse spatial resolution, which may provide new information for the improved management of the Sahelian grasslands.     

Remote sensing of cloud cover in the Arctic region from AVHRR data during the ARTIST experiment

In this paper we present a cloud detection algorithm developed for the Arctic region using Advanced Very High Resolution Radiometer (AVHRR) data. Our approach is a simplified version of the Ebert method to discriminate between clouds, ice and open water in the Arctic Sea. The algorithm is tuned to work on an AVHRR scene typical of the winter to spring transition period. The algorithm has been applied to 1 month (154 scenes) of NOAA-14 AVHRR images (from 16 March to 15 April 1998) covering the region of the Arctic Sea near the Svalbard Islands. The cloud detection results are analysed using various check procedures. The algorithm's pixel classification performance was verified by a satellite image expert. The misclassified pixels were digitalized on the image and counted by the expert in order to quantify the algorithm's accuracy. The cloud classification results are quite accurate: 70% of the images (109) have an error less than 5% and only 11% of the image results have an error greater than 10%. The method's performance is also tested against independent cloud and ice observations obtained, respectively, from the Ny-Ålesund meteorological base and from the Special Sensor Microwave/Imager (SSM/I) dataset. The comparison with these independent sources of data confirms the algorithm's good performance.     

Use of Meteosat imagery to define clouds linked with floods in Greece

A cloud classification scheme has been developed with the aim of defining and monitoring clouds cells associated with heavy rain. In the first stage of the scheme, Meteosat images in the visible were corrected to account for varying illumination times and angles. In the second stage, analysis of Meteosat images in the visible, infrared and water vapour channels resulted in the assignment of spectral signatures to seven categories of cloud class. The analysis was supported by temperature and humidity profiles from radiosondes in the wider geographical area as well as by synoptic maps of the area. Finally, Meteosat images reflecting two flood incidents which occurred in Greece on 21 October 1994 and 12 January 1997 were classified on the basis of the defined cloud categories; cloud cells associated with heavy rain were clearly depicted on the classified images through the category of thick opaque convective clouds.     

An algorithm for snow and ice detection using AVHRR data An extension to the APOLLO software package

Abstract

The AVHRR (Advanced Very High Resolution Radiometer) Processing scheme Over Land, cLbud and Ocean (APOLLO) is used to extract surface and cloud parameters from satellite data. Before these parameters can be computed, it is necessary to distinguish between land and ocean surfaces and to apply algorithms for the detection of partially cloudy and cloud-filled pixels. In regions with seasonal or permanent snow and ice coverage the separation of clouds becomes much more difficult or often impossible. For this reason, and to find cloud-free and partly cloudy snow and ice pixels,- a day-time algorithm has been developed which uses all five AVHRR channels as follows: The threshold testing of the reflected part of channel-3 radiance leads to a definite distinction between snow/ice and water clouds due, to the clouds much higher reflectivity at 3.7 μm. The detection; of sea ice is based on threshold tests of visible reflectances and, in particular, of the temperature difference between channels-4 and -5. Snow is identified if a high visible reflectance is combined with a low reflectance in channel-3 and with a ratio of channel-2 to channel-1 reflectances similar to that of a cloud. The latter criterion is also mostly suitable to distinguish between snow-covered and snow-free ice areas. Some tests of this algorithm applied to AVHRR data from the 1987 Baltic Sea ice season have shown reasonable classification results with the exception of a few areas with ice clouds or with ice topped water clouds.     

Comparison between in situ and MODIS-derived spectral reflectances of snow and sea ice in the Amundsen Sea,Antarctica

The spectral albedo and directional reflectance of snow and sea ice were measured on sea ice of various types, including nilas, grey ice, pancake ice, multi-year pack ice, and land-fast ice in the Ross, Amundsen and Bellingshausen seas during a summer cruise in February through March 2000. Measurements were made using a spectroradiometer that has 512 channels in the visible and near-infrared (VNIR) region in which 16 of the 36 bands of the Moderate Resolution Imaging Spectroradiometer (MODIS) are covered. Directional reflectance is also retrieved from the MODIS radiometrically calibrated data (Level 1B) concurrently acquired from the first National Aeronautics and Space Administration (NASA) Earth Observing System (EOS) satellite, Terra. The locations of the ground ice stations are identified accurately on the MODIS images, and the spectral albedo and directional reflectance values at the 16 VNIR MODIS bands are extracted for those pixel locations. MODIS-derived reflectance is then corrected for the intervening atmosphere whose parameters are retrieved from the MODIS atmospheric profiles product (MOD07_L2) for the same granule. The corresponding spectral albedo and directional reflectance with the same viewing geometry as MODIS are derived from our ground-based spectroradiometer measurements. Because the footprint of the ground spectroradiometer is much smaller than the pixel sizes of MODIS images, the averaged spectral reflectance and albedo in the vicinity of each ice station are simulated for the corresponding MODIS pixel from the ground spectral measurements by weighting over different surface types (various ice types and open water). An accurate determination of ice concentration is important in deriving ground reflectance of a simulated pixel from in situ measurements. The best agreement between the in situ and MODIS measurements was found when the ground had 10/10 ice concentration (discrepancy range 0.2–11.69%, average 4.8%) or was oneice-type dominant (discrepancy range 0.8–16.9%, average 6.2%). The more homogeneous the ground surface and the less variable the ground topography, the more comparable between the in situ and satellite-derived reflectance is expected.     

A novel algorithm of cloud detection for water quality studies using 250 m downscaled MODIS imagery

ABSTRACT

This study is part of a project aimed at developing an automated algorithm for algal bloom detection and quantification in inland water bodies using Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. An important step is to adequately detect and exclude clouds and haze because their presence affects chlorophyll-a (chl-a) estimations. Currently available cloud masking products appear to be ineffective in turbid coastal waters. The purpose of this study is to develop a cloud masking algorithm based on a probabilistic algorithm (Linear Discriminant Analysis) and designed for water bodies by using MODIS images downscaled at a 250 m spatial resolution (MODIS-D-250). Confusion matrix shows that the new cloud mask algorithm yields very satisfactory results, enabling water classification for heavy turbid conditions with a mean kappa coefficient (κ) of 0.993 and a 95% confidence interval ranging from 0.990 to 0.997. The model also shows a very low commission error (sensitive to the presence of haze), which is essential for accurate water quality monitoring, knowing that the presence of clouds/haze/aerosols leads to major issues in the estimation of water quality parameters. The cloud mask model applied on MODIS-D-250 images improves the sensitivity to haze and the classification of turbid waters located at the edge of urban areas better than the operational MODIS products, and it clearly shows an improvement of the spatial resolution (250 m spatial resolution) compared to other cloud mask algorithms (500 m or 1 km spatial resolution), leading to an increase in exploitable data for water quality studies.