Automatic Cloud Cover Assessment Research Articles

Spectral Albedo Estimation of Snow Covers in Pakistan Using Landsat Data

Accurate snow spectral albedo measurements from satellite data can essentially help to determine the changes over the snow surface area. In the current study, spectral snow albedo is measured over the northern region of Pakistan using spatio-temporal imagery datasets taken by Landsat satellite series equipped with TM and ETM+ sensors. An Automatic Cloud Cover Assessment (ACCA) algorithm is used to mask cloudy pixels from further processing. Furthermore, an image independent model, Second Simulation of the Satellite Signal in the Solar Spectrum (6S) is used in the current research work to atmospherically correct the satellite data. The snow cover albedo of northern Pakistan is estimated using pixel values prior to correction, ACCA output values, and 6S model output values. The results of the study show that highest albedo values are estimated using Landsat band 4 data with albedo model. The results of the study also show that the albedo values measured (band 4) in the year 1992 (0.989) are reduced in the year 2000 (0.931). It is expected that the results of the study could be utilized to predict climatic variations for spring runoff estimation.

A new Landsat 8 cloud discrimination algorithm using thresholding tests

ABSTRACTIn this work, we propose a Cloud Discrimination Algorithm for Landsat 8 (CDAL8) to improve a high-frequency automatic land change detection system developed at the National Institute of Advanced Industrial Science and Technology (AIST), Japan for large-scale satellite image analysis. Although the land change detection system can process several kinds of satellite remote sensing data, improvements are needed to enable practical applications using Landsat 8 data. Cloud discrimination is a necessary pre-processing step for land cover change detection. Currently, most of the prediction errors on land change detection are caused by the false cloud discrimination results as a pre-processing step. Therefore, we introduce an improved cloud discrimination algorithm (CDAL8) in this study to improve the overall performance of our land change detection system. The algorithm was developed based on a Moderate Resolution Imaging Spectroradiometer (MODIS) cloud mask algorithm and Cloud and Aerosol Unbiased Decision Intellectual Algorithm (CLAUDIA). CDAL8 is distinct in that it switches judgment tests and their thresholds using a threshold brightness temperature and uses separate features in cloud judgment and clear-sky judgment. To evaluate the accuracy of the proposed algorithm, we compared it with the Automated Cloud-Cover Assessment algorithm (ACCA) and Function of Mask (Fmask) version 3.3 using US Geological Survey Landsat 8 cloud cover assessment validation data, which contain 96 cloud masks. Our proposed cloud discrimination algorithm (CDAL8) have promising results with an accuracy of 88.1%, which was greater than that of the ACCA (82.5%) and Fmask (84.6%). Furthermore, we also confirmed that the average accuracy of CDAL8 was approximately 91.2% when low solar elevation scenes were removed.

A Cloud Detection Method Based on Relationship Between Objects of Cloud and Cloud-Shadow for Chinese Moderate to High Resolution Satellite Imagery

Cloud detection of satellite imagery is very important for quantitative remote sensing research and remote sensing applications. However, many satellite sensors do not have enough bands for a quick, accurate, and simple detection of clouds. Particularly, the newly launched moderate to high spatial resolution satellite sensors of China, such as the charge-coupled device on-board the Chinese Huan Jing 1 (HJ-1/CCD) and the wide field of view (WFV) sensor on-board the Gao Fen 1 (GF-1), only have four available bands including blue, green, red, and near infrared bands, which are far from the requirements of most could detection methods. In order to solve this problem, an improved and automated cloud detection method for Chinese satellite sensors called object-oriented cloud and cloud-shadow matching method (OCM) is presented in this paper. It first modified the automatic cloud cover assessment (ACCA) method, which was developed for Landsat-7 data, to get an initial cloud map. The modified ACCA method is mainly based on threshold and different threshold settings produce different cloud maps. Subsequently, a strict threshold is used to produce a cloud map with high confidence and large amount of cloud omission and a loose threshold is used to produce a cloud map with low confidence and large amount of commission. Second, a corresponding cloud-shadow map is also produced using the threshold of near-infrared band. Third, the cloud maps and cloud-shadow map are transferred to cloud objects and cloud-shadow objects. Cloud and cloud-shadow are usually in pairs; consequently, the final cloud and cloud-shadow maps are made based on the relationship between cloud and cloud-shadow objects. The OCM method was tested using almost 200 HJ-1/CCD and GF-1/WFV images across China and the overall accuracy of cloud detection is close to 90%.

Optimization of Selected Remote Sensing Algorithms for Many-Core Architectures

This paper evaluates the potential of embedded graphic processing units (GPU) in the Nvidia's Tegra K1 for onboard processing. The performance is compared to a general purpose multicore central processing unit (CPU), a full-fledge GPU accelerator, and an Intel Xeon Phi coprocessor, for two representative potential applications, wavelet spectral dimension reduction of hyperspectral imagery and automated cloud-cover assessment (ACCA). For these applications, Tegra K1 achieved 51% performance for the ACCA algorithm and 20% performance for the dimension reduction algorithm, as compared to the performance of the high-end eight-core server Intel Xeon CPU which has a 13.5 times higher power consumption. This paper also shows the potential of modern high-performance computing accelerators for algorithms such as the ones for which the paper presents an optimized parallel implementation. The two algorithms that were tested mostly contain spatially localized computations, and one can assume that all image processing algorithms containing localized computations would exhibit similar speed-ups when implemented on these parallel architectures.

CLOUD DETECTION OF OPTICAL SATELLITE IMAGES USING SUPPORT VECTOR MACHINE

Cloud covers are generally present in optical remote-sensing images, which limit the usage of acquired images and increase the difficulty of data analysis, such as image compositing, correction of atmosphere effects, calculations of vegetation induces, land cover classification, and land cover change detection. In previous studies, thresholding is a common and useful method in cloud detection. However, a selected threshold is usually suitable for certain cases or local study areas, and it may be failed in other cases. In other words, thresholding-based methods are data-sensitive. Besides, there are many exceptions to control, and the environment is changed dynamically. Using the same threshold value on various data is not effective. In this study, a threshold-free method based on Support Vector Machine (SVM) is proposed, which can avoid the abovementioned problems. A statistical model is adopted to detect clouds instead of a subjective thresholding-based method, which is the main idea of this study. <br><br> The features used in a classifier is the key to a successful classification. As a result, Automatic Cloud Cover Assessment (ACCA) algorithm, which is based on physical characteristics of clouds, is used to distinguish the clouds and other objects. In the same way, the algorithm called Fmask (Zhu et al., 2012) uses a lot of thresholds and criteria to screen clouds, cloud shadows, and snow. Therefore, the algorithm of feature extraction is based on the ACCA algorithm and Fmask. Spatial and temporal information are also important for satellite images. Consequently, co-occurrence matrix and temporal variance with uniformity of the major principal axis are used in proposed method. We aim to classify images into three groups: cloud, non-cloud and the others. In experiments, images acquired by the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and images containing the landscapes of agriculture, snow area, and island are tested. Experiment results demonstrate the detection accuracy of the proposed method is better than related methods.

CLOUD DETECTION OF OPTICAL SATELLITE IMAGES USING SUPPORT VECTOR MACHINE

Abstract. Cloud covers are generally present in optical remote-sensing images, which limit the usage of acquired images and increase the difficulty of data analysis, such as image compositing, correction of atmosphere effects, calculations of vegetation induces, land cover classification, and land cover change detection. In previous studies, thresholding is a common and useful method in cloud detection. However, a selected threshold is usually suitable for certain cases or local study areas, and it may be failed in other cases. In other words, thresholding-based methods are data-sensitive. Besides, there are many exceptions to control, and the environment is changed dynamically. Using the same threshold value on various data is not effective. In this study, a threshold-free method based on Support Vector Machine (SVM) is proposed, which can avoid the abovementioned problems. A statistical model is adopted to detect clouds instead of a subjective thresholding-based method, which is the main idea of this study. The features used in a classifier is the key to a successful classification. As a result, Automatic Cloud Cover Assessment (ACCA) algorithm, which is based on physical characteristics of clouds, is used to distinguish the clouds and other objects. In the same way, the algorithm called Fmask (Zhu et al., 2012) uses a lot of thresholds and criteria to screen clouds, cloud shadows, and snow. Therefore, the algorithm of feature extraction is based on the ACCA algorithm and Fmask. Spatial and temporal information are also important for satellite images. Consequently, co-occurrence matrix and temporal variance with uniformity of the major principal axis are used in proposed method. We aim to classify images into three groups: cloud, non-cloud and the others. In experiments, images acquired by the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and images containing the landscapes of agriculture, snow area, and island are tested. Experiment results demonstrate the detection accuracy of the proposed method is better than related methods.

A Rapid Cloud Mask Algorithm for Suomi NPP VIIRS Imagery EDRs

Abstract. Suomi National Polar-orbiting Partnership (NPP) is the first of a new generation of NASA's Earth-observing research satellites. The Suomi NPP Visible Infrared Imaging Radiometer Suite (VIIRS) collects visible and infrared views of Earth's dynamic surface processes. This NPP mission produces a series of Environmental Data Records (EDRs). As accurate information on cloud occurrence is of utmost importance for a wide range of remote-sensing applications and analyses, we developed a cloud mask algorithm, adapted from the Landsat 7 Automatic Cloud Cover Assessment, for use with the VIIRS Imagery EDRs. The algorithm consists of a sequence of pixel-based tests that use thresholds on VIIRS top-of-atmosphere reflectances and brightness temperatures. Our cloud mask algorithm provides a simpler, though less informative and robust, alternative to the VIIRS Cloud Mask (VCM) Intermediate Product, with the advantage in that it can be applied to a higher spatial resolution VIIRS Imagery EDR. The algorithm is compared with the VCM in three case studies.

A Sensor Specified Method Based on Spectral Transformation for Masking Cloud in Landsat Data

Few uniform approaches designed for masking clouds by means of high resolution sensors have been widely developed due to the divergence in spectral channels. Here we propose an algorithm on a basis of two linear constraints among indexes acquired from the normalized Tasseled Cap (TC) transformation. This sensor specific approach requires neither thermal bands nor other reference images. The diversity of cloud type and cloud coverage (1%-20%) is analyzed for validating the present algorithm using nine scenes of the typical Landsat sensors. The first results derived from Landsat MSS and TM images are assessed in comparison with the Maximum Likelihood supervised classification method. Another comparison is then employed in analyzing the agreement between the automatic cloud cover assessment (ACCA) algorithm and our method. Although varying acceptable degrees of discrepancies are occurred among the above three methods in identifying thin clouds and cloud edges, the overall results show a fine agreement in detecting thick clouds and large clouds. In the event of the acquirement of the sensor-based parameters of TC transformation, this method could be developed for flagging clouds on other high resolution sensors, like SPOT, Quick-Bird, HJ, etc.

Development of the Landsat Data Continuity Mission Cloud-Cover Assessment Algorithms

The upcoming launch of the Operational Land Imager (OLI) will start the next era of the Landsat program. However, the Automated Cloud-Cover Assessment (CCA) (ACCA) algorithm used on Landsat 7 requires a thermal band and is thus not suited for OLI. There will be a thermal instrument on the Landsat Data Continuity Mission (LDCM)—the Thermal Infrared Sensor—which may not be available during all OLI collections. This illustrates a need for CCA for LDCM in the absence of thermal data. To research possibilities for full-resolution OLI cloud assessment, a global data set of 207 Landsat 7 scenes with manually generated cloud masks was created. It was used to evaluate the ACCA algorithm, showing that the algorithm correctly classified 79.9% of a standard test subset of <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$3.95\ 10^{9}\ \hbox{pixels}$</tex></formula> . The data set was also used to develop and validate two successor algorithms for use with OLI data—one derived from an off-the-shelf machine learning package and one based on ACCA but enhanced by a simple neural network. These comprehensive CCA algorithms were shown to correctly classify pixels as cloudy or clear 88.5% and 89.7% of the time, respectively.

Implementation on Landsat Data of a Simple Cloud-Mask Algorithm Developed for MODIS Land Bands

This letter assesses the performance on Landsat-7 images of a modified version of a cloud-masking algorithm originally developed for clear-sky compositing of Moderate Resolution Imaging Spectroradiometer images at northern midlatitudes. While most historical Landsat data include measurements at thermal wavelengths and such measurements are also planned for the next mission, thermal tests are not included in the suggested algorithm in order to maintain greater versatility and ease of use. The evaluation of the masking algorithm takes advantage of the availability of manual (visual) cloud masks developed at the U.S. Geological Survey for a collection of Landsat scenes. As part of the evaluation, we also include the automated cloud cover assessment (ACCA) algorithm which does include thermal tests and is used operationally by the Landsat-7 mission to provide scene cloud fractions but no cloud masks. We show that the proposed algorithm performs on par with the ACCA both in terms of scene cloud fraction and pixel-level mask agreement. Specifically, the algorithm gives an error of 0.8% for the scene cloud fraction of 156 scenes and a root-mean-square error of 7.1%, while it agrees with the manual mask for 93.1% of the pixels. These performance indicators are very similar to those of the ACCA (1.2%, 7.1%, and 93.7%).

A cloud mask methodology for high resolution remote sensing data combining information from high and medium resolution optical sensors

This study presents a novel cloud masking approach for high resolution remote sensing images in the context of land cover mapping. As an advantage to traditional methods, the approach does not rely on thermal bands and it is applicable to images from most high resolution earth observation remote sensing sensors. The methodology couples pixel-based seed identification and object-based region growing. The seed identification stage relies on pixel value comparison between high resolution images and cloud free composites at lower spatial resolution from almost simultaneously acquired dates. The methodology was tested taking SPOT4-HRVIR, SPOT5-HRG and IRS-LISS III as high resolution images and cloud free MODIS composites as reference images. The selected scenes included a wide range of cloud types and surface features. The resulting cloud masks were evaluated through visual comparison. They were also compared with ad-hoc independently generated cloud masks and with the automatic cloud cover assessment algorithm (ACCA). In general the results showed an agreement in detected clouds higher than 95% for clouds larger than 50 ha. The approach produced consistent results identifying and mapping clouds of different type and size over various land surfaces including natural vegetation, agriculture land, built-up areas, water bodies and snow.

A combined spectral and object-based approach to transparent cloud removal in an operational setting for Landsat ETM+

The automated cloud cover assessment (ACCA) algorithm has provided automated estimates of cloud cover for the Landsat ETM+ mission since 2001. However, due to the lack of a band around 1.375μm, cloud edges and transparent clouds such as cirrus cannot be detected. Use of Landsat ETM+ imagery for terrestrial land analysis is further hampered by the relatively long revisit period due to a nadir only viewing sensor. In this study, the ACCA threshold parameters were altered to minimise omission errors in the cloud masks. Object-based analysis was used to reduce the commission errors from the extended cloud filters. The method resulted in the removal of optically thin cirrus cloud and cloud edges which are often missed by other methods in sub-tropical areas. Although not fully automated, the principles of the method developed here provide an opportunity for using otherwise sub-optimal or completely unusable Landsat ETM+ imagery for operational applications. Where specific images are required for particular research goals the method can be used to remove cloud and transparent cloud helping to reduce bias in subsequent land cover classifications.

Reconfigurable processing for satellite on-board automatic cloud cover assessment

Clouds have a critical role in many studies such as weather- and climate-related investigations. However, they represent a source of errors in many applications, and the presence of cloud contamination can hinder the use of satellite data. In addition, sending cloudy data to ground stations can result in an inefficient utilization of the communication bandwidth. This requires satellite on-board cloud detection capability to mask out cloudy pixels from further processing. Remote sensing satellite missions have always required smaller size, lower cost, more flexibility, and higher computational power. Reconfigurable Computers (RCs) combine the flexibility of traditional microprocessors with the power of Field Programmable Gate Arrays (FPGAs). Therefore, RCs are a promising candidate for on-board preprocessing. This paper presents the design and implementation of an RC-based real-time cloud detection system. We investigate the potential of using RCs for on-board preprocessing by prototyping the Landsat 7 ETM+ ACCA algorithm on one of the state-of-the-art reconfigurable platforms, SRC-6. It will be shown that our work provides higher detection accuracy and over one order of magnitude improvement in performance when compared to previously reported investigations.

Characterization of the Landsat-7 ETM+ Automated Cloud-Cover Assessment (ACCA) Algorithm

A scene-average automated cloud-cover assessment (ACCA) algorithm has been used for the Landsat-7 Enhanced Thematic Mapper Plus (ETM� ) mission since its launch by NASA in 1999. ACCA assists in scheduling and confirming the acquisition of global “cloud-free” imagery for the U.S. archive. This paper documents the operational ACCA algorithm and validates its performance to a standard error of � 5 percent. Visual assessment of clouds in three-band browse imagery were used for comparison to the five-band ACCA scores from a stratified sample of 212 ETM� 2001 scenes. This comparison of independent cloud-cover estimators produced a 1:1 correlation with no offset. The largest commission errors were at high altitudes or at low solar illumination where snow was misclassified as clouds. The largest omission errors were associated with undetected optically thin cirrus clouds over water. There were no statistically significant systematic errors in ACCA scores analyzed by latitude, seasonality, or solar elevation angle. Enhancements for additional spectral bands, per-pixel masks, land/water boundaries, topography, shadows, multidate and multi-sensor imagery were identified for possible use in future ACCA algorithms.

A New Normalized Difference Cloud Retrieval Technique Applied to Landsat Radiances over the Oklahoma ARM Site

The authors propose a new cloud property retrieval technique that accounts for cloud side illumination and shadowing effects present at high solar zenith angles. The technique uses the normalized difference of nadir reflectivities (NDNR) at a conservative and an absorbing (with respect to liquid water) wavelength. It can be further combined with the inverse nonlocal independent pixel approximation (NIPA) of Marshak et al. that corrects for radiative smoothing, thus providing a retrieval framework where all 3D cloud effects can potentially be accounted for. The effectiveness of the new technique is demonstrated using Monte Carlo simulations. Realworld application is shown to be feasible using Thematic Mapper (TM) radiance observations from Landsat-5 over the Southern Great Plains (SGP) site of the Atmospheric Radiation Measurement (ARM) Program. For the moderately oblique (458) solar zenith angle of the available Landsat scene, NDNR gives similar regional statistics and histograms when compared with standard independent pixel approximation (IPA), but significant differences at the pixel level. Inverse NIPA is also applied for the first time on observed high-resolution radiances of overcast Landsat subscenes. The dependence of the NIPA-retrieved cloud fields on the parameters of the method is illustrated and practical issues related to the optimal choice of these parameters are discussed. It is natural to compare novel cloud retrieval techniques with standard IPA retrievals. IPA is useful in revealing the inadequacy of plane parallel theory in certain situations and in demonstrating sensitivities to parameter choices, parameterizations, and assumptions. For example, it is found that IPA has problems in matching modeled and observed band-7 (2.2 mm) reflectance values for ;6% of the pixels, most of which are at cloud edges. For simultaneous cloud optical depth‐droplet effective radius retrievals (where a conservative and an absorptive TM band are needed), it is found that the band-4 (0.83 mm)‐band-7 pair was the most well behaved, having less saturation, smaller changes in nominal calibration, and better overall consistency with modeled values than other bands. Mean values of optical depth, effective radius, and liquid water path (LWP) for typical IPA retrievals using this pair are t 5 22, re 5 11 mm, and LWP 5 157 gm 22, respectively. Inclusion of aerosol scattering above clouds results in ;8% decrease in mean cloud optical depth for an aerosol optical depth of 0.2. Degradation of instrument resolution up to ;2 km has small effects on the optical property means and histograms, suggesting small actual cloud variability at these scales and/or radiative smoothing. Comparisons with surface instruments (microwave radiometer, pyranometer, and pyrgeometer) verify the statisitical adequacy of the IPA retrievals. Last, cloud fractions derived with a simple threshold method are compared with those from an automated procedure called Automatic Cloud Cover Assessment now in operational use for Landsat-7. For the northernmost 2000 scanlines of the scene, the cloud fraction Ac is 0.585 from thresholding, as compared with Ac 5 0.563 for the automated procedure, and the full scene values are Ac 5 0.870 and Ac 5 0.865, respectively. This suggests that the Landsat-7 automated procedure will likely give reliable scene-averaged cloud fractions for moderately thick clouds over continental U.S. scenes similar to SGP.