Cirrus Band Research Articles

Mapping global flying aircraft activities using Landsat 8 and cloud computing

Satellite-based remote sensing might provide a potential way for monitoring the global flight activities and their environment impacts, while the remote sensing community pays less attention on it. In this study, we presented a flying aircraft detection algorithm which effectively handles the noise on Landsat 8 OLI cirrus band caused by energetic particles in the South Atlantic Anomaly region, and a new framework based on cloud infrastructure was proposed to map global flying aircraft activities from 2013 to 2020 using Landsat 8 Operational Land Imager (OLI) data. Validation was performed for 254 scenes recorded for various cloudy and surface conditions and vapor contents. The overall percentages of false alarms and omissions for these validation images were 5.37% and 7.80%, respectively. Limited to the resolution of Landsat data, cloud, the size and flight altitude of the aircraft, 42.99% flying aircraft were undetected compared with the FlightRadar24. Instead of using the Google Earth Engine, we employed more flexible cloud computing techniques, Google Cloud Storage and Google Calculation Engine, to construct our framework for the larger volume data. A total of 1.94 million Landsat images were analyzed to obtain the activities maps, and the results showed that globally flying aircraft increased by 25.85% from 2014 to 2019 (the year 2013 was excluded for the low coverage of Landsat scenes), with an annual rate of 4.31%. In 2020, flying aircraft were reduced by 40% compared with 2019 due to the influence of COVID-19 and traveling restrictions, and Europe was the most severely affected by COVID-19, with an 84.59% decline of flying aircraft in April 2020. This study provides a unique long-term supplement to monitor aviation activities and their climate impact.

A scattering law based cirrus correction method for Landsat 8 OLI visible and near-infrared images

Cirrus clouds are commonly distributed in the atmosphere, and can lead to degradation of the ground information in optical remotely sensed images obtained using airborne and spaceborne sensors. A cirrus band on the Landsat 8 Operational Land Imager (OLI), i.e., Band 9, is designed to precisely detect cirrus cloud, providing a reference basis for the radiance calculation of cirrus cloud in other bands. Therefore, based on a scattering law, a cirrus cloud correction method is proposed in this paper to restore the ground information for Landsat 8 OLI visible and near-infrared (VNIR) images by comprehensively using the visible and cirrus bands. The scattering law and the high correlation between the two adjoining blue bands are combined to estimate each VNIR band's cirrus radiance. The relationship among the bands is constrained to comply with the scattering law. The differences between the ocean and land surfaces are also considered when determining the parameter in the scattering law. In the experiments conducted in this study, both simulated and acquired images contaminated with cirrus cloud were used to evaluate the proposed method, and both visual and quantitative evaluations were carried out to verify the performance. The results suggest that the cirrus cloud in the VNIR bands can be removed by the use of the proposed method. The ground information can also be adequately restored, with a mean absolute error of less than 0.7757 for the simulated data and a structural similarity index measure of greater than 0.8621 for the acquired data. When compared with three existing correction methods, the proposed method was able to obtain clearer and more accurate results over various land and ocean scenes. Furthermore, this method's applicability to other sensors with similar bands, such as the Sentinel-2 Multispectral Instrument, was also investigated and validated.

Cirrus clouds that adversely affect Landsat 8 images: What are they and how to detect them?

We developed an algorithm called Cmask (Cirrus cloud mask) for cirrus cloud detection in Landsat 8 imagery using time series of Cirrus Band (1.36–1.39 μm) observations. For each pixel, a harmonic model, which includes a water vapor regressor, based on all available Cirrus Band observations is estimated using the Robust Iteratively Reweighted Least Squares (RIRLS) regression approach, and pixels affected by cirrus cloud are identified by comparing model predictions and actual satellite observations of the Cirrus Band Top-Of-Atmosphere (TOA) reflectance. Furthermore, we analyzed the effect of increasing Cirrus Band TOA reflectance on the surface reflectance of the Blue, Green, Red, Near Infrared (NIR), and two Shortwave Infrared (SWIR) (SWIR 1 and SWIR 2) Bands based on a set of globally distributed random samples. The goal of this study is to answer the question of what are cirrus clouds in the context of a Landsat observation, or more specifically, when should we identify a pixel as cirrus cloud such that we know the reflectance in the other spectral bands has been seriously affected by cirrus clouds. The challenge is to then develop a simple and operational algorithm for accurate detection of cirrus clouds in Landsat 8 images. The Cmask algorithm reduced almost by half the errors found in the U.S. Geological Survey (USGS) Quality Assessment (QA) Band for distinguishing cirrus cloud and clear observations (8% versus 15% error).

Thin cloud removal with residual symmetrical concatenation network

Thin cloud removal is important for enhancing the utilization of optical remote sensing imagery. Different from thick cloud removal, the pixels contaminated by thin clouds still preserve some surface information. Therefore, thin cloud removal methods usually focus on suppressing the cloud influence instead of replacing the cloudy pixels. In this paper, we proposed a deep residual symmetrical concatenation network (RSC-Net) to make end-to-end thin cloud removal. The RSC-Net is based on the encoding-decoding framework consisting of multiple residual convolutional layers and residual deconvolutional layers. The feature maps of each convolutional layer are copied and concatenated to the symmetrical deconvolutional layer. We used real cloud-contaminated and cloud-free Landsat-8 data very close in time for both training and testing. The RSC-Net is trained to take cloudy images as input and directly produce corresponding cloud-free images as output with all the bands together except the cirrus band and the panchromatic band. Compared with other traditional and state-of-the-art deep learning based methods, the experimental results show that our method has significant advantages in removing thin cloud contaminations in different bands.

Modeling of Thin-Cloud TOA Reflectance Using Empirical Relationships and Two Landsat-8 Visible Band Data

Clouds are a common barrier of satellite optical images and adversely affect applications of remotely sensed optical data sets. The optical thickness of clouds varies spatiotemporally. The thickness can be very thin making the detection of thin clouds difficult. A new cirrus band (Band-9) of Landsat-8 has been added to detect thin clouds. However, the majority of spaceborne optical sensors existed previously or in operation do not have the cirrus band. An algorithm is developed to detect thin clouds without using a cirrus band. In particular, the top-of-atmosphere reflectance of thin clouds is modeled using the empirical relationships of the deep blue and blue bands of Landsat-8 Operational Land Imager. A Landsat-8 image of path 14/row 36 near southeastern North Carolina, USA, is used to validate the algorithm. Thin clouds are well-identified when compared to Landsat-8 Band-9 data. The spatial correlation coefficient for both is 93.49%. Therefore, the algorithm is valid. The algorithm is further verified when a blue band and a green band are used to develop the algorithm. Thus, the analytical approach should be extendible to Landsats 4, 5, and 7 sensors or optical sensors as long as they have a blue band and a green band. Finally, the applicability of the algorithm under various atmospheric conditions is verified after analyzing two water vapor absorption spectral bands of NASA/JPL Airborne Visible/Infrared Imaging Spectrometer data.

Improving NDVI by removing cirrus clouds with optical remote sensing data from Landsat-8 – A case study in Quito, Ecuador

The Andean region has a high cloud density throughout the year. The use of optical remote sensing data in the computation of environmental indices of this region has been hampered by the presence of clouds. To maximize accuracy in the computation of several environmental indices including the normalized difference vegetation index (NDVI), we compared the performance of two algorithms in removing clouds in Landsat-8 Operational Land Imager (OLI) data of a high-elevation area. The study area was Quito, Ecuador, which is a city located close to the equator and in a high-elevation area crossed by the Andes Mountains. The first algorithm was the automatic cloud removal method (ACRM), which employs a linear regression between the different spectral bands and the cirrus band. The second algorithm was independent component analysis (ICA), which considers the noise (clouds) as part of independent components applied over the study area. These methods were evaluated based on several images from different years with different cloud conditions. The results indicate that neither algorithm is effective over this region for the removal of clouds or for NDVI computation. However, after improving ACRM, the NDVI computed using ACRM showed a better correlation than ICA with the MODIS NDVI product.

Evaluation of Sentinel-2A Surface Reflectance Derived Using Sen2Cor in North America

Surface reflectance can be derived from satellite measurements for the top of atmosphere and provides an important dataset for monitoring land change reliably. In this study, the Sentinel-2A surface reflectance was generated using the Sentinel-2 atmospheric correction (Sen2Cor) processor. To evaluate this dataset, surface data at 40 sites of the aerosol robotic network over North America from January 2016 to August 2017 were collected and processed. The surface reflectance reference was derived from the second simulation of the satellite signal in the solar spectrum–vector (6SV) code. The aerosol optical thickness (AOT), water vapor, surface reflectance, and three spectral indices generated by Sen2Cor were evaluated using the metrics including the accuracy, precision, and uncertainty ( A , P , and U ). The results show that due to the limitations of Sen2Cor aerosol retrieval algorithm, the Sentinel-2A AOT was significantly overestimated, with the relative accuracy over 160%. The Sen2Cor surface reflectance is generally overestimated, especially for the bright pixels, except for the cirrus band. For the 12 Sentinel-2A bands, the mean values of relative A , P , and U are 4.15%, 13.44%, and 14.92%, respectively. Among the three spectral indices, the normalized difference vegetation index performs best, with a correlation coefficient of 0.973 against the surface data. Furthermore, the Sen2Cor surface reflectance was compared with other satellite products. The mean correlation coefficient between Sentinel-2A and Landsat 8 surface reflectance is found to be 0.761. This study suggests that a better AOT retrieval is critical for improvement of Sen2Cor in the future.

Improvement of the Fmask algorithm for Sentinel-2 images: Separating clouds from bright surfaces based on parallax effects

Reliable identification of clouds is necessary for any type of optical remote sensing image analysis, especially in operational and fully automatic setups. One of the most elaborated and widespread algorithms, namely Fmask, was initially developed for the Landsat suite of satellites. Despite their similarity, application to Sentinel-2 imagery is currently hampered by the unavailability of a thermal band, and although results can be improved when taking the cirrus band into account, Sentinel-2 cloud detections are unsatisfactory in two points. (1) Low altitude clouds can be undetectable in the cirrus band, and (2) bright land surfaces – especially built-up structures – are often misclassified as clouds when only considering spectral information. In this paper, we present the Cloud Displacement Index (CDI), which makes use of the three highly correlated near infrared bands that are observed with different view angles. Hence, elevated objects like clouds are observed under a parallax and can be reliably separated from bright ground objects. We compare CDI with the currently used cloud probabilities, and propose how to integrate this new functionality into the Fmask algorithm. We validate the approach using test images over metropolitan areas covering a wide variety of global environments and climates, indicating the successful separation of clouds and built-up structures (overall accuracy 95%, i.e. an improvement in overall accuracy of 0.29–0.39 compared to the previous Fmask versions over the 20 test sites), and hence a full compensation for a missing thermal band.

Sentinel-2 MSI Radiometric Characterization and Cross-Calibration with Landsat-8 OLI

Near-nadir observations by the Multispectral Instrument (MSI) onboard the Sentinel-2 and the Operational Land Imager (OLI) onboard Landsat 8 were collected during two Simultaneous Nadir Overpasses (SNO). Multispectral images with 10, 20, and 30 m resolution from a spatially uniform area in the Saharan desert were acquired for direct comparison of MSI and OLI Top- Of-Atmosphere (TOA) reflectances. This paper presents an initial radiometric cross-calibration of the 8 corresponding spectral bands of the Sentinel-2 MSI and Landsat 8 OLI sensors. With the well-calibrated Landsat 8 OLI as a reference, the comparison indicates that 6 MSI bands are consistent with OLI within 3% in terms of spectral band adjustment factors Bi . The Near-Infra-Red (NIR) and cirrus bands are exceptions. They yield radiometric differences on the order of 8% and 15% respectively. Cross-calibration results show that the radiometric difference of the 7 corresponding bands are consistent to OLI within 1% or better, except on cirrus band. A pixel-by-pixel match between the MSI and OLI observations for different land covers showed that. This initial study suggests that the red-edge band B8A of MSI can be used to replace the NIR band B08 when conducting vegetation monitoring.

OCEAN COLOR RETRIEVAL USING LANDSAT-8 IMAGERY IN COASTAL CASE 2 WATERS (CASE STUDY PERSIAN AND OMAN GULF)

Abstract. Ocean color (OC) monitoring using satellite imageries provides an appropriate tool for a better understanding of marine processes and changes in the coastal environment. Radiance measurements in the range of visible light of the electromagnetic spectrum provides information of ocean color that is associated with the water constituents. This measurements are used to monitor the level of biological activity and the presence of particles in the water. Ocean features such as the concentration of chlorophyll, suspended sediment concentration and sea surface temperature have a significant impact on the dynamics of the ocean. The concentration of chlorophyll (chla), active pigments of phytoplankton photosynthesis, as a key indicator applied for assessment of water quality and biochemistry. Experimental algorithms chla related to internal communication various optical components in the water that may be change in space and time in the water with different optical characteristics. Therefore, the algorithms have been developed for one area may not work for other places and each region according to its specific characteristics needs that determined by an algorithm may be appropriate to local. We have tried treatment several algorithms for determination of chlorophyll, including experimental algorithms with a simple band ratio of blue-green band (i.e. OCx) and algorithms includes two bands ratio with variable 𝑅𝑟𝑠(λ2)/𝑅𝑟𝑠(λ1), the three bands ratio with variable [𝑅𝑟𝑠(λ1)−1−𝑅𝑟𝑠(λ2)−1]×𝑅𝑟𝑠(λ3) and four bands ratio with variable [𝑅𝑟𝑠(λ1)−1−𝑅𝑟𝑠(λ2)−1]/[𝑅𝑟𝑠(λ4)−1−𝑅𝑟𝑠(λ3)−1] that desired wavelength (i.e. λ1, λ2, λ3 and λ4) in the range of red and near-infrared wavelengths of the electromagnetic spectrum are in the region of the Persian Gulf and Oman Sea look. Despite the high importance of the Persian Gulf and Oman Sea which can have up basin countries, to now few studies have been done in this area. The focus of this article on the northern part of Oman Sea and Persian Gulf, the shores of neighboring Iran (case 2 water). In this paper, by using Landsat 8 satellite imageries, we have discussed chla concentrations and customizing different OC algorithms for this new dataset (Landsat-8 imagery). This satellite was launched in 2013 and its data using two sensors continuously are provided operating one sensor imager land (OLI: Operational Land Imager) and the Thermal Infrared Sensor (TIRS: Thermal InfraRed Sensor) and are available. This sensors collect image data, respectively, for the nine-band short wavelength in the range of 433-2300 nm and dual-band long wavelength thermal. Seven band of the nine band picked up by the sensor information of OLI to deal with sensors TM (Thematic Mapper) and ETM+ (Enhanced Thematic Mapper Plus) in previous satellite Landsat compatible and two other band, the band of coastal water (433 to 453 nm) and Cirrus band (1360 to 1390 nm), short wave infrared provides to measure water quality and high thin clouds. Since OLI sensor in Landsat satellite 8 compared with other sensors to study OC have been allocated a much better spatial resolution can be more accurate to determine changes in OC. To evaluate the results of the image sensor MODIS (Moderate Resolution Imaging Spectroradiometer) at the same time satellite images Landsat 8 is used. The statistical parameters used in order to evaluate the performance of different algorithms, including root mean square error (RMSE) and coefficient of determination (R2), and on the basis of these parameters we choose the most appropriate algorithm for the area. Extracted results for implementing different OC algorithms clearly shows superiority of utilized method by R2=0.71 and RMSE=0.07.

OCEAN COLOR RETRIEVAL USING LANDSAT-8 IMAGERY IN COASTAL CASE 2 WATERS (CASE STUDY PERSIAN AND OMAN GULF)

Ocean color (OC) monitoring using satellite imageries provides an appropriate tool for a better understanding of marine processes and changes in the coastal environment. Radiance measurements in the range of visible light of the electromagnetic spectrum provides information of ocean color that is associated with the water constituents. This measurements are used to monitor the level of biological activity and the presence of particles in the water. Ocean features such as the concentration of chlorophyll, suspended sediment concentration and sea surface temperature have a significant impact on the dynamics of the ocean. The concentration of chlorophyll (<i>chla</i>), active pigments of phytoplankton photosynthesis, as a key indicator applied for assessment of water quality and biochemistry. Experimental algorithms <i>chla</i> related to internal communication various optical components in the water that may be change in space and time in the water with different optical characteristics. Therefore, the algorithms have been developed for one area may not work for other places and each region according to its specific characteristics needs that determined by an algorithm may be appropriate to local. We have tried treatment several algorithms for determination of chlorophyll, including experimental algorithms with a simple band ratio of blue-green band (i.e. OCx) and algorithms includes two bands ratio with variable 𝑅<sub>𝑟𝑠</sub>(λ<sub>2</sub>)/𝑅<sub>𝑟𝑠</sub>(λ<sub>1</sub>), the three bands ratio with variable [𝑅<sub>𝑟𝑠</sub>(λ<sub>1</sub>)−1−𝑅<sub>𝑟𝑠</sub>(λ<sub>2</sub>)−1]×𝑅<sub>𝑟𝑠</sub>(λ<sub>3</sub>) and four bands ratio with variable [𝑅<sub>𝑟𝑠</sub>(λ<sub>1</sub>)−1−𝑅<sub>𝑟𝑠</sub>(λ<sub>2</sub>)−1]/[𝑅<sub>𝑟𝑠</sub>(λ<sub>4</sub>)−1−𝑅<sub>𝑟𝑠</sub>(λ<sub>3</sub>)−1] that desired wavelength (i.e. λ<sub>1</sub>, λ<sub>2</sub>, λ<sub>3</sub> and λ<sub>4</sub>) in the range of red and near-infrared wavelengths of the electromagnetic spectrum are in the region of the Persian Gulf and Oman Sea look. Despite the high importance of the Persian Gulf and Oman Sea which can have up basin countries, to now few studies have been done in this area. The focus of this article on the northern part of Oman Sea and Persian Gulf, the shores of neighboring Iran (case 2 water). In this paper, by using Landsat 8 satellite imageries, we have discussed chla concentrations and customizing different OC algorithms for this new dataset (Landsat-8 imagery). This satellite was launched in 2013 and its data using two sensors continuously are provided operating one sensor imager land (OLI: Operational Land Imager) and the Thermal Infrared Sensor (TIRS: Thermal InfraRed Sensor) and are available. This sensors collect image data, respectively, for the nine-band short wavelength in the range of 433-2300 nm and dual-band long wavelength thermal. Seven band of the nine band picked up by the sensor information of OLI to deal with sensors TM (Thematic Mapper) and ETM+ (Enhanced Thematic Mapper Plus) in previous satellite Landsat compatible and two other band, the band of coastal water (433 to 453 nm) and Cirrus band (1360 to 1390 nm), short wave infrared provides to measure water quality and high thin clouds. Since OLI sensor in Landsat satellite 8 compared with other sensors to study OC have been allocated a much better spatial resolution can be more accurate to determine changes in OC. To evaluate the results of the image sensor MODIS (Moderate Resolution Imaging Spectroradiometer) at the same time satellite images Landsat 8 is used. The statistical parameters used in order to evaluate the performance of different algorithms, including root mean square error (RMSE) and coefficient of determination (R<sup>2</sup>), and on the basis of these parameters we choose the most appropriate algorithm for the area. Extracted results for implementing different OC algorithms clearly shows superiority of utilized method by R2=0.71 and RMSE=0.07.

An analysis of Landsat 7 and Landsat 8 underflight data and the implications for time series investigations

Abstract The newly launched Landsat 8 satellite continues the long and extremely important record of Earth observation from the Landsat program. We analyzed differences between Landsat 7 and Landsat 8 surface reflectances and cirrus cloud characterization to address how substitutable Landsat 8 observations are within this long archive. Comparison of surface reflectance estimates acquired near simultaneously during Landsat 8's underflight orbital placement shows that Landsat 8 surface reflectance is consistently darker in the blue, green, and red bands and brighter in the near infrared than in Landsat 7. Vegetation indices that rely on the visible and near infrared bands should be used with caution as individual biases in index components can be amplified to create large biases in vegetation indices. We also analyzed time series datasets from the Landsat Climate Data Record (CDR) surface reflectance product across four scenes that contained only Landsat 7 data, Landsat 7 data and only Landsat 8 data post-launch, and Landsat 7 data and data from both sensors post-launch to investigate how sensor differences propagate in time series analysis. If left uncorrected or unexplained, the difference in reflectance between Landsat 7 and Landsat 8 creates spurious time trends in visible wavelengths and in the Normalized Difference Vegetation Index (NDVI). The introduction of Landsat 8 into time series of Landsat 7 data also biases the mean reflectance or vegetation index value as measured by a time series model intercept while increasing the Root Mean Squared Error of such models. We characterized the spectral reflectance of cirrus clouds in the underflight data that were omitted from Landsat 7 cloud masks but were detected in Landsat 8's cloud mask due to the use of the newly added cirrus band. While these cirrus cloud observations missed in Landsat 7's cloud mask are only slightly brighter in the visible bands, a simulation of time series containing Landsat 8 data that does not use the cirrus band shows that omission of cirrus clouds can result in anomalously brighter time series intercepts and positive time trends. Our results indicate that while Landsat 8 has improved on the legacy of previous sensors through increased radiometric resolution, better cloud identification, and better geometric accuracy, difference in reflectance between sensors in the current Landsat CDR product must be corrected or explained within time series analysis to avoid deleterious consequences. Future efforts should identify the contributions of target specific effects versus differences in atmospheric correction methods to better inform approaches to synthesize the two sensors.

An experimental comparison between KELM and CART for crop classification using Landsat-8 OLI data

The operational land imager (OLI) is the latest instrument in the Landsat series of satellite imagery, which officially began normal operations on 30 May 2013. The OLI includes two bands that are not on the thematic mapper series of sensors aboard Landsat-5 and 7; a cirrus band and a coastal/aerosol band. This paper compares the classification and regression tree and the kernel-based extreme learning machine (KELM) for mapping crops in Hokkaido, Japan, using OLI data, except the cirrus band and the pan band. The OLI data acquired on 8 July 2013 was used for crop classification of beans, beets, grassland, maize, potatoes and winter wheat. The KELM algorithm performed better in this study and achieved overall accuracies of 90.1%. According to the Jeffries–Matusita (J–M) distances, the short wavelength infrared band provides the greater contribution (the highest value was observed for band 6 in OLI data).

Evaluating Landsat 8 evapotranspiration for water use mapping in the Colorado River Basin

Abstract Evapotranspiration (ET) mapping at the Landsat spatial resolution (100 m) is essential to fully understand water use and water availability at the field scale. Water use estimates in the Colorado River Basin (CRB), which has diverse ecosystems and complex hydro-climatic regions, will be helpful to water planners and managers. Availability of Landsat 8 images, starting in 2013, provides the opportunity to map ET in the CRB to assess spatial distribution and patterns of water use. The Operational Simplified Surface Energy Balance (SSEBop) model was used with 528 Landsat 8 images to create seamless monthly and annual ET estimates at the inherent 100 m thermal band resolution. Annual ET values were summarized by land use/land cover classes. Croplands were the largest consumer of “blue” water while shrublands consumed the most “green” water. Validation using eddy covariance (EC) flux towers and water balance approaches showed good accuracy levels with R2 ranging from 0.74 to 0.95 and the Nash–Sutcliffe model efficiency coefficient ranging from 0.66 to 0.91. The root mean square error (and percent bias) ranged from 0.48 mm (13%) to 0.60 mm (22%) for daily (days of satellite overpass) ET and from 7.75 mm (2%) to 13.04 mm (35%) for monthly ET. The spatial and temporal distribution of ET indicates the utility of Landsat 8 for providing important information about ET dynamics across the landscape. Annual crop water use was estimated for five selected irrigation districts in the Lower CRB where annual ET per district ranged between 681 mm to 772 mm. Annual ET by crop type over the Maricopa Stanfield irrigation district ranged from a low of 384 mm for durum wheat to a high of 990 mm for alfalfa fields. A rainfall analysis over the five districts suggested that, on average, 69% of the annual ET was met by irrigation. Although the enhanced cloud-masking capability of Landsat 8 based on the cirrus band and utilization of the Fmask algorithm improved the removal of contaminated pixels, the ability to reliably estimate ET over clouded areas remains an important challenge. Overall, the performance of Landsat 8 based ET compared to available EC datasets and water balance estimates for a complex basin such as the CRB demonstrates the potential of using Landsat 8 for annual water use estimation at a national scale. Future efforts will focus on (a) use of consistent methodology across years, (b) integration of multiple sensors to maximize images used, and (c) employing cloud-computing platforms for large scale processing capabilities.

Combined Haze and Cirrus Removal for Multispectral Imagery

Multispectral satellite images are often contaminated by haze and/or cirrus. A previous paper presented a haze removal method that calculates a haze thickness map (HTM) based on a local search of dark objects. The haze-free signal is restored by subtracting the HTM from the hazy image assuming an additive model of the haze influence. The HTM method is substantially improved by employing the 1.38- $\mu\mbox{m}$ cirrus band. The top-of-atmosphere reflectance cirrus band is used as an additional source of information. The method restores the information in highly inhomogeneous surfaces attenuated by a low-altitude haze and high-altitude cirrus, improving the interpretation of the scene content while preserving the shape of the spectral signatures. The new enhanced HTM method is successfully applied to Landsat-8 OLI and Sentinel-2 real and simulated scenes.

Improvement and expansion of the Fmask algorithm: cloud, cloud shadow, and snow detection for Landsats 4–7, 8, and Sentinel 2 images

Identification of clouds, cloud shadows and snow in optical images is often a necessary step toward their use. Recently a new program (named Fmask) designed to accomplish these tasks was introduced for use with images from Landsats 4–7 (Zhu & Woodcock, 2012). In this paper, there are the following: (1) improvements in the Fmask algorithm for Landsats 4–7; (2) a new version for use with Landsat 8 that takes advantage of the new cirrus band; and (3) a prototype algorithm for Sentinel 2 images. Though Sentinel 2 images do not have a thermal band to help with cloud detection, the new cirrus band is found to be useful for detecting clouds, especially for thin cirrus clouds. By adding a new cirrus cloud probability and removing the steps that use the thermal band, the Sentinel 2 scenario achieves significantly better results than the Landsats 4–7 scenario for all 7 images tested. For Landsat 8, almost all the Fmask algorithm components are the same as for Landsats 4–7, except a new cirrus cloud probability is calculated using the new cirrus band, which improves detection of thin cirrus clouds. Landsat 8 results are better than the Sentinel 2 scenario, with 6 out of 7 test images showing higher accuracies.

Radiometric Non-Uniformity Characterization and Correction of Landsat 8 OLI Using Earth Imagery-Based Techniques

Landsat 8 is the first satellite in the Landsat mission to acquire spectral imagery of the Earth using pushbroom sensor instruments. As a result, there are almost 70,000 unique detectors on the Operational Land Imager (OLI) alone to monitor. Due to minute variations in manufacturing and temporal degradation, every detector will exhibit a different behavior when exposed to uniform radiance, causing a noticeable striping artifact in collected imagery. Solar collects using the OLI’s on-board solar diffuser panels are the primary method of characterizing detector level non-uniformity. This paper reports on an approach for using a side-slither maneuver to estimate relative detector gains within each individual focal plane module (FPM) in the OLI. A method to characterize cirrus band detector-level non-uniformity using deep convective clouds (DCCs) is also presented. These approaches are discussed, and then, correction results are compared with the diffuser-based method. Detector relative gain stability is assessed using the side-slither technique. Side-slither relative gains were found to correct streaking in test imagery with quality comparable to diffuser-based gains (within 0.005% for VNIR/PAN; 0.01% for SWIR) and identified a 0.5% temporal drift over a year. The DCC technique provided relative gains that visually decreased striping over the operational calibration in many images.

The Spectral Response of the Landsat-8 Operational Land Imager

Abstract: This paper discusses the pre-launch spectral characterization of the Operational Land Imager (OLI) at the component, assembly and instrument levels and relates results of those measurements to artifacts observed in the on-orbit imagery. It concludes that the types of artifacts observed and their magnitudes are consistent with the results of the pre-launch characterizations. The OLI in-band response was characterized both at the integrated instrument level for a sampling of detectors and by an analytical stack-up of component measurements. The out-of-band response was characterized using a combination of Focal Plane Module (FPM) level measurements and optical component level measurements due to better sensitivity. One of the challenges of a pushbroom design is to match the spectral responses for all detectors so that images can be flat-fielded regardless of the spectral nature of the targets in the imagery. Spectral variability can induce striping (detector-to-detector variation), banding (FPM-to-FPM variation) and other artifacts in the final data products. Analyses of the measured spectral response showed that the maximum discontinuity between FPMs due to spectral filter differences is 0.35% for selected targets for all bands except for Cirrus, where there is almost no signal. The average discontinuity between FPMs is 0.12% for the same targets. These results were expected and are in accordance with the OLI requirements. Pre-launch testing identified low levels (within requirements) of spectral crosstalk amongst the three HgCdTe (Cirrus, SWIR1 and SWIR2) bands of the OLI and on-orbit data confirms this crosstalk in the imagery. Further post-launch analyses and simulations revealed that the strongest crosstalk effect is from the SWIR1 band to the Cirrus band; about 0.2% of SWIR1 signal leaks into the Cirrus. Though the total crosstalk signal is only a few counts, it is evident in some scenes when the in-band cirrus signal is very weak. In moist cirrus-free atmospheres and over typical land surfaces, at least 30% of the cirrus signal was due to the SWIR1 band. In the SWIR1 and SWIR2 bands, crosstalk accounts for no more than 0.15% of the total signal.

新一代Landsat系列卫星：Landsat8遥感影像新增特征及其生态环境意义

The successful launch of Landsat 8 on February 11,2013 has made it possible for the continuity of Landsat earth observation mission once stopped due to the failure of the aged Landsat 5 in late 2011.The satellite has two sensors,the Operational Land Imager(OLI) and the Thermal Infrared Sensor(TIRS),which collect remotely-sensed data over a 185 km swath.Landsat 8 has several new characteristics as well as those of Landsat 7.These include adding two spectral bands(deep blue and cirrus bands),refining spectral range of some bands of Landsat 7 ETM+,splitting one thermal band to two bands,and improving radiometric resolution from 8 bits to 16 bits.This paper aims to examine the new characteristics of the satellite′s image.Image processing and analysis have been carried out on the satellite′s first image acquired on March 18,2013.The results show that(1) the addition of the cirrus band can be useful for the separation of cloud from bright ground objects;(2) the band is also helpful for the distinguishing soil from building materials because the spectral range of the cirrus band is designed within a strong water vapor absorbing zone of the clay minerals;(3) the addition of the deep blue band can be useful for monitoring suspended materials in open waters;(4) the narrowing of spectral range of the panchromatic band can help the differentiation of vegetated and non-vegetated area in the panchromatic image;and(5) the enhancement of radiometric resolution from 8 bits to 16 bits would avoid spectral saturation of the data in extremely dark/light regions.In general,Landsat 8 has technical advantages over Landsat 7 except a lower spatial resolution in thermal data.Its launch and operation can undoubtedly make significant contributions to the morning of global ecosystem and environmental changes.