SeaWiFS Technical Report Series. Volume 7: Cloud screening for polar orbiting visible and infrared (IR) satellite sensors

Methods for detecting and screening cloud contamination from satellite derived visible and infrared data are reviewed in this document. The methods are applicable to past, present, and future polar orbiting satellite radiometers. Such instruments include the Coastal Zone Color Scanner (CZCS), operational from 1978 through 1986; the Advanced Very High Resolution Radiometer (AVHRR); the Sea-viewing Wide Field-of-view Sensor (SeaWiFS), scheduled for launch in August 1993; and the Moderate Resolution Imaging Spectrometer (IMODIS). Constant threshold methods are the least demanding computationally, and often provide adequate results. An improvement to these methods are the least demanding computationally, and often provide adequate results. An improvement to these methods is to determine the thresholds dynamically by adjusting them according to the areal and temporal distributions of the surrounding pixels. Spatial coherence methods set thresholds based on the expected spatial variability of the data. Other statistically derived methods and various combinations of basic methods are also reviewed. The complexity of the methods is ultimately limited by the computing resources. Finally, some criteria for evaluating cloud screening methods are discussed

Sea surface temperature distribution in the Azores region. Part I: AVHRR imagery and in situ data processing.

Sixteen months of 1.1 km resolution NOAA-12, -14, and -16 data for the Azores region are investigated. Advanced Very High Resolution Radiometer (AVHRR) derived sea surface temperature (SST) is compared to an extensive in situ temperature measurement database, mainly constituted during fisheries campaigns. This comparison shows that SST maps include numerous pixels with temperature values below the range observed for the Azores. Low temperatures are attributed in literature to pixel contamination by cloud neighbouring and these are usually removed by eroding pixels around clouds. Results of this study show that running an erosion filter removes only two thirds of the contaminated pixels. Remnant clouds are filtered inputting threshold values to SST 8-day temperature histograms. Based on a comparison of the SST values derived on an image-by-image basis, it is also demonstrated that differences among the sensors are lower than the measurement accuracy, whilst, on the contrary, nighttime and daytime SST distributions are statistically different. Based on monthly and 15-day average computations at nighttime, AVHRR-derived SST distribution in the Azores and associated dominant space and time scales are proposed in the second part of this paper (SST distribution in the Azores region. Part II: Space and time variability and its relation to North Atlantic Oscillation)

Sea surface temperature distribution in the Azores region. Part I: AVHRR imagery and in situ data processing.

Sixteen months of 1.1 km resolution NOAA-12, -14, and -16 data for the Azores region are investigated. Advanced Very High Resolution Radiometer (AVHRR) derived sea surface temperature (SST) is compared to an extensive in situ temperature measurement database, mainly constituted during fisheries campaigns. This comparison shows that SST maps include numerous pixels with temperature values below the range observed for the Azores. Low temperatures are attributed in literature to pixel contamination by cloud neighbouring and these are usually removed by eroding pixels around clouds. Results of this study show that running an erosion filter removes only two thirds of the contaminated pixels. Remnant clouds are filtered inputting threshold values to SST 8-day temperature histograms. Based on a comparison of the SST values derived on an image-by-image basis, it is also demonstrated that differences among the sensors are lower than the measurement accuracy, whilst, on the contrary, nighttime and daytime SST distributions are statistically different. Based on monthly and 15-day average computations at nighttime, AVHRR-derived SST distribution in the Azores and associated dominant space and time scales are proposed in the second part of this paper (SST distribution in the Azores region. Part II: Space and time variability and its relation to North Atlantic Oscillation)

A self-sufficient approach for GERB cloudy radiance detection.

Geostationary Earth Radiation Budget (GERB) is the broadband radiometer onboard the Meteosat Second Generation (MSG) platform, launched at the end of August 2002 and still in commissioning phase. GERB data is planned to be used in many applications concerning Earth Radiation Budget (ERB) calculation. In order to evaluate the impact of clouds on ERB, a cloud detection is required and, at present, a cloud mask based on higher spatial and spectral resolution data acquired by Spinning Enhanced Visible and Infrared Imager (SEVIRI), the imager onboard the same MSG platform, is planned to be used in order to identify cloudy GERB soundings. As an alternative, a self-sufficient (only based on GERB data) method (OCA, the One-channel Cloudy-radiance-detection Approach) is proposed, as a time-saving and, probably, more suitable solution than the planned co-location approach. In this paper, preliminary results obtained by using several years of Meteosat data as well as GERB synthetic radiances (produced from Meteosat-7 observations) are presented. It is shown how results obtained by using GERB data alone can be comparable (and better in terms of number and spatial distribution of clear-sky GERB soundings identified) to the ones achieved if the co-location of a higher resolution cloud mask is use

MODIS: Moderate-resolution imaging spectrometer. Earth observing system, volume 2B

The Moderate-Resolution Imaging Spectrometer (MODIS), as presently conceived, is a system of two imaging spectroradiometer components designed for the widest possible applicability to research tasks that require long-term (5 to 10 years), low-resolution (52 channels between 0.4 and 12.0 micrometers) data sets. The system described is preliminary and subject to scientific and technological review and modification, and it is anticipated that both will occur prior to selection of a final system configuration; however, the basic concept outlined is likely to remain unchanged

Mapping and monitoring forest remnants : a multiscale analysis of spatio-temporal data

KEYWORDS : Landsat, time series, machine learning, semideciduous Atlantic forest, Brazil, wavelet transforms, classification, change detectionForests play a major role in important global matters such as carbon cycle, climate change, and biodiversity. Besides, forests also influence soil and water dynamics with major consequences for ecological relations and decision-making. One basic requirement to quantify and model these processes is the availability of accurate maps of forest cover. Data acquisition and analysis at appropriate scales is the keystone to achieve the mapping accuracy needed for development and reliable use of ecological models.The current and upcoming production of high-resolution data sets plus the ever-increasing time series that have been collected since the seventieth must be effectively explored. Missing values and distortions further complicate the analysis of this data set. Thus, integration and proper analysis is of utmost importance for environmental research. New conceptual models in environmental sciences, like the perception of multiple scales, require the development of effective implementation techniques.This thesis presents new methodologies to map and monitor forests on large, highly fragmented areas with complex land use patterns. The use of temporal information is extensively explored to distinguish natural forests from other land cover types that are spectrally similar. In chapter 4, novel schemes based on multiscale wavelet analysis are introduced, which enabled an effective preprocessing of long time series of Landsat data and improved its applicability on environmental assessment.In chapter 5, the produced time series as well as other information on spectral and spatial characteristics were used to classify forested areas in an experiment relating a number of combinations of attribute features. Feature sets were defined based on expert knowledge and on data mining techniques to be input to traditional and machine learning algorithms for pattern recognition, viz . maximum likelihood, univariate and multivariate decision trees, and neural networks. The results showed that maximum likelihood classification using temporal texture descriptors as extracted with wavelet transforms was most accurate to classify the semideciduous Atlantic forest in the study area.In chapter 6, a multiscale approach to digital change detection was developed to deal with multisensor and noisy remotely sensed images. Changes were extracted according to size classes minimising the effects of geometric and radiometric misregistration.Finally, in chapter 7, an automated procedure for GIS updating based on feature extraction, segmentation and classification was developed to monitor the remnants of semideciduos Atlantic forest. The procedure showed significant improvements over post classification comparison and direct multidate classification based on artificial neural networks.</p

Does the Madden-Julian Oscillation influence aerosol variability?

We investigate the modulation of aerosols by the Madden-Julian Oscillation (MJO) using multiple, global satellite aerosol products: aerosol index (AI) from the Total Ozone Mapping Spectrometer (TOMS) on Nimbus-7, and aerosol optical thickness (AOT) from the Moderate Resolution Imaging Spectroradiometer (MODIS) on Terra and Aqua and the Advanced Very High Resolution Radiometer (AVHRR) on NOAA satellites. A composite MJO analysis indicates that large variations in the TOMS AI and MODIS/AVHRR AOT are found over the equatorial Indian and western Pacific Oceans where MJO convection is active, as well as the tropical Africa and Atlantic Ocean where MJO convection is weak but the background aerosol level is high. A strong inverse linear relationship between the TOMS AI and rainfall anomalies, but a weaker, less coherent positive correlation between the MODIS/AVHRR AOT and rainfall anomalies, were found. The MODIS/AVHRR pattern is consistent with ground-based Aerosol Robotic Network data. These results indicate that the MJO and its associated cloudiness, rainfall, and circulation variability systematically influence the variability in remote sensing aerosol retrieval results. Several physical and retrieval algorithmic factors that may contribute to the observed aerosol-rainfall relationships are discussed. Preliminary analysis indicates that cloud contamination in the aerosol retrievals is likely to be a major contributor to the observed relationships, although we cannot exclude possible contributions from other physical mechanisms. Future research is needed to fully understand these complex aerosol-rainfall relationships

User's guide to image processing applications of the NOAA satellite HRPT/AVHRR data. Part 1: Introduction to the satellite system and its applications. Part 2: Processing and analysis of AVHRR imagery

The use of NOAA Advanced Very High Resolution Radar/High Resolution Picture Transmission (AVHRR/HRPT) imagery for earth resource applications is provided for the applications scientist for use within the various Earth science, resource, and agricultural disciplines. A guide to processing NOAA AVHRR data using the hardware and software systems integrated for this NASA project is provided. The processing steps from raw data on computer compatible tapes (1B data format) through usable qualitative and quantitative products for applications are given. The manual is divided into two parts. The first section describes the NOAA satellite system, its sensors, and the theoretical basis for using these data for environmental applications. Part 2 is a hands-on description of how to use a specific image processing system, the International Imaging Systems, Inc. (I2S) Model 75 Array Processor and S575 software, to process these data

Analysis of Long-Term Cloud Cover, Radiative Fluxes, and Sea Surface Temperature in the Eastern Tropical Pacific

Grant activities accomplished during this reporting period are summarized. The contributions of the principle investigator are reported under four categories: (1) AHVRR (Advanced Very High Resolution Radiometer) data; (2) GOES (Geostationary Operational Environ Satellite) data; (3) system software design; and (4) ATSR (Along Track Scanning Radiometer) data. The contributions of the associate investigator are reported for:(1) longwave irradiance at the surface; (2) methods to derive surface short-wave irradiance; and (3) estimating PAR (photo-synthetically active radiation) surface. Several papers have resulted. Abstracts for each paper are provided