AVHRR Images Research Articles

Modelling and forecasting the effects of increasing sea surface temperature on coral bleaching in the Indo-Pacific region

ABSTRACT The Coral Triangle (CT) and the South China Sea (SCS) are the world’s great tropical seas, located in the Indo-Pacific (IP) region. It is home to the richest marine ecosystem on Earth, with a total of 76% reef-building coral species as well as 37% coral reef fish species. Unfortunately, this sensitive area is now vulnerable to Sea Surface Temperature (SST) warming. This research explored the possible consequences of SST warming on the rich ecosystems of the IP region, specifically on bleaching of its coral reefs. Reefbase provided coral bleaching records together with the daily NOAA AVHRR Optimum Interpolation (OI) SST V2 dataset (OISSTv2) were used to explore the relationship between coral bleaching and SST in the IP region. Three different categories of monthly mean SST were tested as potential covariates: minimum SST, mean SST and maximum SST, obtained from the OISSTv2. The fitted logistic regression (LR) model revealed a significant and large correlation between coral bleaching and annual maximum monthly mean SST in the study area using the bleaching data from an online database and the time-series of AVHRR images. Predicted maps of coral bleaching based on the LR model were highly consistent with NOAA Coral Reef Watch (CRW) Degree heating Weeks (DHW) maps. However, some important discrepancies resulted from the more specific local fitting used in the LR model. The maximum SST was forecasted from 2020 to 2100 based on the Coupled Model Intercomparison Project Phase 5 (CMIP5) dataset under the Representative Concentration Pathways (RCP2.6) scenario. The fitted logistic regression model was employed to transform the forecasted maximum SST values into maps of the probability of coral bleaching from 2020 to 2100. The results provide considerable cause for concern, including the likelihood of widespread coral bleaching in many places in the IP region over the next 30 years.

Derivation of Wind Vectors from AVHRR/MetOp at EUMETSAT

AbstractAtmospheric motion vectors (AMVs) are derived operationally at EUMETSAT from the AVHRR/3 instrument on the Polar System satellite MetOp-A since 2011. The launch of MetOp-B in 2012 allowed for doubling of the production of AMVs over the polar regions using both MetOp-A and MetOp-B satellite data. In addition to the single AVHRR polar wind product, in 2014 EUMETSAT developed a new global AVHRR wind product extracted from a pair of MetOp-A and MetOp-B images. This new product is extracted using the large overlap in the imagery data obtained from the tandem configuration of the two satellites on the same orbital plane but with a phase difference of about 50 min. The tandem configuration also provides the possibility to derive wind vectors over polar areas using a triplet of AVHRR images, keeping the same time period necessary to derive the single MetOp polar wind product but allowing for a temporal consistency check in the calculation of the AMV quality index. Three different AMV products are currently extracted from AVHRR imagery at EUMETSAT, using two or three images taken by one or two satellites having different coverage and time integration.This paper describes the scientific concept of the AVHRR wind extraction algorithm developed at EUMETSAT and presents the performances of the various AVHRR wind products. Intercomparisons of these different products highlight the role of the temporal gap between the images used to extract the wind and the impact of the consistency check on the calculation of the quality index.

Regional-scale winter wheat phenology monitoring using multisensor spatio-temporal fusion in a South Central China growing area

Monitoring crop phenology is essential for evaluating crop productivity and crop management. Remote sensing provides an efficient way to monitor crop phenological metrics at a large-scale. However, the widely used AVHRR and MODIS images are less reliable at a small-scale and in areas with heterogeneous land covers, such as the patchy cropland in South Central China. Therefore, this study analyzed the spatial and temporal variations of winter wheat phenology in South Central China, using enhanced vegetation index (EVI) time series predicted by a spatio-temporal fusion method that combines information from Landsat and MODIS images. The 13-year predicted EVI showed a close correspondence with the EVI derived from original Landsat images. Start of season (SOS), peak greenness, and end of season (EOS) were derived from the predicted EVI time series. The comparison with ground observations showed that the differences between the predicted phenological metrics and observations were usually within seven days. The length of the growing season demonstrated high spatial heterogeneity over the study area and the spatial patterns varied from year to year. The phenological dates did not show obvious increasing or decreasing trends through 13 years. The length of the growing season in the study area was positively correlated with precipitation, but the duration from SOS to peak greenness and the duration from peak greenness to EOS were strongly and negatively correlated with hours of sunshine.

New method and error analysis of lake retrieval with MetOp-A AVHRR images on the Tibetan Plateau

Land-surface water is an important factor influencing the regional environment and climate and is a key factor in the Tibetan Plateau, which is one of the most sensitive regions to global changes. ...

TREND ASSESSMENT OF SPATIO-TEMPORAL CHANGE OF TEHRAN HEAT ISLAND USING SATELLITE IMAGES

Abstract. Numerous investigations on Urban Heat Island (UHI) show that land cover change is the main factor of increasing Land Surface Temperature (LST) in urban areas, especially conversion of vegetation and bare soil to concrete, asphalt and other man-made structures. On the other hand, other human activities like those which cause to burning fossil fuels, that increase the amount of carbon dioxide, may raise temperature in global scale in comparison with small scales (urban areas). In this study, multiple satellite images with different spatial and temporal resolutions have been used to determine Land Surface Temperature (LST) variability in Tehran metropolitan area. High temporal resolution of AVHRR images have been used as the main data source when investigating temperature variability in the urban area. The analysis shows that UHI appears more significant at afternoon and night hours. But the urban class temperature is almost equal to its surrounding vegetation and bare soil classes at around noon. It also reveals that there is no specific difference in UHI intense during the days throughout the year. However, it can be concluded that in the process of city expansion in years, UHI has been grown both spatially and in magnitude. In order to locate land-cover types and relate them to LST, Thematic Mapper (TM) images have been exploited. The influence of elevation on the LST has also been studied, using digital elevation model derived from SRTM database.

Evaluation of future climate change impact on snow hydrology for a mountainous watershed of South Korea using SLURP model and NOAA AVHRR images

This study is to evaluate the future potential climate impact on snow hydrology using SLURP model for a 6661.0 km2 mountainous watershed of South Korea. For the model test, the NOAA AVHRR images were analyzed to prepare snow-related data of the model. Snow cover areas were extracted using channels 1, 3, and 4, and the snow depth was spatially interpolated using snowfall data of 11 ground meteorological stations. With the snowmelt parameters (snow cover area, snow water equivalent, and snow depth), the model was calibrated for 2 sets (2002–2003, 2004–2005), and verified for 2 sets (1997–1998 and 2001–2002) using the calibrated parameters. The average Nash–Sutcliffe efficiencies during the full year period (December to November) and snowmelt period (December to April) were 0.60 and 0.66, respectively. The future climate data of CCCma CGCM2 SRES A2 and B2 scenarios were adjusted and downscaled using change factor method. By the future impact of climate change, the annual dam inflows were projected to change maximum −29.3 and −30.4 % for 2090s A2 scenario and 2030s for B2 scenario, respectively. The future dam inflow increased in winter season (December to February) up to 222.0 %, while other periods decreased up to 54.8 %. The future snowmelt increased in December and January by the future temperature increase of 3.9 °C in minimum. The future snowmelt for the 2 months affected the dam inflows during the winter season.

A Case Study of Polar Low Detection Using ERS-2 Wave Mode Image

An empirical approach for detecting polar lows is developed based on satellite data from two sensors: Synthetic aperture radar (SAR) wave mode image on board ERS-2 satellite and AVHRR image on board the National Oceanic and Atmospheric Administration (NOAA) family of polar orbiting platforms (i. e. POES). This approach is primarily based on 10m wind field and wave height besides the latitudinal position. Based on these criteria, the polar low systems could potentially be detected using CWAVE algorithm (7) for practical applications. A probable approach for this purpose is discussed in this study through an example.

Quantifying the Relationship Between Intersensor Images in Solar Reflective Bands: Implications for Intercalibration

Satellite instruments have acquired large volume images at different spatial, spectral, radiometric, and temporal resolutions. Reliable detection of long-term environmental change requires critical sensor intercalibration destined to minimize inconsistency in these multisensor images. However, uncertainty in intercalibration has not yet been comprehensively quantified in most existing studies. This paper developed a quantitative relationship between multisensor images in solar reflective bands by accounting for sensor difference, atmospheric condition, and Sun-target-sensor geometry. The relationship was validated with collocated and concurrent TERRA MODIS/NOAA-17 AVHRR images over the Dunhuang calibration site. Then, it was used to investigate sensitivity of intercalibration to intersensor scale factor, total ozone concentration (TOC), total precipitable water vapor content (TPW), and aerosol optical thickness (AOT). The main conclusions include: 1) error in intersensor scale factor may induce a maximum uncertainty of 2.35% for both visible (VIS) and near-infrared (NIR) bands; 2) error in TOC can produce a maximum uncertainty of 0.30% for VIS band but very minor impact on NIR band; 3) error in TPW may generate a maximum uncertainty of 8.81% for NIR band, particularly for a dry atmosphere; 4) error in AOT can result in a maximum uncertainty of 0.25% for VIS band and 0.96% for NIR band at near-nadir, and 10.16% and 8.18% for heavy aerosol loadings at a very high solar angle. The following study quantifies uncertainties in intercalibration for solar reflective bands and thus offers guidance for intercalibration.

Mapping crop phenology using NDVI time-series derived from HJ-1 A/B data

With the availability of high frequent satellite data, crop phenology could be accurately mapped using time-series remote sensing data. Vegetation index time-series data derived from AVHRR, MODIS, and SPOT-VEGETATION images usually have coarse spatial resolution. Mapping crop phenology parameters using higher spatial resolution images (e.g., Landsat TM-like) is unprecedented. Recently launched HJ-1 A/B CCD sensors boarded on China Environment Satellite provided a feasible and ideal data source for the construction of high spatio-temporal resolution vegetation index time-series. This paper presented a comprehensive method to construct NDVI time-series dataset derived from HJ-1 A/B CCD and demonstrated its application in cropland areas. The procedures of time-series data construction included image preprocessing, signal filtering, and interpolation for daily NDVI images then the NDVI time-series could present a smooth and complete phenological cycle. To demonstrate its application, TIMESAT program was employed to extract phenology parameters of crop lands located in Guanzhong Plain, China. The small-scale test showed that the crop season start/end derived from HJ-1 A/B NDVI time-series was comparable with local agro-metrological observation. The methodology for reconstructing time-series remote sensing data had been proved feasible, though forgoing researches will improve this a lot in mapping crop phenology. Last but not least, further studies should be focused on field-data collection, smoothing method and phenology definitions using time-series remote sensing data.

The effect of precipitation on vegetation cover over three landscape units in a protected semi-arid grassland: Temporal dynamics and suitable climatic index

The principal drivers that effectively determine the vitality of semi-arid grassland ecosystems are the amount and timing of precipitation. In contrast to well-established relationships between precipitation and some vegetation biophysical properties such as vegetation productivity and biomass, the effect of precipitation on percent vegetation cover in semi-arid grasslands are poorly understood. The percent green vegetation cover is an important indicator of ecosystem health, especially in a protected semi-arid grassland where dead material dominates vegetation canopy. Consequently, this study used field data, high spatial resolution SPOT satellite images, and low spatial but high temporal resolution AVHRR images to (1) analyze temporal variations in maximum percent green vegetation cover over three landscape units in a protected grassland from 1988 to 2007, and (2) investigate the performance of three commonly used climatic moisture indexes (accumulated precipitation – P; climate moisture index – CMI; and the modified Palmer drought severity index – MPDSI) in estimating percent vegetation cover. The results revealed moderate to strong links between percent vegetation cover and climate conditions in three landscape units. The selected indices were able to explain a relatively high proportion of variation in percent vegetation cover in the upland/sloped grassland (R2=0.41 for P, 0.45 for CMI, and 0.53 for MPDSI), low variation in the riparian shrub (R2=0.25 for P, 0.28 for CMI, and 0.32 for MPDSI), and even lower variation in the valley grassland (R2=0.09 for P, 0.14 for CMI, and 0.21 for MPDI). In comparison with accumulated precipitation, the CMI and MPDSI which considered water availability, were able to explain a much higher variation in percent vegetation cover over the three grassland landscape units. The results from this work provide new insight into the effects of precipitation/climatic factors upon different landscape units and further aid to establish a more complete picture in this field.

Study of Snow Depletion Characteristics at Two Mountainous Watersheds Using NOAA AVHRR Time Series Data

Spatial information of snow cover and depth distribution is a key component for snowmelt runoff modeling. Wide snow cover areas can be extracted from NOAA AVHRR or Terra MODIS satellite images. In this study eight sets of annual snow cover data (1997-2006) in two mountainous watersheds (A: Chungju-Dam and B: Soyanggang-Dam) were extracted using NOAA AVHRR images. The distribution of snow depth within the Snow Cover Area (SCA) was generated using snowfall data from ground meteorological observation stations. Snow depletion characteristics for the two watersheds were analyzed snow distribution time series data. The decreased pattern of SCA can be expressed as a logarithmic function; the determination coefficients were 0.62 and 0.68 for the A and B watersheds, respectively. The SCA decreased over 70% within 10 days from the time of maximum SCA.

Optimal thermal water locations along the Gulf of Suez coastal zones, Egypt

Remote sensing and GIS techniques were utilized to specify criteria characterizing thermal water localities along the Gulf of Suez coastal zones. Surface temperatures were extracted from ETM+ (band 6) and AVHRR image data. Specific sites were characterized by high surface temperatures: 31–34 °C at Ayun Musa, 31–37 °C at Ain El Sukhna, and 34–43 °C at Hammam Faraun. The results were consistent with measurements taken during field visits. These springs are located in regions of tectonic heating due to the opening of the Red Sea and Gulf of Suez rifts. The heat for these springs is probably derived from high heat flow and deep circulation controlled by faults associated with rift opening. Data fusion was applied to ETM+ and ASTER images from 2000 to extract information about lineaments and major structures controlling hot spring distribution and geothermal localities in the Gulf of Suez area. The major geologic structures were oriented to NNW–SSE-trending faults. Principal component analyses and image classification techniques were applied to construct rock unit maps. The hot spring sites are covered by wadi deposits and surrounded by fractured Eocene limestone. The thermal water areas are characterized by low topography and high-order drainage patterns flowing toward the Gulf of Suez coastal zones. The results of this study indicate that localities most likely to contain thermal water along the Gulf of Suez are covered with fractured Eocene limestone and characterized by low topography, high-order drainage patterns, and surface temperatures ranging from 34 °C to 43 °C. These criteria can help in predicting the locations of other hot spring areas and selecting optimum sites for the generation of thermal energy.

An Object-Based Method for Contrail Detection in AVHRR Satellite Images

Contrails are important in understanding how aviation potentially affects climate change. The commonly used methods for detecting contrails usually only consider the spectral information of the image. This study focuses on introducing an object-based method for detecting contrails in AVHRR images. An object-based method utilizes not only the spectral characteristics, but also spatial information to aid in the detection. The results of the experimental study demonstrate that it is practical to use an object-based classification method for contrail detection. Compared with pixel-based classification methods, the accuracy of the results is improved using the object-based method.

Estimation of agricultural water productivity in Qazvin plain using modis and Avhrr images

The world has finite water resources, which are under increasing stress as the human population and water demand per capita both increase. These problems are not new but are now becoming more widespread and their impacts more devastating. This has provided additional impetus for the search for solutions to problems arising from the mismatch between demand and supply in terms of water quantity, quality and timing. Increasing water productivity has been identified as one of the global challenges that require urgent attention.This study examines approaches to assessing water productivity in agriculture. Many studies have been carried out on agricultural water productivity [WP] in the world, where most of these studies, specifically in relation to water and food policies of WP, are considered in regional or watershed scale. There are many methods of calculating evapotranspiration (ETc) on regional scale but remote sensing technique since last decade of this century has become more popular one. SEBAL algorithm was used in this research to calculate ETc. Last versions of SEBAL algorithm can calculate aerial crop biomass and then can calculate crop water productivity. In this research, five cloudless MODIS and AVHRR images from Qazvin plain which is located in the center of Iran were selected, and then water productivity calculated with SEBAL algorithm, which runs with ENVI and ILWIS models on MODIS and AVHRR images. Amount of ETc, which was estimated from SEBAL, compared with lysimeter data with r-square [R2] value of 0.9 for MODIS images and 0.81 for AVHRR images. Estimated ETc was higher than lysimeter data in AVHRR and lower than it in MODIS images. Water productivity was estimated 0.76 and 1.03 kg/m3 in MODIS and AVHRR images respectively, but is 0.9 kg/m3 in lysimeter.

An intelligent wavelet transform-based framework to detect subsurface fires with NOAA–AVHRR images

Subsurface coal fires (in this article, termed as hotspots), responsible for atmospheric pollution, human fatalities and perilous land subsidence, pose a big threat to major coal-producing countries in the world. The majority of the research performed to date has focused on providing hotspot allocation information for a specific region of interest and most has explored quite expensive high-resolution Landsat Thematic Mapper (TM) satellite images for the same. This article aims to investigate the applicability of a wavelet transform-based model to detect subsurface fires (hotspots) with freely available National Oceanic and Atmospheric Administration/Advanced Very High Resolution Radiometer (NOAA–AVHRR) images and find out the possibility of extracting novel hotspot features by applying a wavelet transform-based analysis technique. The proposed feature vector consists of wavelet variance coefficients (WVCs) obtained from scale-by-scale decomposition of the AVHRR image variance and builds up a strong base for designing an accurate classification system. Furthermore, the support vector machine (SVM), an efficient machine learning tool, is applied to the proposed feature vector in order to develop a classification model. The demonstrated results successfully prove the effectiveness of the proposed framework as the classified images show a good correspondence with records of subsurface fires mapped by the Bharat Coking Coal Limited (BCCL), India. The effectiveness of the SVM method is also evaluated in comparison with the classical neural network-based approach.

Gulf Stream thermal fronts detected by synthetic aperture radar

Our purpose is to detect ocean surface features, specifically oceanic thermal fronts, through analysis of SAR (synthetic aperture radar)‐derived wind stress fields. Fine‐resolution measurements of near‐surface wind speeds over the Gulf Stream region of the Northwest Atlantic were made using SAR images collected by RADARSAT‐2. Linear statistical relationships between the wind stress curl and divergence to the crosswind and downwind components of the SST gradient field were used to derive a new method for detecting Gulf Stream thermal fronts from Synthetic Aperture Radar (SAR) imagery. In particular, sea surface temperature front features, as suggested by corresponding AVHRR and MODIS images, are evident in both of the wind stress curl and divergence fields. The importance of this methodology is that clouds are often present in the Northwest Atlantic in autumn and winter months and they obscure AVHRR and MODIS images, whereas SAR can penetrate clouds.

Satellite detection of carbon monoxide emission prior to the Gujarat earthquake of 26 January 2001

NOAA AVHRR images have clearly shown anomalous changes in land surface temperature associated with earthquakes in the past two decades. Soon after the Gujarat earthquake of January 26, 2001, an anomalous increase in land surface temperature was inferred from MODIS satellite data a few days prior to the main earthquake event. The cause of such an anomalous change in surface temperature prior to the earthquake is attributed to many probable phenomena, but no definite cause has been identified. In the present study, changes of a complementary nature were found of land surface temperature associated with the emission of CO from the epicentral region. The observed changes on land and atmosphere associated with the Gujarat earthquake of 26 January, 2001, show the existence of strong coupling between land, atmosphere and ionosphere.

Further evidences of deep convective vertical transport of water vapor through the tropopause

A few years ago, we identified a deep convective transport mechanism, of water vapor through the tropopause, namely, storm top gravity wave breaking, such that tropospheric water substance can be injected into the lower stratosphere via this pathway. The main evidence presented previously was taken from the lower resolution AVHRR images of the storm anvil top cirrus plumes obtained by polar orbiting satellites. Recent observations have provided further supporting evidence for this important cross-tropopause transport mechanism. There are now many higher resolution satellite images, mainly from MODIS instrument, that show more definitely the existence of these plumes, many of which would probably be unseen by lower resolution images. Furthermore, a thunderstorm movie taken in Denver (USA) area during STEPS2000 field campaign and another thunderstorm movie taken by a building top webcam in Zurich also demonstrate that the jumping cirrus phenomenon, first identified by T. Fujita in 1980s, may be quite common in active thunderstorm cells, quite contrary to previous belief that it is rare. We have used a cloud model to demonstrate that the jumping cirrus is exactly the gravity wave breaking phenomenon that transports water vapor through the tropopause. These additional evidences provide increasing support that deep convection contributes substantially to the troposphere-to-stratosphere transport of water substance. This corroborates well with recent studies of the stratospheric HDO/H 2O ratio which is much highly than it would be if the transport is via slow ascent. The only explanation that can be used to interpret this observation at present is that water substance is transported through the tropopause via rapid vertical motion, i.e., deep convection.

Cloud filtration technique for the Black-Sea AVHRR data

We consider a version of the procedure of cloudiness recognition in the AVHRR images regionally adapted for the Black-Sea region. The threshold algorithms using the specific features of the formation of upward radiation in different spectral ranges are taken as basic. In studying the data obtained for the Black Sea, we analyze and modify the well-known algorithms of detection of the cloudy situations in the day and night time. The efficiency and disadvantages of the developed method are estimated by analyzing the results of processing of the satellite data obtained in 2005 and 2006.

Molecular and isotopic constraints on the sources of suspended particulate organic carbon on the northwestern Atlantic margin

The abundance, carbon isotopic composition (Δ 14C and δ 13C), and lipid biomarker (alkenones and saturated fatty acids) distributions of suspended particulate organic matter were investigated at three stations centered on the 2000, 3000, and 3500 m isobaths over the New England slope in order to assess particulate carbon sources and dynamics in this highly productive and energetic region. Transmissometry profiles reveal that particle abundances exhibit considerable fine structure, with several distinct layers of elevated suspended particulate matter concentration at intermediate water depths in addition to the presence of a thick bottom nepheloid layer at each station. Excluding surface water samples, the Δ 14C values of particulate organic carbon (POC) indicated the presence of a pre-aged component in the suspended POC pool (Δ 14C<+38‰). The Δ 14C values at the 3000 m station exhibited greater variability and generally were lower than those at the other two stations where the values decreased in a more systematic matter with increasing sampling depth. These lower Δ 14C values were consistent with higher relative abundances of terrigenous long-chain fatty acids at this station than at the other two stations. Two scenarios were considered regarding the potential provenances of laterally transported POC: cross-shelf transport of shelf sediment (Δ 14C=−140‰) and along-slope transport of the slope sediment proximal to the sampling locations (Δ 14C=−260‰). Depending on the scenario, isotopic mass balance calculations indicate allochthonous POC contributions ranging between 15% and 54% in the meso- and bathy-pelagic zone, with the highest proportions at the 3000 m station. Alkenone-derived temperatures recorded on suspended particles from surface waters closely matched in-situ temperatures at each station. However, alkenone-derived temperatures recorded on particles from the subsurface layer down to 250 m were lower than those of overlying surface waters, especially at the 3000 m station, implying supply of phytoplankton organic matter originally produced in cooler surface waters. AVHRR images and temperature profiles indicate that the stations were under the influence of a warm-core ring during the sampling period. The low alkenone-derived temperatures in the subsurface layer coupled with the lower Δ 14C values for the corresponding POC suggests supply of OC on resuspended sediments underlying cooler surface waters distal to the study area, possibly further north or west. Taken together, variations in Δ 14C values, terrigenous fatty acid abundances, and alkenone-derived temperatures among the stations suggest that input of laterally advected OC is a prominent feature of POC dynamics on the NW Atlantic margin, and is spatially heterogeneous on a scale smaller than the distance between the stations (<150 km).