Spatial Resolution Satellite Data Research Articles

Examining the potential of using remotely sensed fire data to predict areas of rapid forest change in South America

Major advances have been made in the last decades estimating deforestation by using satellite data. This has been mostly done by detecting differences in a time series of high resolution data. However, the interpretation of the images and the detection of change is cumbersome and expensive. Furthermore, the area covered by a high resolution image is relatively small and only available on a monthly bases. This work investigates if detected fire of the low resolution (AVHRR) sensor can be used as a quick and inexpensive way of locating areas of rapid forest cover change. It tests the hypothesis that vegetation fires, as detected by earth orbiting satellites, are a reliable indicator of “hot spots” of deforestation in tropical South America. Fine spatial resolution satellite data were used to measure the deforestation that occurred in the mid 1990s in 41 sites spread across the tropical forests of South America. These data were then compared to the number of fires occurring in these sites, as detected by the NOAA-AVHRR satellite between April 1992 and December 1993. At the individual site level, 25 of the test sites showed a significant statistical relationship between the fires and the deforestation occurring in the following years. A further 6 sites showed neither fire activity nor significant deforestation. When aggregated to the regional level, only the set of sites lying in relatively dry areas with the same land use practices (located in Brazil) showed a significant correlation between fire and deforestation. This correlation enabled us to produce a map of high deforestation risk occurring in 1992 in the Brazilian Amazon, which showed close correspondence to the actual deforestation fronts of the year 1992.

Estimation of methane emission from West Siberian wetland by scaling technique between NOAA AVHRR and SPOT HRV

Wetlands are one of the most important ecosystems in the world and at the same time they are presumed to be a source of methane gas, which is one of the most important greenhouse gases. The West Siberian wetlands is the largest in the world and remote sensing techniques can play an important role for monitoring the wetland. High spatial resolution satellite data are effective for monitoring land cover type changes, but can't cover a wide area because of a narrow swath width. On the other hand, global scale data are indispensable in covering a large area, but are too coarse to get the detailed information due to the low spatial resolution. It is necessary to devise a method for the fusion of the data with different spatial resolutions for monitoring the scale-differed phenomena. In this paper, firstly, a SPOT HRV image near Plotnikovo mire was used to map four wetland ecosystems (birch forest, conifer forest, forested bog and open bog) supplemented by field observation. Then, spectral mixture analysis was performed between NOAA AVHRR and SPOT HRV data acquired on the same day. Secondly, field observations were scaled up with these different spatial resolution satellite data. Each of the wetland ecosystem coverage fraction at the sub-pixel level was provided by spectral mixture analysis. Field observation shows that the mean rate of CH 4 emission from forested bog and open bog averaged 21.1 and 233.1 (mg CH 4/m 2/day), respectively. The methane emission from the area was estimated by multiplying these average methane emission rates and the fraction coverage in each AVHRR pixel. Finally, the total methane emission over AVHRR coverage was estimated to be 9.46 (10 9 g CH 4/day) and the mean methane emission over AVHRR coverage was calculated as 59.3 (mg CH 4/m 2/day). We could conclude that this mean value is within the probabilistic variability as compared with the airborne measurement results.

Burned area mapping using multi-temporal moderate spatial resolution data—a bi-directional reflectance model-based expectation approach

While remote sensing offers the capability for monitoring land surface changes, extracting the change information from satellite data requires effective and automated change detection techniques. The majority of change detection techniques rely on empirically derived thresholds to differentiate changes from background variations, which are often considered noise. Over large areas, reliable threshold definition is problematic due to variations in both the surface state and those imposed by the sensing system. A new approach to change detection, applicable to high-temporal frequency satellite data, that maps the location and approximate day of change occurrence is described. The algorithm may be applied to a range of change detection applications by using appropriate wavelengths. The approach is applied here to the problem of mapping burned areas using moderate spatial resolution satellite data. A bi-directional reflectance model is inverted against multi-temporal land surface reflectance observations, providing an expectation and uncertainty of subsequent observations through time. The algorithm deals with angular variations observed in multi-temporal satellite data and enables the use of a statistical measure to detect change from a previously observed state. The algorithm is applied independently to geolocated pixels over a long time series of reflectance observations. Large discrepancies between predicted and measured values are attributed to change. A temporal consistency threshold is used to differentiate between temporary changes considered as noise and persistent changes of interest. The algorithm is adaptive to the number, viewing and illumination geometry of the observations, and to the amount of noise in the data. The approach is demonstrated using 56 days of Moderate Resolution Imaging Spectroradiometer (MODIS) land surface reflectance data generated for Southern Africa during the 2000 burning season. Qualitatively, the results show high correspondence with contemporaneous MODIS active fire detection results and reveal a coherent spatio-temporal progression of burning. Validation of these results is in progress and recommendations for future research, pending the availability of independent validation data sets, are made. This approach is now being considered for the MODIS burned area algorithm.

Remote sensing methods in medium spatial resolution satellite data land cover classification of large areas

Numerous large-area, multiple image-based, multiple sensor land cover mapping programs exist or have been proposed, often within the context of national forest monitoring, mapping and modelling initiatives, worldwide. Common methodological steps have been identified that include data acquisition and preprocessing, map legend development, classification approach, stratification, incorporation of ancillary data and accuracy assessment. In general, procedures used in any large-area land cover classification must be robust and repeatable; because of data acquisition parameters, it is likely that compilation of the maps based on the classification will occur with original image acquisitions of different seasonality and perhaps acquired in different years and by different sensors. This situation poses some new challenges beyond those encountered in large-area single image classifications. The objective of this paper is to review and assess general medium spatial resolution satellite remote sensing land cover classification approaches with the goal of identifying the outstanding issues that must be overcome in order to implement a large-area, land cover classification protocol.

The Use of DEM and Satellite Data for Regional Scale Soil Databases

New, quantitative methods and data sources for characterizing small scale soil resources have been demonstrated. AVHRR and coarse spatial resolution DEM were designed for mapping large areas of the world quickly and cost effectively. The method combines digital elevation data, “ground truth” information, including the soil taxonomic class for measured soil locations, and a time series of satellite images to form a digital soil database. The results show that using ancillary information such as AVHRR data and DEM derivatives from the national to continental level surveys is among the most promising tools for geographers and soil surveyors. The AVHRR data is often used for land cover studies but its usefulness in soil studies has not yet been proven. This study is a representative example of the usefulness of AVHRR data in characterizing the soil-forming environment and delineating soil patterns, particularly when integrated with other data for describing the soil landscape, such as the DEM, slope, curvature and PDD. The predictive power of AVHRR and similar low spatial resolution satellite data sources could be further improved with the development of soil sensitive filters. Mention should be made of the potential improvement of the products derived from these data sources with the use of better quality data provided by satellites that have been launched recently. Neither the AVHRR nor the DEM-derivatives show high correlation with the soil classes, but both represent a great portion of the environmental variability. In general, the more uncorrelated information is extracted from DEM and AVHRR, the better explanation of the spatial soil variability is achieved with an integrated use of them. The images of AVHRR time series show a relatively low correlation, thus each of the new dates adds much potential information on the soils. The studies also highlighted the great help of surface vegetation in soil remote sensing, as indicated by the high R² value of Band 1 and NDVI. The importance of the short-term weather history of the study area was also demonstrated. Terrain information and terrain variables were primarily developed for large scale local studies. Small scale mapping of large regions presents different issues, like over-generalization and over-smoothing of the soil information. The terrain features with smaller extents are dissolved into a larger neighborhood. As a smoother terrain map is created, a lot of detail is lost and less variability is observable. Many of the terrain attributes are useless with this approach. Elevation, slope, relief intensity, potential drainage density and the curvature variables are the most informative digital variables for characterizing the soil-landscape in small scale inventories. The resulting soil databases will have all the advantages of quantitatively derived databases, including consistency, homogeneity, and reduced data generalization and edge-matching problems. Although the results from the above procedures are believed to be accurate enough to serve as a basis for global and regional studies, they should be checked and further revised by local and regional experts to ensure quality. Research should continue on improving the procedures, augmenting the pedon data with new field sampling, and incorporating new image and DEM data sources. One of the most important results of these studies is the demonstration of the usefulness of these data sources for small scale soil mapping and the overall validity and representatitivity of the AVHRR-terrain/soil correlation within the temperate region of the world. Further studies will need to be performed to test the use of AVHRR and terrain data for other climate zones of the World, where potential problems, like continuous cloud cover, may occur.

Tropical forest mapping from coarse spatial resolution satellite data: production and accuracy assessment issues

Global to regional datasets from coarse spatial resolution satellite sensors are increasingly becoming available. Land cover and vegetation extent maps are among the terrestrial products derived from such data. Whilst they provide a previously unavailable synoptic view of global situations, accuracy assessment methodological issues are often ignored when creating these digital databases or producing statistics from such sources. This paper looks at how these issues are addressed in the framework of the Joint Research Centre's TREES (Tropical Ecosystem Environment observation by Satellite) project, initiated to map and monitor the pan-tropical humid forest belt using Advanced Very High Resolution Radiometer (AVHRR) data. The paper shows that without accuracy assessment and a correction procedure, both the thematic legend and statistical data derived from coarse spatial maps can be misleading.

Tropical forest mapping from coarse spatial resolution satellite data: Production and accuracy assessment issues

Global to regional datasets from coarse spatial resolution satellite sensors are increasingly becoming available. Land cover and vegetation extent maps are among the terrestrial products derived from such data. Whilst they provide a previously unavailable synoptic view of global situations, accuracy assessment methodological issues are often ignored when creating these digital databases or producing statistics from such sources. This paper looks at how these issues are addressed in the framework of the Joint Research Centre's TREES (Tropical Ecosystem Environment observation by Satellite) project, initiated to map and monitor the pan-tropical humid forest belt using Advanced Very High Resolution Radiometer (AVHRR) data. The paper shows that without accuracy assessment and a correction procedure, both the thematic legend and statistical data derived from coarse spatial maps can be misleading.

Visualising active volcanism with high spatial resolution satellite data: the 1991-1993 eruption of Mount Etna

We present seven Landsat Thematic Mapper (TM) images that document the period before, during and after the 1991–1993 eruption of Mount Etna. This episode remains the most volumetrically significant to occur at the volcano within the past 300 years. We show how TM data can be used to identify major changes in the lava flow regime, specifically the transition between channel and tube-fed flow conditions, by mapping changes in the radiant properties of the flow surface. The high spatial resolution of TM (30-m pixels) allows point sources of radiance, such as skylights and tumuli, to be distinguished from linear radiant features such as surface lava flows. Using this information we illustrate the spatial and temporal development of a major tube system that developed within the flow-field. Detailed field maps of the tube system compiled by Calvari and Pinkerton (1998) allow us to validate the accuracy of our interpretations, and lead us to conclude that TM can be used to infer the position of tubes within the active flow-field to a reasonable level of accuracy. This indicates that similar high spatial resolution data could be used as a stand-alone tool to provide timely information regarding tube formation at future eruptions, useful information given that tubes can be a major factor in determining, for a given effusion rate, how far lava flows. When draped over a digital elevation model, the thermal data not only portray the nature of the relationship between flow surface structure and regional topography, but also provide a unique visualisation of how the flow-field advanced and threatened to inundate the town of Zafferana before, ultimately, being diverted.

Use of combined digital elevation model and satellite radiometric data for regional soil mapping

Previous reports demonstrated that data from air- and spaceborne sensors are appropriate for delineation of soil patterns. Also, many attempts have been made to use digital elevation model (DEM) for deriving soil information. However, little is known about the potential use of low spatial resolution satellite and digital elevation data in small-scale soil mapping. A case study was conducted to assess the use of integrated terrain and Advanced Very High Resolution Radiometer (AVHRR) databases for small-scale soil pattern delineation. The main objective was to test the effect of the addition of terrain descriptor data to the AVHRR data set on the classification results. Two database were used for the study. The first one was purely AVHRR data and contained the five basic AVHRR channels and the normalized difference vegetation index (NDVI) of five different dates, while in the second database the AVHRR data was complemented with a DEM, a curvature, a slope, an aspect and the potential drainage density layers. The performance of these two databases when employed to derive soil information was compared. These databases were then further processed using the Discriminant Analysis Feature Extraction (DAFE) function (which is based on a canonical analysis procedure), and were then classified using the Fisher linear discriminant, and the ECHO spectral–spatial classifiers. Based on the results, it was concluded that the two reflective bands, the middle infrared, the two thermal bands and the NDVI provided a relatively wide range of detectable soil information. The use of single images or small dimensional AVHRR data sets (less then 10 layers) does not result in acceptable performances, while the use of multispectral and multitemporal databases improved the classification performance very significantly. However, the purely AVHRR-based model could not always delineate soil variations related to terrain differences, and resulted in an overall classification performance of 49.1%. Digital elevation and terrain descriptor data were essential in the model for achieving acceptable results. In the second part of the study an integrated AVHRR-terrain database was used, where five terrain layers were added to the 30 AVHRR channels. Two different spatial resolutions were compared, 500 m and 1 km, respectively. The use of elevation, slope, aspect and curvature as differentiating criteria often lead to a satisfactory result in terrain characterization, particularly in large-scale mapping. However, with those variables extracted from DEM of physiographically complex areas, e.g., — where plain areas and mountainous/hilly regions occur together in the same study — often lose their ability to delineate soil variations of the level lands. Beyond these terrain descriptors we implemented a new function, called potential drainage density (PDD) to improve the performance of the model on the plain areas. The classification accuracy of the integrated AVHRR-terrain database was improved significantly over the case when only AVHRR data was in the model. The classification performances of the three different resolution images were 87.3% for the 500-m resolution image and 70.1% for the 1-km resolution image.

Evaluating land surface parameters in the Biosphere‐Atmosphere Transfer Scheme using remotely sensed data sets

Remotely sensed data sets were used to evaluate the spatial/temporal specification of albedo (α), roughness length (Z0), and green leaf area index (Lg) in the Biosphere‐Atmosphere Transfer Scheme (BATS). These parameters were evaluated for 1988 and 1993 in the Upper Mississippi River Basin (UMRB) using data from the International Satellite Land Surface Climatology Project Initiative 1 (ISLSCP 1) and by applying the ISLSCP 1 algorithms to high spatial resolution satellite data (Pathfinder advanced very high resolution radiometer (AVHRR) land (PAL)). ISLSCP 1 Lg was more variable spatially than BATS, particularly in midsummer months. ISLSCP 1 α was generally lower than BATS α. BATS and ISLSCP 1 generally showed small spatial variability in α for a given cover type. ISLSCP 1 Z0 were up to 100% larger than BATS. Spatial/temporal variability in ISLSCP 1 and BATS Z0 was primarily restricted to the spring. Comparisons between the ISLSCP 1 and PAL data indicated substantial differences in estimates of Lg and Z0 because of differences in the transformation of the vegetation index to Lg and grid cell heterogeneity. PAL Lg and Z0 were extremely variable spatially. Large differences between years in the PAL Lg were found, particularly in climate‐affected regions. The remotely sensed data indicate that improved parameterizations in BATS are needed to account for spatial variability in Lg and Z0, as well as interannual variability in Lg. However, the comparisons also demonstrate that assumptions inherent in remote sensing algorithms and the impact of these on parameter estimates must be carefully examined.

Satellites, space, time and the African trypanosomiases

The human and animal trypanosomiases of Africa provide unique challenges to epidemiologists because of the spatial and temporal scales over which variation in transmission takes place. This chapter describes how our descriptions of the different components of transmission, from the parasites to the affected hosts, eventually developed to include geographical dimensions. It then briefly mentions two key analytical techniques used in the application of multi-temporal remotely sensed imagery to the interpretation of field data; temporal Fourier analysis for data reduction, and a variety of discriminant analytical techniques to describe the distribution and abundance of vectors and diseases. Satellite data may be used both for biological, process-based models and for statistical descriptions of vector populations and disease transmission. Examples are given of models for the tsetse Glossina morsitans in the Yankari Game Reserve, Nigeria, and in The Gambia. In both sites the satellite derived index of Land Surface Temperature (LST) is the best correlate of monthly mortality rates and is used to drive tsetse population models. The Gambia model is then supplemented with a disease transmission component; the mean infection rates of the vectors and of local cattle are satisfactorily described by the model, as are the seasonal variations of infection in the cattle. High and low spatial resolution satellite data have been used in a number of statistical studies of land cover types and tsetse habitats. In addition multi-temporal data may be related to both the incidence and prevalence of trypanosomiasis. Analysis of past and recent animal and human trypanosomiasis data from south-east Uganda supports the suggestion of the importance of cattle as a reservoir of the human disease in this area; mean infection prevalences in both human and animal hosts rise and fall in a similar fashion over the same range of increasing vegetation index values. Monthly sleeping sickness case data from the districts and counties of south-east Uganda are analysed and often show significant correlations with local LST. Case numbers increase with LST in areas that are relatively cooler than average for this part of Uganda, but decrease with LST in areas that are on average warmer. This indicates different seasonal cycles of risk across the region, and may be related to the differing vectorial roles of the two local tsetse, G. fuscipes and G. pallidipes. Finally, the increasing pace of change, and the likelihood of new or reemerging vector-borne diseases, highlight the need for accurate and timely information on habitat changes and the impacts these will have on disease transmission. The next generation of satellites will have significantly more spectral and spatial resolution than the current satellites, and will enable us to refine both statistical and biological predictions of trypanosomiasis and other vector-borne diseases within disease early warning systems.

NOAA/AVHRR Bidirectional Reflectance: Modeling and Application for the Monitoring of a Temperate Forest

In this article, bidirectional effects on NOAA/AVHRR short wavelength data were analyzed for the regional monitoring of the temperate pine ( Pinus pinaster) Landes forest. Because of differences in soil hydrologic characteristics, the forest is described by five ecotypes, which determine the understorey type and the potential for pine growth. Bidirectional visible and near-infrared reflectances were analyzed from a daily NOAA-11 archive through years 1990–1994 using the bidirectional reflectance function model of Rahman. Rahman’s model fits 72% of AVHRR visible reflectance, and 93% of AVHRR near-infrared reflectance. A typical example shows that 1) in the principal plane, the ratio of hot spot to forward scatter reflectance is 2.9 and 2.1 for visible and near-infrared, respectively, 2) bidirectional effects become negligible across the principal plane, and 3) NDVI shows a shape opposite to that of reflectances. After normalization, the seasonal dynamics of AVHRR reflectances were related to the phenological cycle of pine trees and understorey vegetation. Visible reflectance drops earlier on coastal than on inland sites, due to an advance in the onset of pine growth. Near-infrared reflectance increases in summer for annual understorey, and remains steady for evergreen understorey. Differences in the seasonal dynamics of NDVI consequently characterize each Landes ecotype. From the modeled reflectances, we estimated the seasonal dynamics of visible and near-infrared albedo. We calculated the solar albedo by a combination of the last two. The visible albedo displays a seasonal dynamic similar to that of normalized visible reflectance. The solar albedo was found constant and around 12.7% on each ecotype. In both cases, albedo strongly increases with solar zenith angle, consistent with ground measurements. These results clearly show the usefulness of coarse spatial resolution satellite data for the regional monitoring and modeling of temperate coniferous ecosystems.

Integration of High and Low Resolution NDVI Data for Monitoring Vegetation in Mediterranean Environments

The integration of the useful features of high and low spatial and temporal resolution satellite data is a major issue in remote sensing studies. The current work presents the development and testing of a procedure based on classification and regression analysis techniques for generating an NDVI data set with the spatial resolution of Landsat TM images and the temporal resolution of NOAA AVHRR maximum-value composites. The procedure begins with a classification of the high resolution TM data which yields land use references. These are degraded to low spatial resolution in order to produce abundance images comparable with the AVHRR data. Linear regressions are then applied between the AVHRR NDVI data and the abundance images to estimate the profiles of the pure classes, which are then merged to the high spatial resolution classification outputs to generate an integrated data set. Experiments carried out in an area of Tuscany (Central Italy) intercomparing different strategies for each methodological step (hard and fuzzy classification, mean and Gaussian degradation, uni- and multivariate regression) identified an optimum methodology composed of fuzzy classification, mean degradation, and multivariate regression procedures.

RELATION BETWEEN NOAA-AVHRR SATELLITE DATA AND STOCKING RATE OF RANGELANDS

Biomass of both wild herbivores and livestock in rangelands is correlated with rainfall at a regional scale. Thus, rainfall may be a good predictor of actual stocking rates. However, rainfall data are scarce in many regions, and their spatial resolution is usually much coarser than needed to set or to evaluate wildlife or livestock stocking rates. We here show a relationship between livestock biomass and an annual vegetation index (normalized-difference vegetation index-integrated value, NDVI-I) calculated from remotely sensed data on spectral properties of rangelands of Argentina. The relationship is as strong or even stronger than previously reported correlations between herbivore biomass and rainfall. This, together with the greater availability and higher spatial resolution of satellite data, makes remote sensing a potentially valuable tool to predict stocking rates for regions, landscapes, and different portions of a landscape. The form of the relationship between stocking rate and average NDVI-I was exponential, which, as previously shown, indicates an increasing herbivore load per unit of primary production as rainfall or productivity increases. This may be at least partially explained by the fact that the NDVI interannual variation and seasonality were negatively related with average NDVI-I. Thus, stocking rate may increase exponentially because of an increasing year-to-year reliability of the forage resource and a more even distribution within the year.

A simplified method of accurate geometric correction for NOAA AVHRR 1B data

There are different methods for geometric correction of NOAA AVHRR data, but these methods either pay less attention to the accuracy, or are technically complex, almost unsuitable for most users. To combine AVHRR data with other high spatial resolution satellite data, or with ancillary data in GIS, it is necessary to develop an accurate geometric correction method, which should be easy to use even for non-professional users. After analysing the pixel shape and size of AVHRR 1B data along scan line and evaluating the quality of geographical data of NOAA AVHRR 1B data set, we found that the geographical data was adequately accurate for identifying the pixel size and shape and the method was developed accordingly. The proposed method has two steps. The first step is to correct pixel distortion. The separate program performs the distortion correction, applying the geographical data of AVHRR 1B data set to assigning the value of each pixel of the desired output geographical area with given pixel size, and making logical judgment for unassigned pixel. The second step is to perform conventional polynomial transformation on the results of the first step. An application of this method is presented in the paper. To examine the precision, SAVI images derived from the geometrically corrected NOAA AVHRR band image were used to perform overlay with each other and also with a 1 :50000 river system map. A half-pixel accuracy was achieved.

Extracting crop radiometric responses from simulated low and high spatial resolution satellite data using a linear mixing model

Abstract The images from optical sensors with a broad path width (e.g. NOAA-AVHRR) are used for monitoring vegetation on a regional scale. The European agricultural land uses, which are generally heterogeneous, can be coarsely distinguished by these radiometers. Such sensors, however, do not allow the discrimination of seasonal radiometric changes of a given crop. Some future Earth observation platforms will carry two types of instruments on board. The first instrument will have moderate spatial resolution but a broad path width to allow almost daily observations of the emerged areas. The second will have high spatial resolution and a narrow path width to give the opportunity of making land use thematic maps from the few images recorded per year. The combination of these two types of data allows the medium resolution signal to be unmixed in order to restore the radiometric evolution of a particular crop or of a group of crops. From the application of a linear mixing model to coarse spatial resolution data...

Data sets for global remote sensing: lessons learnt†

Abstract Developments in the use of high temporal, coarse spatial resolution satellite data have made a major contribution to land remote sensing. Global data sets from the Advanced Very High Resolution Radiometer (AVHRR) have provided the community with some preliminary tools to address global change research issues. They have also helped the research community to decide where new and improved instrumentation and processing techniques are needed. The authors have used their experience in developing the application of AVHRR data to land monitoring, their involvement in the development of the Moderate resolution Imaging Spectrometer (MODIS) instrument and their participation in the Earth Observing System (EOS) and the Data and Information System of the International Geosphere Biosphere Program (IGBP-DIS) to document some of the more important lessons which have been learnt and those which may have been forgotten. The discussion is arranged under the headings of instrument, data-handling and science issues.

Cloud and Convection Frequencies over the Southeast United States as Related to Small-Scale Geographic Features

Visible and infrared data from the GOES West satellite were collected at 0700 CST and at each hour from 1000 CST to 1700 CST during summer 1986. Use of relatively high spatial and temporal resolution satellite data allowed study of local area cloud variations over broad regions in many new ways. Cloud frequency charts were computed for the area from Mississippi cast to Georgia and the Gulf of Mexico north to Tennessee for each of the nine hours as well as convection frequency charts of four convection intensifies as defined by the temperature of the cloud top. Strong diurnal cloud variations were observed. These new data analyses show an average maximum cloud frequency near 45 percent over the land areas at about 1400 local time. The maximum of deep convection, about 8 percent, was one hour later. Cloudiness and deep convection were at a maximum during the nocturnal hours over the Gulf of Mexico. Cloud frequency shows a strong relationship to small terrain features. Small fresh water bodies have cloud minima relative to the surroundings in the afternoon hours. The higher, steep terrain shows cloud maxima and the adjacent lower terrain exhibits afternoon cloud minima due to divergence caused by the valley to mountain breeze. The sea breeze-induced convergence causes relative cloud maxima over Gulf of Mexico near-shore land areas with the stronger maxima and greater areal coverage over peninsulas. Peninsulas that are of a similar scale or larger as compared to that of the convective cells show a late afternoon maxima of deep convection. Small scale geographical features such as small coastal islands and reservoirs show no relationship to deep convection in the frequency analysis.

Estimating surface temperatures from satellite thermal infrared data—A simple formulation for the atmospheric effect

In recent years the availability of high spatial resolution thermal infrared data from satellites has prompted the use of energy budget models relating satellite-derived surface temperatures to surface moisture, near-surface thermal inertia, energy exchange with the atmosphere, etc. However, correction of the high spatial resolution satellite data for atmospheric water vapor effects can represent a substantial computational burden unless simplifying assumptions are utilized. A simple formulation is developed and its applicability tested by application to standard meteorological soundings at a time near the overpass of an NOAA operational satellite. It appears that reasonable estimates of surface temperature (±2–3°C) are readily obtained for areas of order 100–300 km 2.