Annual Land Surface Temperature Research Articles

THE CLIMATE OF THE CANARY ISLANDS BY ANNUAL CYCLE PARAMETERS

Annual cycle parameters (ACP) provide a global climatology of annual land surface temperature (LST) based on daily 1 km MODIS observations. These are based on a simple model of the annual temperature cycle and allow estimating LST patterns under largely cloud-free conditions for every day of year. Further, they deliver measures for the LST variability and the frequency of cloud occurrence. It has been demonstrated, that they reproduce important surface climate characteristics at global and urban scale but their ability to reproduce topo-climates has yet to be studied in detail. In this paper their suitability to investigate climatic variability at km scale were studied at the case of the Canary Islands (Spain). This Archipelago, has a very stable climate dominated by the Azores high and the trade wind belt, but shows a large number of micro-climates ranging from arid hot climates to cold climates. It was found that ACPs are a relevant source of climatic information at km scale in complex orography. Specifically, known features such as subsidence inversion, the resulting sea of clouds, the strong differentiation in precipitation between the flat and high islands, as well as the northern and southern slopes at the latter were clearly visible in the parameters.

THE CLIMATE OF THE CANARY ISLANDS BY ANNUAL CYCLE PARAMETERS

Abstract. Annual cycle parameters (ACP) provide a global climatology of annual land surface temperature (LST) based on daily 1 km MODIS observations. These are based on a simple model of the annual temperature cycle and allow estimating LST patterns under largely cloud-free conditions for every day of year. Further, they deliver measures for the LST variability and the frequency of cloud occurrence. It has been demonstrated, that they reproduce important surface climate characteristics at global and urban scale but their ability to reproduce topo-climates has yet to be studied in detail. In this paper their suitability to investigate climatic variability at km scale were studied at the case of the Canary Islands (Spain). This Archipelago, has a very stable climate dominated by the Azores high and the trade wind belt, but shows a large number of micro-climates ranging from arid hot climates to cold climates. It was found that ACPs are a relevant source of climatic information at km scale in complex orography. Specifically, known features such as subsidence inversion, the resulting sea of clouds, the strong differentiation in precipitation between the flat and high islands, as well as the northern and southern slopes at the latter were clearly visible in the parameters.

The impact of built-up surfaces on land surface temperatures in Italian urban areas

Urban areas are characterized by the very high degree of soil sealing and continuous built-up areas: Italy is one of the European countries with the highest artificial land cover rate, which causes a substantial spatial variation in the land surface temperature (LST), modifying the urban microclimate and contributing to the urban heat island effect. Nevertheless, quantitative data regarding the contribution of different densities of built-up surfaces in determining urban spatial LST changes is currently lacking in Italy. This study, which aimed to provide clear and quantitative city-specific information on annual and seasonal spatial LST modifications resulting from increased urban built-up coverage, was conducted generally throughout the whole year, and specifically in two different periods (cool/cold and warm/hot periods). Four cities (Milan, Rome, Bologna and Florence) were included in the study. The LST layer and the built-up-surface indicator were obtained via use of MODIS remote sensing data products (1km) and a very high-resolution map (5m) of built-up surfaces recently developed by the Italian National Institute for Environmental Protection and Research. The relationships between the dependent (mean daily, daytime and nighttime LST values) and independent (built-up surfaces) variables were investigated through linear regression analyses, and comprehensive built-up-surface-related LST maps were also developed. Statistically significant linear relationships (p<0.001) between built-up surfaces and spatial LST variations were observed in all the cities studied, with a higher impact during the warm/hot period than in the cool/cold ones. Daytime and nighttime LST slope patterns depend on the city size and relative urban morphology. If implemented in the existing city plan, the urban maps of built-up-surface-related LST developed in this study might be able to support more sustainable urban land management practices by identifying the critical areas (Hot-Spots) that would benefit most from mitigation actions by local authorities, land-use decision makers, and urban planners.

A time series analysis of urbanization induced land use and land cover change and its impact on land surface temperature with Landsat imagery

Abstract The relationship between land use and land cover (LULC) patterns and thermal characteristic has long been studied for examining the impact of urbanization on urban thermal environment. Previous studies were inclined to the use of one or a few images without fully considering the temporal domain of the reflective and thermal infrared data on board Landsat sensors. This paper took the Atlanta metropolitan area as a case study to illustrate LULC change and its impact on land surface temperature (LST) variations. The Landsat L1T (Standard terrain correction) images from TM/ETM + from 1984 to 2011 (507 in total) were downloaded through the USGS online portal and consistently calibrated to surface reflectance and brightness temperature (BT). The cloud-, cloud shadow-, and snow-contaminated pixels were excluded in the analysis according to the metadata, and a further screening procedure based on the RIRLS (Robust Iteratively Reweighted Least Squares) technique was implemented. The time series LSTs (TSLSTs) was derived using the single channel algorithm because it required only the parameters of water vapor and land surface emissivity and had a reported error close to 1 K. The LULC classification and change detection was accomplished by using the Continuous Change Detection and Classification (CCDC) algorithm. The TSLSTs were further decomposed into the seasonal and trend components by an additive model. Results showed that the overall LULC classification and change detection accuracies were 89% and 92%, respectively. Urban growth was mainly observed in Fulton County and Gwinnett County. High-intensity urban land had the largest mean LST value of 294.9 K and yearly amplitude of 17.4 K. A comparison of the trend component between urban and non-urban land covers showed a difference of 1.8 K per decade. Next, temporal thermal signatures were created to characterize and quantify the impact of urban LULC changes. Decomposition analysis showed that the conversion of evergreen forest to medium-intensity urban land generated the largest difference in annual LST variation (5.7 K) and the largest trend difference (0.0004 K/day).

Linking Surface Urban Heat Islands with Groundwater Temperatures.

Urban temperatures are typically, but not necessarily, elevated compared to their rural surroundings. This phenomenon of urban heat islands (UHI) exists both above and below the ground. These zones are coupled through conductive heat transport. However, the precise process is not sufficiently understood. Using satellite-derived land surface temperature and interpolated groundwater temperature measurements, we compare the spatial properties of both kinds of heat islands in four German cities and find correlations of up to 80%. The best correlation is found in older, mature cities such as Cologne and Berlin. However, in 95% of the analyzed areas, groundwater temperatures are higher than land surface temperatures due to additional subsurface heat sources such as buildings and their basements. Local groundwater hot spots under city centers and under industrial areas are not revealed by satellite-derived land surface temperatures. Hence, we propose an estimation method that relates groundwater temperatures to mean annual land-surface temperatures, building density, and elevated basement temperatures. Using this method, we are able to accurately estimate regional groundwater temperatures with a mean absolute error of 0.9 K.

The cumulative effects of urban expansion on land surface temperatures in metropolitan JingjinTang, China

AbstractRapid urbanization has resulted in the permanent conversion of large areas of cropland and natural vegetation to impervious surfaces and therefore greatly modified land surface properties and land‐atmosphere interactions. This study sought to examine the urbanization process using Landsat images from 2001 to 2010 in metropolitan JingjinTang (JJT), a rapidly expanding urban cluster in northern China. We aggregated the original results of land use data as fractional cover information in 1 km and 10 km grids. Annual and seasonal land surface temperatures (LSTs) were processed from Moderate Resolution Imaging Spectroradiometer products. We used moving window and gradient analysis methods to examine the differences in LST between urban and other land types, further identifying LST increases in gradients of urbanization levels. Urban extent increased by 1.6 times, and approximately 45% newly developed areas were converted from croplands during this process. Emerging urban land in JJT has caused approximately 0.85 ± 0.68°C warming in terms of annual mean LST, and the greatest warming occurred in the summer. An increase in urban land of 10% in a 1 km grid in JJT would cause approximately a 0.21°C increase in annual LST. Urbanization also led to increases in daytime LSTs and nighttime LSTs by approximately 1.03 ± 1.38°C and 0.78 ± 1.02°C, respectively. The warming trend induced by urbanization exhibits clear seasonal and diurnal differences, and this warming trend is most likely caused by the cumulative effects of changes in land properties, radiation storage, and anthropogenic heat release by urbanization.

Temporal Dynamics of Land Surface Temperature From Landsat TIR Time Series Images

Land surface temperature (LST) is a valuable parameter in studies of surface energy balance, landscape thermal patterns, and human–environment interactions. An effective way to quantify the LST dynamics over spatial and temporal domains is to utilize the consistent Landsat thermal infrared (TIR) data since 1982. Currently, only a small proportion of studies utilized the Landsat TIR data for investigating both the intra- and interannual LST variations. The objectives of the study are to provide statistical evidence for the existence of the annual temperature cycle (ATC) and to develop a decomposition technique to explore landscape thermal patterns by land cover. Eighty-two cloud-free TIR images of Los Angeles County from Landsat TM between 2000 and 2010 were collected and consistently calibrated to the LSTs. The LSTs were then analyzed by the Lomb–Scargle periodogram technique to test whether the time series LSTs showed rhythmic patterns and by a decomposition model to analyze the intra- and interannual landscape thermal patterns. The periodogram analysis confirmed that ATC was statistically significant with the periodic time of 362 days. Furthermore, sensitivity analysis showed that the Lomb–Scargle technique can still discover the ATC with the difference of up to five days, even when the number of images decreased to 60. Based on the periodogram analysis, a decomposition model was initialized to disassemble the time series LSTs into seasonality and trend components for comparisons among land covers. Results suggested that the developed areas exhibited relatively low seasonal amplitude of 11.7 K, while largest mean annual LST value is 302.8 K. The difference of the averaged trend component between urban and other land covers reached 1.1 K over the decade. Future research may be directed in dealing with the time-varying seasonality component for better quantifying the thermal patterns.

A New Global Climatology of Annual Land Surface Temperature

Land surface temperature (LST) is an important parameter in various fields including hydrology, climatology, and geophysics. Its derivation by thermal infrared remote sensing has long tradition but despite substantial progress there remain limited data availability and challenges like emissivity estimation, atmospheric correction, and cloud contamination. The annual temperature cycle (ATC) is a promising approach to ease some of them. The basic idea to fit a model to the ATC and derive annual cycle parameters (ACP) has been proposed before but so far not been tested on larger scale. In this study, a new global climatology of annual LST based on daily 1 km MODIS/Terra observations was processed and evaluated. The derived global parameters were robust and free of missing data due to clouds. They allow estimating LST patterns under largely cloud-free conditions at different scales for every day of year and further deliver a measure for its accuracy respectively variability. The parameters generally showed low redundancy and mostly reflected real surface conditions. Important influencing factors included climate, land cover, vegetation phenology, anthropogenic effects, and geology which enable numerous potential applications. The datasets will be available at the CliSAP Integrated Climate Data Center pending additional processing.

Recent climate variability and its impact on precipitation, temperature, and vegetation dynamics in the Lancang River headwater area of China

The alpine ecosystem is one of the most fragile ecosystems threatened by global climate change. The impact of climate variability on the vegetation dynamics of alpine ecosystems has become important in global change studies. In this study, spatially explicit gridded data, including the Moderate Resolution Imaging Spectroradiometer (MODIS) land-surface temperature (LST) product (MOD11A1/A2), the Tropical Rainfall Measuring Mission (TRMM) rainfall product (3B43), and MODIS net primary productivity (NPP) product (MOD17A3), together with meteorological observation data, were used to explore the spatio-temporal pattern of climate variability and its impact on vegetation dynamics from 2000 to 2012 in the Lancang River headwater area. We found that the variation patterns of LST, precipitation, and NPP in the study area showed remarkable spatial differences. From the northwest to the southeast the spatial variation of average annual LST exhibited a decreasing–increasing–decreasing–increasing pattern. At the same time, most of the study area exhibited an increasing LST during the growing season. The annual precipitation increased in the semi-arid northern part, whereas it decreased in the semi-humid southern part. The precipitation variability during the growing season has a pattern similar to the annual precipitation variability. Although the majority of the regions have seen an NPP increase from 2000 to 2012, the responses of the vegetation to the varied climate factors were spatially heterogeneous. The alpine–subalpine meadows in the high-altitude areas were more sensitive to climate variability in the growing season. It is argued that satellite remote-sensing products have great potential in investigating the impact of climate variability on vegetation dynamics at the finer scale, especially for the Lancang River headwater area with complex surface heterogeneity.

The relationship analysis of vegetation cover, rainfall and land surface temperature based on remote sensing in Tibet, China

The analyses of vegetation influenced by meteorological factors contribute to research the relationships between terrestrial ecosystems and climate changes. Using the data of normalized difference vegetation index, land surface temperature from MODIS satellite products and rainfall from TRMM satellite products to build 16-day data sequence from 2000 to 2011 years, this paper analyzed the spatial-temporal distribution of vegetation cover, land surface temperature and rainfall, and also discussed the relationships among three factors in Tibet. The results indicated that the mean vegetation coverage in August varied in the range of 0% to 99% depending on location. The southeastern part of Tibet had annual rainfall of 600~800 mm, whereas the western part suffered from drought with a value below 200 mm. The mean annual land surface temperature varied between −8.9°C and 16.3°C. During the whole year, the correlation coefficient between vegetation index and land surface temperature was the smallest in July. The correlation coefficient between vegetation index and rainfall was the biggest in September. Vegetation was influenced greater by the rainfall than land surface temperature from April to October in Tibet.

Ascaris lumbricoides Infection and Its Relation to Environmental Factors in the Mbeya Region of Tanzania, a Cross-Sectional, Population-Based Study

BackgroundWith one quarter of the world population infected, the intestinal nematode Ascaris lumbricoides is one of the most common infectious agents, especially in the tropics and sub-tropics. Infection is caused by oral intake of eggs and can cause respiratory and gastrointestinal problems. To identify high risk areas for intervention, it is necessary to understand the effects of climatic, environmental and socio-demographic conditions on A. lumbricoides infection.MethodologyCross-sectional survey data of 6,366 study participants in the Mbeya region of South-Western Tanzania were used to analyze associations between remotely sensed environmental data and A. lumbricoides infection. Non-linear associations were accounted for by using fractional polynomial regression, and socio-demographic and sanitary data were included as potential confounders.Principal FindingsThe overall prevalence of A. lumbricoides infection was 6.8%. Our final multivariable model revealed a significant non-linear association between rainfall and A. lumbricoides infection with peak prevalences at 1740 mm of mean annual rainfall. Mean annual land surface temperature during the day was linearly modeled and negatively associated with A. lumbricoides infection (odds ratio (OR) = 0.87, 95% confidence interval (CI) = 0.78–0.97). Furthermore, age, which also showed a significant non-linear association (infection maximum at 7.7 years), socio-economic status (OR = 0.82, CI = 0.68–0.97), and latrine coverage around the house (OR = 0.80, CI = 0.67–0.96) remained in the final model.Conclusions A. lumbricoides infection was associated with environmental, socio-demographic and sanitary factors both in uni- and multivariable analysis. Non-linear analysis with fractional polynomials can improve model fit, resulting in a better understanding of the relationship between environmental conditions and helminth infection, and more precise predictions of high prevalence areas. However, socio-demographic determinants and sanitary conditions should also be considered, especially when planning public health interventions on a smaller scale, such as the community level.

Hookworm Infection and Environmental Factors in Mbeya Region, Tanzania: A Cross-Sectional, Population-Based Study

BackgroundHookworm disease is one of the most common infections and cause of a high disease burden in the tropics and subtropics. Remotely sensed ecological data and model-based geostatistics have been used recently to identify areas in need for hookworm control.MethodologyCross-sectional interview data and stool samples from 6,375 participants from nine different sites in Mbeya region, south-western Tanzania, were collected as part of a cohort study. Hookworm infection was assessed by microscopy of duplicate Kato-Katz thick smears from one stool sample from each participant. A geographic information system was used to obtain remotely sensed environmental data such as land surface temperature (LST), vegetation cover, rainfall, and elevation, and combine them with hookworm infection data and with socio-demographic and behavioral data. Uni- and multivariable logistic regression was performed on sites separately and on the pooled dataset.Principal FindingsUnivariable analyses yielded significant associations for all ecological variables. Five ecological variables stayed significant in the final multivariable model: population density (odds ratio (OR) = 0.68; 95% confidence interval (CI) = 0.63–0.73), mean annual vegetation density (OR = 0.11; 95% CI = 0.06–0.18), mean annual LST during the day (OR = 0.81; 95% CI = 0.75–0.88), mean annual LST during the night (OR = 1.54; 95% CI = 1.44–1.64), and latrine coverage in household surroundings (OR = 1.02; 95% CI = 1.01–1.04). Interaction terms revealed substantial differences in associations of hookworm infection with population density, mean annual enhanced vegetation index, and latrine coverage between the two sites with the highest prevalence of infection.Conclusion/SignificanceThis study supports previous findings that remotely sensed data such as vegetation indices, LST, and elevation are strongly associated with hookworm prevalence. However, the results indicate that the influence of environmental conditions can differ substantially within a relatively small geographic area. The use of large-scale associations as a predictive tool on smaller scales is therefore problematic and should be handled with care.

Spatial Distribution of Podoconiosis in Relation to Environmental Factors in Ethiopia: A Historical Review

BackgroundAn up-to-date and reliable map of podoconiosis is needed to design geographically targeted and cost-effective intervention in Ethiopia. Identifying the ecological correlates of the distribution of podoconiosis is the first step for distribution and risk maps. The objective of this study was to investigate the spatial distribution and ecological correlates of podoconiosis using historical and contemporary survey data.MethodsData on the observed prevalence of podoconiosis were abstracted from published and unpublished literature into a standardized database, according to strict inclusion and exclusion criteria. In total, 10 studies conducted between 1969 and 2012 were included, and data were available for 401,674 individuals older than 15 years of age from 229 locations. A range of high resolution environmental factors were investigated to determine their association with podoconiosis prevalence, using logistic regression.ResultsThe prevalence of podoconiosis in Ethiopia was estimated at 3.4% (95% CI 3.3%–3.4%) with marked regional variation. We identified significant associations between mean annual Land Surface Temperature (LST), mean annual precipitation, topography of the land and fine soil texture and high prevalence of podoconiosis. The derived maps indicate both widespread occurrence of podoconiosis and a marked variability in prevalence of podoconiosis, with prevalence typically highest at altitudes >1500 m above sea level (masl), with >1500 mm annual rainfall and mean annual LST of 19–21°C. No (or very little) podoconiosis occurred at altitudes <1225 masl, with annual rainfall <900 mm, and mean annual LST of >24°C.ConclusionPodoconiosis remains a public health problem in Ethiopia over considerable areas of the country, but exhibits marked geographical variation associated in part with key environmental factors. This is work in progress and the results presented here will be refined in future work.

Spatial and Temporal Variations of Land Surface Temperature Over the Tibetan Plateau Based on Harmonic Analysis

Abstract Land surface temperature (LST) is an essential parameter in the physics of land surface processes. The spatiotemporal variations of LST on the Tibetan Plateau were studied using AQUA Moderate Resolution Imaging Spectroradiometer LST data. Considering the data gaps in remotely sensed LST products caused by cloud contamination, the harmonic analysis of time series (HANTS) algorithm was used to eliminate the influence of cloud cover and to describe the periodical signals of LST. Observed air temperature data from 79 weather stations were employed to evaluate the fitting performance of the HANTS algorithm. Results indicate that HANTS can effectively fit the LST time series and remove the influence of cloud cover. Based on the HANTS-derived mean term and annual harmonics, annual mean LST, seasonal fluctuation, and peak time of the LST annual cycle are discussed. The spatial distribution of annual mean LST generally exhibits consistency with altitude in the study area, and the spatial distribution of s...

Pan-Arctic Land Surface Temperature from MODIS and AATSR: Product Development and Intercomparison

Models and observations show that the Arctic is experiencing the most rapid changes in global near-surface air temperature. We developed novel EASE-grid Level 3 (L3) land surface temperature (LST) products from Level 2 (L2) AATSR and MODIS data to provide weekly, monthly and annual LST means over the pan-Arctic region at various grid resolutions (1–25 km) for the past decade (2000–2010). In this paper, we provide: (1) a review of previous validation of MODIS/AATSR L2; (2) a description of the processing chain of L3 products; (3) an assessment of the 25 km products uncertainty, and; (4) a quantification of the bias introduced by over-representing clear-sky days in MODIS L3 products. In addition, we generated uncertainty maps by comparing L3 products with LST from passive microwave sensors (AMSR-E and SSM/I) and the North American Regional Reanalysis (NARR). Results show a close correspondence between MODIS and AATSR monthly products with a mean-difference (MD) of −1.1 K. Comparing L3 products with NARR indicates a close agreement in summer and a systematic bias in winter, which is entirely negative with respect to MODIS L3 (MD: −3.6, Min: −6.8, Max: −1 K). Comparing monthly averaged MODIS L3 to NARR clear-sky to quantify over-representing clear-sky days indicates a decrease of winter and an increase of summer difference compared to NARR all-sky. Finally, we provide suggestions to improve LST retrieval over Arctic regions.

Land use classification from multitemporal Landsat imagery using the Yearly Land Cover Dynamics (YLCD) method

Several previous studies have shown that the inclusion of the LST (Land Surface Temperature) parameter to a NDVI (Normalized Difference Vegetation Index) based classification procedure is beneficial to classification accuracy. In this work, the Yearly Land Cover Dynamics (YLCD) approach, which is based on annual behavior of LST and NDVI, has been used to classify an agricultural area into crop types. To this end, a time series of Landsat-5 images for year 2009 of the Barrax (Spain) area has been processed: georeferenciation, destriping and atmospheric correction have been carried out to estimate NDVI and LST time series for year 2009, from which YLCD parameters were estimated. Then, a maximum likelihood classification was carried out on these parameters based on a training dataset obtained from a crop census. This classification has an accuracy of 87% (kappa=0.85) when crops are subdivided in irrigated and non-irrigated fields, and when cereal crops are aggregated in a single crop, and performs better than a similar classification from Landsat bands only. These results show that a good crop differentiation can be obtained although detailed crop separation may be difficult between similar crops (barley, wheat and oat) due to similar annual NDVI and LST behavior. Therefore, the YLCD approach is suited for vegetation classification at local scale. As regards the assessment of the YLCD approach for classification at regional and global scale, it will be carried out in a further study.

Terrain Segmentation of Egypt from Multi-Temporal Night LST Imagery and Elevation Data

Monthly night averaged land surface temperature (LST) MODIS imagery was analyzed throughout a year-period (2006), in an attempt to segment the terrain of Egypt into regions with different LST seasonal variability, and represent them parametrically. Regions with distinct spatial and temporal LST patterns were outlined using several clustering techniques capturing aspects of spatial, temporal and temperature homogeneity or differentiation. Segmentation was supplemented, taking into consideration elevation, morphological features and landcover information. The northern coastal region along the Mediterranean Sea occupied by lowland plain areas corresponds to the coolest clusters indicating a latitude/elevation dependency of seasonal LST variability. On the other hand, for the inland regions, elevation and terrain dissection plays a key role in LST seasonal variability, while an east to west variability of clusters’ spatial distribution is evident. Finally, elevation biased clustering revealed annual LST differences among the regions with the same physiographic/terrain characteristics. Thermal terrain segmentation outlined the temporal variation of LST during 2006, as well as the spatial distribution of LST zones.

Comparison of land surface temperature (LST) modeled with a spatially-distributed solar radiation model (SRAD) and remote sensing data

The solar radiation model SRAD was applied to a Mediterranean mountainous environment in southern California for estimating land surface temperature (LST). The simulated SRAD LST results were compared with high- and meso-resolution satellite-based LST data at daily, monthly and annual temporal scales to identify potential ways of improving the LST accuracy in either the SRAD or satellite-based approaches. It was found that mean monthly and annual LST from SRAD closely matched the MODIS LST observations (but for the fact they were from 0.7 to 1.5 °C lower) while the daily LST from SRAD agreed less well with ASTER observations and were 2.1 °C and 4.8 °C higher for 01/23/05 and 07/21/06, respectively. High mountainous, steep and south/north facing slopes resulted in large discrepancies in LST estimates and the winter LST estimates are more sensitive to terrain factors and their associated land use/cover characteristics than the summer estimates. It is therefore suggested that vertically variable elevation lapse rates, spatially distributed surface albedo and leaf area index for the time-specific simulations that represent the heterogeneity of land surface characteristics be used in SRAD inputs. The results also show how instantaneous model outputs rather than lumped average daily outputs offer better comparisons with satellite based data as well.

About regression-kriging: From equations to case studies

This paper discusses the characteristics of regression-kriging (RK), its strengths and limitations, and illustrates these with a simple example and three case studies. RK is a spatial interpolation technique that combines a regression of the dependent variable on auxiliary variables (such as land surface parameters, remote sensing imagery and thematic maps) with simple kriging of the regression residuals. It is mathematically equivalent to the interpolation method variously called “Universal Kriging” (UK) and “Kriging with External Drift” (KED), where auxiliary predictors are used directly to solve the kriging weights. The advantage of RK is the ability to extend the method to a broader range of regression techniques and to allow separate interpretation of the two interpolated components. Data processing and interpretation of results are illustrated with three case studies covering the national territory of Croatia. The case studies use land surface parameters derived from combined Shuttle Radar Topography Mission and contour-based digital elevation models and multitemporal-enhanced vegetation indices derived from the MODIS imagery as auxiliary predictors. These are used to improve mapping of two continuous variables (soil organic matter content and mean annual land surface temperature) and one binary variable (presence of yew). In the case of mapping temperature, a physical model is used to estimate values of temperature at unvisited locations and RK is then used to calibrate the model with ground observations. The discussion addresses pragmatic issues: implementation of RK in existing software packages, comparison of RK with alternative interpolation techniques, and practical limitations to using RK. The most serious constraint to wider use of RK is that the analyst must carry out various steps in different software environments, both statistical and GIS.

Climate controls on vegetation phenological patterns in northern mid‐ and high latitudes inferred from MODIS data

AbstractRecent studies using both field measurements and satellite‐derived‐vegetation indices have demonstrated that global warming is influencing vegetation growth and phenology. To accurately predict the future response of vegetation to climate variation, a thorough understanding of vegetation phenological cycles and their relationship to temperature and precipitation is required. In this paper, vegetation phenological transition dates identified using data from the moderate‐resolution imaging spectroradiometer (MODIS) in 2001 are linked with MODIS land surface temperature (LST) data from the northern hemisphere between 35°N and 70°N. The results show well‐defined patterns dependent on latitude, in which vegetation greenup gradually migrates northward starting in March, and dormancy spreads southward from late September. Among natural vegetation land‐cover types, the growing‐season length for forests is strongly correlated with variation in mean annual LST. For urban areas, the onset of greenup is 4–9 days earlier on average, and the onset of dormancy is about 2–16 days later, relative to adjacent natural vegetation. This difference (especially for urban vs. forests) is apparently related to urban heat island effects that result in both the average spring temperature and the mean annual temperature in urban areas being about 1–3°C higher relative to rural areas. The results also indicate that urban heat island effects on vegetation phenology are stronger in North America than in Europe and Asia. Finally, the onset of forest greenup at continental scales can be effectively described using a thermal time‐chilling model, which can be used to infer the delay or advance of greenup onset in relation to climatic warming at global scale.