Remote Sensing — Open Access Journal

Synthetic aperture radar tomography (TomoSAR) has been proven to be a useful way to reconstruct vertical structure over forest areas with P-band images, on account of its three-dimensional imaging ability. In the case of a small number of non-uniformly distributed acquisitions, compressive sensing (CS) is generally adopted in TomoSAR. However, the performance of CS depends on the selected hyperparameter, which is closely related to the noise of a pixel. In this paper, to overcome this limitation, we propose a sparse iterative covariance-based estimation (SPICE) approach based on the wavelet and orthogonal sparse basis (W&O-SPICE) for application over forest areas. SPICE is a sparse spectral estimation method that achieves a high vertical resolution, and takes account of the noise adaptively for each resolution cell. Thus, it does not require the user to select a hyperparameter. Furthermore, the used sparse basis not only ensures the sparsity of the forest canopy scattering contribution, but it can also keep the original sparse information of the ground contribution. The proposed method was tested in simulated experiments and the results demonstrated that W&O-SPICE can successfully reconstruct the vertical structure of a forest. Moreover, three P-band fully polarimetric airborne SAR images with non-uniformly distributed baselines were applied to reconstruct the vertical structure of a tropical forest in Mabounie, Gabon. The underlying topography and forest height were estimated, and the root-mean-square errors (RMSEs) were 6.40 m and 4.50 m with respect to the LiDAR digital terrain model (DTM) and canopy height model (CHM), respectively. In addition, W&O-SPICE showed a better performance than W&O-CS, beamforming, Capon, and the iterative adaptive approach (IAA). Full article

Leaf chlorophyll content (LCC) provides valuable information about the nutrition and photosynthesis statuses of crops. Vegetation index-based methods have been widely used in crop management studies for the non-destructive estimation of LCC using remote sensing technology. However, many published vegetation indices are sensitive to crop canopy structure, especially the leaf area index (LAI), when crop canopy spectra are used. Herein, to address this issue, we propose four new spectral indices (The red-edge-chlorophyll absorption index (RECAI), the red-edge-chlorophyll absorption index/optimized soil-adjusted vegetation index (RECAI/OSAVI), the red-edge-chlorophyll absorption index/ the triangular vegetation index (RECAI/TVI), and the red-edge-chlorophyll absorption index/the modified triangular vegetation index(RECAI/MTVI2)) and evaluate their performance for LCC retrieval by comparing their results with those of eight published spectral indices that are commonly used to estimate LCC. A total of 456 winter wheat canopy spectral data corresponding to physiological parameters in a wide range of species, growth stages, stress treatments, and growing seasons were collected. Five regression models (linear, power, exponential, polynomial, and logarithmic) were built to estimate LCC in this study. The results indicated that the newly proposed integrated RECAI/TVI exhibited the highest LCC predictive accuracy among all indices, where R² values increased by more than 13.09% and RMSE values reduced by more than 6.22%. While this index exhibited the best association with LCC (0.708** ≤ r ≤ 0.819**) among all indices, RECAI/TVI exhibited no significant relationship with LAI (0.029 ≤ r ≤ 0.167), making it largely insensitive to LAI changes. In terms of the effects of different field management measures, the LCC predictive accuracy by RECAI/TVI can be influenced by erective winter wheat varieties, low N fertilizer application density, no water application, and early sowing dates. In general, the newly developed integrated RECAI/TVI was sensitive to winter wheat LCC with a reduction in the influence of LAI. This index has strong potential for monitoring winter wheat nitrogen status and precision nitrogen management. However, further studies are required to test this index with more diverse datasets and different crops. Full article

In this study, the China Hourly Merged Precipitation Analysis (CHMPA) data which combines the satellite-retrieved Climate Prediction Center Morphing (CMORPH) with the automatic weather station precipitation observations is firstly assimilated into the Weather Research and Forecasting (WRF) model using the Four-Dimensional Variational (4DVar) method. The analyses and subsequent forecasts of heavy rainfall during Meiyu season occurred in July 2013 over eastern China is evaluated. Besides, the sensitivity of rainfall forecast skill of assimilating the CHMPA data to the rainfall error, the rainfall thinning distance, and the rainfall accumulation time within assimilation window are investigated in this study. Then, the impact of 4DVar data assimilation with and without CHMPA rainfall data is evaluated to show how the assimilation of CHMPA impacts the precipitation simulations. It is found that assimilation of the CHMPA data helps to produce a better short-range precipitation forecast in this study. The rainfall fields after assimilation of CHMPA is closer to observations in terms of quantity and pattern. However, the leading time of improved forecast is limited to about 18 hours. It is also found that CHMPA data assimilation produces stronger realistic moisture divergence, precipitabale water field and the vertical wind field in the forecasting fields, which eventually contributes to the improved forecast of heavy rainfall. This study can provide references for the assimilation of CHMPA data into the WRF model using 4DVar, which is valuable for limited-area numerical weather prediction and hydrological applications. Full article

Bathing water quality has been monitored in the west coast of Tangier, Morocco due to increased urban and industrial discharge through the Boukhalef river, using in-situ bacteriological measurements which demand high economical and temporal costs. In this study, Landsat 8 Thermal Infrared Sensor (TIRS) images were used as an alternative to the classical method, for determining bathing water quality to help decision makers obtain up-to-date and cost-effective information for coastal environment protection. For this purpose, during spring and summer 2017, seven sampling points were examined in terms of bacteriological parameters: Total Coliforms (TC), Faecal Coliforms (FC), Intestinal Enterococci (IE) and Escherichia coli (E. coli). Also, a spatial-temporal analysis was performed in this temporal window to detect temperature anomalies and their spatial distribution along the coastal bathing area. In addition, a relationship between in-situ bacteriological parameter measurements and temperature from satellite images was analyzed. The results of the water temperature distribution showed the highest values next to the Boukhalef river mouth, as well as the poorest water quality according to in-situ measurements, while lower values and better water quality status were observed moving away from the Boukhalef river mouth. The relationship between water temperature and bacterial concentration showed a high correlation coefficient (R² = 0.85). Consequently, the model development approaches used may be useful in estimating bacterial concentration in coastal bathing areas and can serve to create a monitoring system to support decision makers in the protection actions of the coast. Full article

Studying wheat phenology can greatly enhance our understanding of how wheat growth responds to climate change, and guide us to reasonably confront its influence. However, comprehensive global-scale wheat phenology–climate analysis is still lacking. In this study, we extracted the wheat harvest date (WHD) from 1981–2014 from satellite data using threshold-, logistic-, and shape-based methods. Then, we analyzed the effects of heat and drought stress on WHD based on gridded daily temperature and monthly drought data (the Palmer drought severity index (PDSI) and the standardized precipitation evapotranspiration index (SPEI)) over global wheat-growing areas. The results show that WHD was generally delayed from the low to mid latitudes. With respect to variation trends, we detected a significant advancement of WHD in 32.1% of the world’s wheat-growing areas since 1981, with an average changing rate of −0.25 days/yr. A significant negative correlation was identified between WHD and the prior three months’ normal-growing-degree-days across 50.4% of the study region, which implies that greater preseason effective temperature accumulation may cause WHD to occur earlier. Meanwhile, WHD was also found to be significantly and negatively correlated with the prior three months’ extreme-growing-degree-days across only 9.6% of the study region (mainly located in northern South Asia and north Central-West Asia). The effects of extreme heat stress were weaker than those of normal thermal conditions. When extreme drought (measured by PDSI/SPEI) occurred in the current month, in the month prior to WHD, and in the second month prior to WHD, it forced WHD to advance by about 9.0/8.1 days, 13.8/12.2 days, and 10.8/5.3 days compared to normal conditions, respectively. In conclusion, we highlight the effects that heat and drought stress have on advancing wheat harvest timing, which should be a research focus under future climate change. Full article

Fusion of remote sensing data often improves vegetation mapping, compared to using data from only a single source. The effectiveness of this fusion is subject to many factors, including the type of data, collection method, and purpose of the analysis. In this study, we compare the usefulness of hyperspectral (HS) and Airborne Laser System (ALS) data fusion acquired in separate flights, Multiple Flights Data Fusion (MFDF), and during a single flight through Instrument Fusion (IF) for the classification of non-forest vegetation. An area of 6.75 km² was selected, where hyperspectral and ALS data was collected during two flights in 2015 and one flight in 2017. This data was used to classify three non-forest Natura 2000 habitats i.e., Xeric sand calcareous grasslands (code 6120), alluvial meadows of river valleys of the Cnidion dubii (code 6440), species-rich Nardus grasslands (code 6230) using a Random Forest classifier. Our findings show that it is not possible to determine which sensor, HS, or ALS used independently leads to a higher classification accuracy for investigated Natura 2000 habitats. Concurrently, increased stability and consistency of classification results was confirmed, regardless of the type of fusion used; IF, MFDF and varied information relevance of single sensor data. The research shows that the manner of data collection, using MFDF or IF, does not determine the level of relevance of ALS or HS data. The analysis of fusion effectiveness, gauged as the accuracy of the classification result and time consumed for data collection, has shown a superiority of IF over MFDF. IF delivered classification results that are more accurate compared to MFDF. IF is always cheaper than MFDF and the difference in effectiveness of both methods becomes more pronounced when the area of aerial data collection becomes larger. Full article

A borehole radar investigation was performed at the Sanzuodian reservoir, Chifeng, China to assess possible leakage paths located in the deep dam foundation. The key methodologies used include both single-hole reflection and cross-hole radar tomography, which make a high-resolution identification of the hydraulic connection paths between upstream and downstream sides possible. The leakage paths are characterized by direct wave loss due to high electromagnetic attenuation in the single-hole reflection profile and the nearly horizontal-banded low-velocity zone in the cross-hole velocity tomography due to possible large internal erosion. Meanwhile, some small structures inside the dam, including the core wall thickness changing point, the connecting point between asphalt and concrete walls, and the contacting interface between the dry and the water-saturated formations can be identified from the single-hole reflection profile clearly. Interpreted leakage paths are proven by the water flow measurement. Borehole radar is a useful high-resolution tool, suitable for deep leakage detection and evaluation. Full article

An early assessment of biological invasions is important for initiating conservation strategies. Instrumental progress in high spatial resolution (HSR) multispectral satellite sensors greatly facilitates ecosystems’ monitoring capability at an increasingly smaller scale. However, species detection is still challenging in environments characterized by a high variability of vegetation mixing along with other elements, such as water, sediment, and biofilm. In this study, we explore the potential of Pléiades HSR multispectral images to detect and monitor changes in the salt marshes of the Bay of Arcachon (SW France), after the invasion of Spartina anglica. Due to the small size of Spartina patches, the spatial and temporal monitoring of Spartina species focuses on the analysis of five multispectral images at a spatial resolution of 2 m, acquired at the study site between 2013 and 2017. To distinguish between the different types of vegetation, various techniques for land use classification were evaluated. A description and interpretation of the results are based on a set of ground truth data, including field reflectance, a drone flight, historical aerial photographs, GNSS and photographic surveys. A preliminary qualitative analysis of NDVI maps showed that a multi-temporal approach, taking into account a delayed development of species, could be successfully used to discriminate Spartina species (sp.). Then, supervised and unsupervised classifications, used for the identification of Spartina sp., were evaluated. The performance of the species identification was highly dependent on the degree of environmental noise present in the image, which is season-dependent. The accurate identification of the native Spartina was higher than 75%, a result strongly affected by intra-patch variability and, specifically, by the presence of areas with a low vegetation density. Further, for the invasive Spartina anglica, when using a supervised classifier, rather than an unsupervised one, the accuracy of the classification increases from 10% to 90%. However, both algorithms highly overestimate the areas assigned to this species. Finally, the results highlight that the identification of the invasive species is highly dependent both on the seasonal presence of itinerant biological features and the size of vegetation patches. Further, we believe that the results could be strongly improved by a coupled approach, which combines spectral and spatial information, i.e., pattern-recognition techniques. Full article

The problem of soil salinization has always been a global problem involving resource, environmental, and ecological issues, and is closely related to the sustainable development of the social economy. Remote sensing provides an effective technical means for soil salinity identification and quantification research. This study focused on the estimation of the soil salt content in saline-alkali soils and applied the Successive Projections Algorithm (SPA) method to the estimation model; twelve spectral forms were applied in the estimation model of the spectra and soil salt content. Regression modeling was performed using the Partial Least Squares Regression (PLSR) method. Proximal-field spectral measurements data and soil samples were collected in the Yellow River Irrigation regions of Shizuishan City. A total of 60 samples were collected. The results showed that application of the SPA method improved the modeled determination coefficient (R²) and the ratio of performance to deviation (RPD), and reduced the modeled root mean square error (RMSE) and the percentage root mean square error (RMSE%); the maximum value of R² increased by 0.22, the maximum value of RPD increased by 0.97, the maximum value of the RMSE decreased by 0.098 and the maximum value of the RMSE% decreased by 8.52%. The SPA–PLSR model, based on the first derivative of reflectivity (FD), the FD–SPA–PLSR model, showed the best results, with an R² value of 0.89, an RPD value of 2.72, an RMSE value of 0.177, and RMSE% value of 11.81%. The results of this study demonstrated the applicability of the SPA method in the estimation of soil salinity, by using field spectroscopy data. The study provided a reference for a subsequent study of the hyperspectral estimation of soil salinity, and the proximal sensing data from a low distance, in this study, could provide detailed data for use in future remote sensing studies. Full article

This work presents a methodology for the short-term forecast of intense rainfall based on a neural network and the integration of Global Navigation and Positioning System (GNSS) and meteorological data. Precipitable water vapor (PWV) derived from GNSS is combined with surface pressure, surface temperature and relative humidity obtained continuously from a ground-based meteorological station. Five years of GNSS data from one station in Lisbon, Portugal, are processed. Data for precipitation forecast are also collected from the meteorological station. Spaceborne Spinning Enhanced Visible and Infrared Imager (SEVIRI) data of cloud top measurements are also gathered, providing collocated information on an hourly basis. In previous studies it was found that the time-varying PWV is correlated with rainfall and can be used to detected heavy rain. However, a significant number of false positives were found, meaning that the evolution of PWV does not contain enough information to infer future rain. In this work, a nonlinear autoregressive exogenous neural network model (NARX) is used to process the GNSS and meteorological data to forecast the hourly precipitation. The proposed methodology improves the detection of intense rainfall events and reduces the number of false positives, with a good classification score varying from 63% up to 72% and a false positive rate of 36% down to 21%, for the tested years in the dataset. A score of 64% for intense rain events classification with 22% false positive rate is obtained for the most recent years. The method also achieves an almost 100% hit rate for the rain vs no rain detection, with close to no false alarms. Full article

A barchan dune near Tarfaya in Morocco has been surveyed using ground-penetrating radar (GPR) revealing packages of dipping strata within the dune that are truncated by bounding surfaces. The bounding surfaces dip in the downwind direction, truncate sets of cross-stratification, and are themselves downlapped by dipping strata. Models of aeolian strata suggest that the bounding surfaces could be reactivation surfaces, an erosion surface formed when a dune is reshaped by a change in wind. Alternatively, they could be superposition surfaces formed by smaller bedforms migration over the dune surface. These two hypotheses are tested using a combination of field and satellite observations. The average annual migration rate for the barchan dune derived from satellite images, gives an annual migration rate of 21.4 myr⁻¹. The number of reactivation surfaces imaged within the dune by GPR appears to scale with the annual migrating rate and dune turnover time suggesting that at this location, annual cycles in the wind regime are a potential control on dune stratigraphy with reactivation surfaces generated by changes in the wind direction, including wind reversals in the winter months. Alternatively, it is hypothesized that erosion in the lee of small superimposed bedforms as they pass the dune crest and approach the brink at the top of the slipface will create superposition surfaces. The migration rate of superimposed bedforms with a wavelength of 20 m has been measured at 2 mday⁻¹. This suggests that small superimposed bedforms will arrive at the dune crest approximately every 10 days. Thus, bounding surface created by erosion in the lee of superimposed dunes will be very common. Given that the turnover time of the barchan dune is estimated at 4.3 years, the number of superposition surfaces produced by the faster bedforms could be more than 100. The number of bounding surface imaged by a GPR profile along the length of the dune appears to support the wind-driven reactivation hypothesis. However, a GPR profile across the dune images many small trough sets, instead of a single slipface, suggesting that superimposed dunes play an important role in the stratigraphy of a relatively simple barchan dune. Full article

It has been found that the concentration of atmospheric methane (CH₄) has rapidly increased since 2007 after a decade of nearly constant concentration in the atmosphere. As an important greenhouse gas, such an increase could enhance the threat of global warming. To better quantify this increasing trend, a novel statistic method, i.e. the Ensemble Empirical Mode Decomposition (EEMD) method, was used to analyze the CH₄ trends from three different measurements: the mid–upper tropospheric CH₄ (MUT) from the space-borne measurements by the Atmospheric Infrared Sounder (AIRS), the CH₄ in the marine boundary layer (MBL) from NOAA ground-based in-situ measurements, and the column-averaged CH₄ in the atmosphere (X_CH4) from the ground-based up-looking Fourier Transform Spectrometers at Total Carbon Column Observing Network (TCCON) and the Network for the Detection of Atmospheric Composition Change (NDACC). Comparison of the CH₄ trends in the mid–upper troposphere, lower troposphere, and the column average from these three data sets shows that, overall, these trends agree well in capturing the abrupt CH₄ increase in 2007 (the first peak) and an even faster increase after 2013 (the second peak) over the globe. The increased rates of CH₄ in the MUT, as observed by AIRS, are overall smaller than CH₄ in MBL and the column-average CH₄. During 2009–2011, there was a dip in the increase rate for CH₄ in MBL, and the MUT-CH₄ increase rate was almost negligible in the mid-high latitude regions. The increase of the column-average CH₄ also reached the minimum during 2009–2011 accordingly, suggesting that the trends of CH₄ are not only impacted by the surface emission, however that they also may be impacted by other processes like transport and chemical reaction loss associated with [OH]. One advantage of the EEMD analysis is to derive the monthly rate and the results show that the frequency of the variability of CH₄ increase rates in the mid–high northern latitude regions is larger than those in the tropics and southern hemisphere. Full article

Many deep learning models, such as convolutional neural network (CNN) and recurrent neural network (RNN), have been successfully applied to extracting deep features for hyperspectral tasks. Hyperspectral image classification allows distinguishing the characterization of land covers by utilizing their abundant information. Motivated by the attention mechanism of the human visual system, in this study, we propose a spectral-spatial attention network for hyperspectral image classification. In our method, RNN with attention can learn inner spectral correlations within a continuous spectrum, while CNN with attention is designed to focus on saliency features and spatial relevance between neighboring pixels in the spatial dimension. Experimental results demonstrate that our method can fully utilize the spectral and spatial information to obtain competitive performance. Full article

The interest of the scientific community on the remote observation of sun‐induced chlorophyll fluorescence (SIF) has increased in the recent years. In this context, hyperspectral ground measurements play a crucial role in the calibration and validation of future satellite missions. For this reason, the European cooperation in science and technology (COST) Action ES1309 OPTIMISE has compiled three papers on instrument characterization, measurement setups and protocols, and retrieval methods (current paper). This study is divided in two sections; first, we evaluated the uncertainties in SIF retrieval methods (e.g., Fraunhofer line depth (FLD) approaches and spectral fitting method (SFM)) for a combination of off-the-shelf commercial spectrometers. Secondly, we evaluated how an erroneous implementation of the retrieval methods increases the uncertainty in the estimated SIF values. Results show that the SFM approach applied to high-resolution spectra provided the most reliable SIF retrieval with a relative error (RE) ≤6% and <5% for F₆₈₇ and F₇₆₀, respectively. Furthermore, although the SFM was the least affected by an inaccurate definition of the absorption spectral window (RE = 5%) and/or interpolation strategy (RE = 15%–30%), we observed a sensitivity of the SIF retrieval for the simulated training data underlying the SFM model implementation. Full article

Imagery from medium resolution satellites, such as Landsat, have long been used to map forest disturbances in the tropics. However, the Landsat spatial resolution (30 m) has often been considered too coarse for reliably mapping small-scale selective logging. Imagery from the recently launched Sentinel-2 sensor, with a resampled 10 m spatial resolution, may improve the detection of forest disturbances. This study compared the performance of Landsat 8 and Sentinel-2 data for the detection of selective logging in an area located in the Brazilian Amazon. Logging impacts in seven areas, which had governmental authorization for harvesting timber, were mapped by calculating the difference of a self-referenced normalized burn ratio (ΔrNBR) index over corresponding time periods (2016–2017) for imagery of both satellite sensors. A robust reference dataset was built using both high- and very-high-resolution imagery. It was used to define optimum ΔrNBR thresholds for forest disturbance maps, via a bootstrapping procedure, and for estimating accuracies and areas. A further assessment of our approach was also performed in three unlogged areas. Additionally, field data regarding logging infrastructure were collected in the seven study sites where logging occurred. Both satellites showed the same performance in terms of accuracy, with area-adjusted overall accuracies of 96.7% and 95.7% for Sentinel-2 and Landsat 8, respectively. However, Landsat 8 mapped 36.9% more area of selective logging compared to Sentinel-2 data. Logging infrastructure was better detected from Sentinel-2 (43.2%) than Landsat 8 (35.5%) data, confirming its potential for mapping small-scale logging. We assessed the impacted area by selective logging with a regular 300 m × 300 m grid over the pixel-based results, leading to 1143 ha and 1197 ha of disturbed forest on Sentinel-2 and Landsat 8 data, respectively. No substantial differences in terms of accuracy were found by adding three unlogged areas to the original seven study sites. Full article

Growing interest in the proximal sensing of sun‐induced chlorophyll fluorescence (SIF) has been boosted by space-based retrievals and up-coming missions such as the FLuorescence EXplorer (FLEX). The European COST Action ES1309 “Innovative optical tools for proximal sensing of ecophysiological processes” (OPTIMISE, ES1309; https://optimise.dcs.aber.ac.uk/) has produced three manuscripts addressing the main current challenges in this field. This article provides a framework to model the impact of different instrument noise and bias on the retrieval of SIF; and to assess uncertainty requirements for the calibration and characterization of state-of-the-art SIF-oriented spectroradiometers. We developed a sensor simulator capable of reproducing biases and noises usually found in field spectroradiometers. First the sensor simulator was calibrated and characterized using synthetic datasets of known uncertainties defined from laboratory measurements and literature. Secondly, we used the sensor simulator and the characterized sensor models to simulate the acquisition of atmospheric and vegetation radiances from a synthetic dataset. Each of the sensor models predicted biases with propagated uncertainties that modified the simulated measurements as a function of different factors. Finally, the impact of each sensor model on SIF retrieval was analyzed. Results show that SIF retrieval can be significantly affected in situations where reflectance factors are barely modified. SIF errors were found to correlate with drivers of instrumental-induced biases which are as also drivers of plant physiology. This jeopardizes not only the retrieval of SIF, but also the understanding of its relationship with vegetation function, the study of diel and seasonal cycles and the validation of remote sensing SIF products. Further work is needed to determine the optimal requirements in terms of sensor design, characterization and signal correction for SIF retrieval by proximal sensing. In addition, evaluation/validation methods to characterize and correct instrumental responses should be developed and used to test sensors performance in operational conditions. Full article

It is widely acknowledged that urban form significantly affects urban thermal environment, which is a key element to adapt and mitigate extreme high temperature weather in high-density urban areas. However, few studies have discussed the impact of physical urban form features on the land surface temperature (LST) from a perspective of comprehensive urban spatial structures. This study used the ordinary least-squares regression (OLS) and random forest regression (RF) to distinguish the relative contributions of urban form metrics on LST at three observation scales. Results of this study indicate that more than 90% of the LST variations were explained by selected urban form metrics using RF. Effects of the magnitude and direction of urban form metrics on LST varied with the changes of seasons and observation scales. Overall, building morphology and urban ecological infrastructure had dominant effects on LST variations in high-density urban centers. Urban green space and water bodies demonstrated stronger cooling effects, especially in summer. Building density (BD) exhibited significant positive effects on LST, whereas the floor area ratio (FAR) showed a negative influence on LST. The results can be applied to investigate and implement urban thermal environment mitigation planning for city managers and planners. Full article

Evapotranspiration (ET) is a critical component of the water and energy balance of climate–soil–vegetation interactions and can account for a water loss of about 90% in arid regions. It is recognized that there are differences among different ET products, but it is not known what the range of this difference is and to what extent it impacts on water resources and ecosystem management. In this study, we assess the effects of value differences of five representative ET products on water resources and ecosystem management in the Murrumbidgee River catchment in Australia. The results show there are obvious differences in the annual and monthly ET values among these five ET products, which lead to huge differences on the estimations of mean annual runoff, soil water storage changes, and yearly irrigation water per area. Meanwhile, they result in different relationships between the annual gross primary productivity and ET and different water-use efficiency values for both forest and grassland, but the influence of ET variations on forest is less obvious than on grassland. The effects of the variations among the ET products on water resources and ecosystem management are remarkable and need to be the subject of more attention. Full article

Urbanization has become one of the most important human activities modifying the Earth’s land surfaces; and its impacts on tropical and subtropical cities (e.g., in South/Southeast Asia) are not fully understood. Colombo; the capital of Sri Lanka; has been urbanized for about 2000 years; due to its strategic position on the east–west sea trade routes. This study aims to investigate the characteristics of urban expansion and its impacts on land surface temperature in Colombo from 1988 to 2016; using a time-series of Landsat images. Urban land cover changes (ULCC) were derived from time-series satellite images with the assistance of machine learning methods. Urban density was selected as a measure of urbanization; derived from both the multi-buffer ring method and a gravity model; which were comparatively adopted to evaluate the impacts of ULCC on the changes in land surface temperature (LST) over the study period. The experimental results indicate that: (1) the urban land cover classification during the study period was conducted with satisfactory accuracy; with more than 80% for the overall accuracy and over 0.73 for the Kappa coefficient; (2) the Colombo Metropolitan Area exhibits a diffusion pattern of urban growth; especially along the west coastal line; from both the multi-buffer ring approach and the gravity model; (3) urban density was identified as having a positive relationship with LST through time; (4) there was a noticeable increase in the mean LST; of 5.24 °C for water surfaces; 5.92 °C for vegetation; 8.62 °C for bare land; and 8.94 °C for urban areas. The results provide a scientific reference for policy makers and urban planners working towards a healthy and sustainable Colombo Metropolitan Area. Full article