Physical assessment of coastal vulnerability under enhanced land subsidence in Semarang,Indonesia, using multi-sensor satellite data

Research has been conducted in Semarang, Indonesia, to assess coastal vulnerability under enhanced land subsidence using multi-sensor satellite data, including the Advanced Land Observing Satellite (ALOS) Phased Array type L-band SAR (PALSAR), Landsat TM, IKONOS, and TOPEX/Poseidon. A coastal vulnerability index (CVI) was constructed to estimate the level of vulnerability of a coastline approximately 48.68?km in length using seven physical variables, namely, land subsidence, relative sea level change, coastal geomorphology, coastal slope, shoreline change, mean tidal range, and significant wave height. A comparison was also performed between a CVI calculated using seven parameters and a CVI using six parameters, the latter of which excludes the land subsidence parameter, to determine the effects of land subsidence during the coastal vulnerability assessment. This study showed that the accuracy of coastal vulnerability was increased 40% by adding the land subsidence factor (i.e., CVI 6 parameters?=?53%, CVI 7 parameters?=?93%). Moreover, Kappa coefficient indicated very good agreement (0.90) for CVI 7 parameters and fair agreement (0.3) for CVI 6 parameters. The results indicate that the area of very high vulnerability increased by 7% when land subsidence was added. Hence, using the CVI calculation including land subsidence parameters, the very high vulnerability area is determined to be 20% of the total coastline or 9.7?km of the total 48.7?km of coastline. This study proved that land subsidence has significant influence on coastal vulnerability in Semarang.     

Simulation of the land surface temperature from moon-based Earth observations

The land surface temperature (LST) is a key parameter for the Earth’s energy balance. As a natural satellite of the Earth, the orbital of the moon differs from that of current Earth observation satellites. It is a new way to measure the land surface temperature from the moon and has many advantages compared with artificial satellites. In this paper, we present a new method for simulating the LST measured by moon-based Earth observations. Firstly, a modified land-surface diurnal temperature cycle (DTC) method is applied to obtain the global LST at the same coordinated universal time (UTC) using the Moderate Resolution Imaging Spectroradiometer (MODIS) LST products. The lunar elevation angles calculated using the ephemeris data (DE405) from the Jet Propulsion Laboratory (JPL) were then applied to simulate the Earth coverage observed from the moon. At the same time, the modified DTC model was validated using in situ data, MODIS LST products, and the FengYun-2F (FY-2F) LST, respectively. The results show that the fitting accuracy (root-mean-square error, RMSE) of the modified DTC model is not greater than 0.72?°C for eight in situ stations with different land cover types, and the maximum fitting RMSE of the modified model is smaller than that of current DTC models. By the comparison of the simulated LST with MODIS and FY-2F LST products, the errors of the results were feasible and accredited, and the simulated global LST has a reasonable spatiotemporal distribution and change trend. The simulated LST data can therefore be used as base datasets to simulate the thermal infrared imagery from moon-based Earth observations in future research.     

A comparison of different regression models for downscaling Landsat and MODIS land surface temperature images over heterogeneous landscape

Remotely sensed high spatial resolution thermal images are required for various applications in natural resource management. At present, availability of high spatial resolution (<200 m) thermal images are limited. The temporal resolution of such images is also low. Whereas, coarser spatial resolution (∼1000 m) thermal images with high revisiting capability (∼1 day) are freely available. To bridge this gap, present study attempts to downscale coarser spatial resolution thermal image to finer spatial resolution using relationships between land surface temperature (LST) and vegetation indices over a heterogeneous landscape of India. Five regression based models namely (i) Disaggregation of Radiometric Temperature (DisTrad), (ii) Temperature Sharpening (TsHARP), (iii) TsHARP with local variant, (iv) Least median square regression downscaling (LMS_DS) and (v) Pace regression downscaling (PR_DS) are applied to downscale LST of Landsat Thematic Mapper (TM) and Terra MODIS (Moderate Resolution Imaging Spectroradiometer) images. All the five models are first evaluated on Landsat image aggregated to 960 m resolution and downscaled to 480 m and 240 m resolution. The downscale accuracy is achieved using LMS_DS and PR_DS models at 240 m resolution at 0.61 °C and 0.75 °C respectively. MODIS data downscaled from 1000 m to 250 m spatial resolution results root mean square error (RMSE) of 1.43 °C and 1.62 °C for LMS_DS and PR_DS models, respectively. The LMS_DS model is less sensitive to outliers in heterogeneous landscape and provides higher accuracy when compared to other models. Downscaling model is found to be suitable for agricultural and vegetated landscapes up to a spatial resolution of 250 m but not applicable to water bodies, dry river bed sand sandy open areas.     

Influence of land surface parameters on the spatio-seasonal land surface temperature regime in rural West Bengal,India

The magnitude and causes of changes in the land surface temperature of rural areas have not been extensively studied. The thermal band of Landsat imagery is taken to extract winter, summer, and monsoon season land surface temperature (LST) and relate it to surface parameters over a 30-year period. From the extracted parameters constructed a prospective surface temperature (PST) model using Multivariate Adaptive Regression Splines. The Chandrabhaga river basin in West Bengal of the lateritic Rarh Tract at the Chota Nagpur Plateau fringe was chosen as the study area because it is far from urban influences, to avoid the well-known heat island effect. Over the study period, summer and winter average LST increased linearly by 0.085?°C/y and 0.016?°C/y respectively. These results were validated with air temperature (RMSE?=?x and y, respectively). Over time more of the area is in the higher temperature zones, e.g., in April 2011, 4% area exceeded >32°, whereas in 2015 this proportion reached 52%. PST models of all the seasons were moderate to highly correlate (0.57–0.87) with actual LST, showing the value of this model. It also revealed the relative importance of the regional factors. Based on this information factor management is a scientific step to restrict or minimize the temperature rise effect.     

Validation of non-linear split window algorithm for land surface temperature estimation using Sentinel-3 satellite imagery: Case study; Tehran Province,Iran

In recent years, land surface temperature (LST) has become critical in environmental studies and earth science. Remote sensing technology enables spatiotemporal monitoring of this parameter on large scales. This parameter can be estimated by satellite images with at least one thermal band. Sentinel-3 SLSTR data provide LST products with a spatial resolution of 1 km. In this research, direct and indirect validation procedures were employed to evaluate the Sentinel-3 SLSTR LST products over the study area in different seasons from 2018 to 2019. The validation method was based on the absolute (direct) evaluation of this product with field data and comparison (indirect) evaluation with the MODIS LST product and the estimated LST using the non-linear split-window (NSW) algorithm. Also, two emissivity estimation methods, (1) NDVI thresholding method (NDVI-THM) and (2) classification-based emissivity method (CBEM), were used to estimate the LST using the NSW method according to the two thermal bands of Sentinel-3 images. Then, the accuracy of these methods in estimating LST was evaluated using field data and temporal changes of vegetation, which the NDVI-THM method generated better results. For indirect evaluation between the Sentinel-3 LST product, MODIS LST product, and LST estimated using NSW, four filters based on spatial and temporal separates between pairs of pixels and pixel quality were used to ensure the accuracy and consistency of the compared pairs of a pixel. In general, the accuracy results of the LST products of MODIS and Sentinel-3, and LST estimated using NSW showed a similar trend for LST changes during the seasons. With respect to the two absolute and comparative validations for the Sentinel-3 LST products, summer with the highest values of bias (?1.24 K), standard deviation (StDv = 2.66 K), and RMSE (2.43 K), and winter with the lowest ones (bias of 0.14 K, StDv of 1.13 K, and RMSE of 1.12 K) provided the worst and best results for the seasons in the period of 2018–2019, respectively. According to both absolute and comparative evaluation results, the Sentinel-3 SLSTR LST products provided reliable results for all seasons on a large temporal and spatial scale over our studied area.     

A novel method for destriping of OCM-2 data and radiometric performance analysis for improved ocean color data products

Despite the capability of Ocean Color Monitor aboard Oceansat-2 satellite to provide frequent, high-spatial resolution, visible and near-infrared images for scientific research on coastal zones and climate data records over the global ocean, the generation of science quality ocean color products from OCM-2 data has been hampered by serious vertical striping artifacts and poor calibration of detectors. These along-track stripes are the results of variations in the relative response of the individual detectors of the OCM-2 CCD array. The random unsystematic stripes and bandings on the scene edges affect both visual interpretation and radiometric integrity of remotely sensed data, contribute to confusion in the aerosol correction process, and multiply and propagate into higher level ocean color products generated by atmospheric correction and bio-optical algorithms. Despite a number of destriping algorithms reported in the literature, complete removal of stripes without residual effects and signal distortion in both low- and high-level products is still challenging. Here, a new operational algorithm has been developed that employs an inverted gaussian function to estimate error fraction parameters, which are uncorrelated and vary in spatial, spectral and temporal domains. The algorithm is tested on a large number of OCM-2 scenes from Arabian Sea and Bay of Bengal waters contaminated with severe stripes. The destriping effectiveness of this approach is then evaluated by means of various qualitative and quantitative analyses, and by comparison with the results of the previously reported method. Clearly, the present method is more effective in terms of removing the stripe noise while preserving the radiometric integrity of the destriped OCM-2 data. Furthermore, a preliminary time-dependent calibration of the OCM-2 sensor is performed with several match-up in-situ data to evaluate its radiometric performance for ocean color applications. OCM-2 derived water-leaving radiance products obtained after calibration show a good consistency with in-situ and MODIS-Aqua observations, with errors less than the validated uncertainties of ±5% and ±35% endorsed for the remote-sensing measurements of water-leaving radiance and retrieval of chlorophyll concentrations respectively. The calibration results show a declining trend in detector sensitivity of the OCM-2 sensor, with a maximum effect in the shortwave spectrum, which provides evidence of sensor degradation and its profound effect on the striping artifacts in the OCM-2 data products.     

Modelling of air temperature using remote sensing and artificial neural network in Turkey

The aim of this research was to forecast monthly mean air temperature based on remote sensing and artificial neural network (ANN) data by using twenty cities over Turkey. ANN contained an input layer, hidden layer and an output layer. While city, month, altitude, latitude, longitude, monthly mean land surface temperatures were chosen as inputs, and monthly mean air temperature was chosen as output for network. Levenberg–Marquardt (LM) learning algorithms and tansig, logsig and linear transfer functions were used in the network. The data of Turkish State Meteorological Service (TSMS) and Technological Research Council of Turkey–Bilten for the period from 1995 to 2004 were chosen as training when the data of 2005 year were being used as test. Result of research was evaluated according to statistical rules. The best linear correlation coefficient (R), and root mean squared error (RMSE) between the estimated and measured values for monthly mean air temperature with ANN and remote sensing method were found to be 0.991–1.254 K, respectively.     

Effect of surface roughness on microwave brightness temperature from lunar surface: Numerical analysis with a hybrid method

A hybrid method, combining the radiative transfer theory and the method of moments (MoM), is proposed to study the potential effect of the lunar surface roughness on the microwave brightness temperature. The total upward emission reaching the lunar surface from below media is calculated by the radiative transfer theory, and then the brightness temperature is obtained by weighting the bidirectional transmission coefficients which is computed using the MoM. The method is validated by both flat and rough surface models with analytic solutions. With the hybrid method, brightness temperatures from simulated lunar model are calculated and compared to those from a flat layered model. The comparisons show that the effect of rough surface on brightness temperature cannot be ignored and also depends on many other factors, such as observation angle and polarizations. For vertical polarization, an optimal observation angle may exist to reduce the effect of surface roughness. These results indicate that the knowledge of lunar surface roughness is important in microwave remote sensing to the Moon and may probably provide a guide to lunar projects in future.     

基于DTW的HJ-1B卫星地表温度产品质量检验

HJ-1B卫星热红外数据应用广泛,但其地表温度反演产品的质量检验工作尚不完善。以黑河流域为研究区,利用普适性单通道算法得到HJ 1B地表温度,基于7个地面站点（下垫面为荒漠、沙漠、植被、农作物、雪地和湿地）同步观测资料和MODIS地表温度产品（MOD11A1）,引入动态时间规整方法（DTW）对站点处HJ 1B地表温度进行验证。试验结果表明：HJ 1B地表温度反演产品与地面观测值的偏差值在沙漠和荒漠站点为1K以内,均方根误差在05K左右;对于植被和农作物站点的偏差在2K以内,均方根误差为1~2K;基于DTW的验证对时序不匹配的数据评价结果与现有指标表现一致。HJ-1B地表温度反演产品与地面站点的相对偏差均低于其与MODIS地表温度反演产品的相对偏差。     

用温度梯度法测量壁面高温的研究

直接测量液体火箭发动机燃烧室的内壁温度是十分困难的。提出了温度梯度法,即通过测量冷却通道中肋片上的温度梯度来推算出内壁面温度的方法。理论分析和数值计算表明,燃烧室冷却肋片中心截面的温度分布可以用一个三次方程来表示。用最小二乘法拟合测试数据可以推算出内壁面温度。     

Water surface temperature estimation from Landsat 7 ETM+ thermal infrared data using the generalized single-channel method: Case study of Embalse del Río Tercero (Córdoba,Argentina)

Monitoring of warm distribution in water is fundamental to understand the performance and functioning of reservoirs and lakes. Surface water temperature is a key parameter in the physics of aquatic systems processes since it is closely related to the energy fluxes through the water–atmosphere interface. Remote sensing applied to water quality studies in inland waterbodies is a powerful tool that can provide additional information difficult to achieve by other means. The combination of good real-time coverage, spatial resolution and free availability of data makes Landsat system a proper alternative. Many papers have developed algorithms to retrieve surface temperature (principally, land surface temperature) from at-sensor and surface emissivity data. The aim of this study is to apply the single-channel generalized method (SCGM) developed by Jiménez-Muñoz and Sobrino (2003) for the estimation of water surface temperature from Landsat 7 ETM+ thermal bands. We consider a constant water emissivity value (0.9885) and we compare the results with radiative transfer classic method (RTM).     

Comparison of ANN and MLR models for estimating solar radiation in Turkey using NOAA/AVHRR data

In this paper, the estimation capacities of MLR and ANN are investigated to estimate monthly-average daily SR over Turkey. The satellite data are used for 73 different locations over Turkey. Land surface temperature, altitude, latitude, longitude and month are offered as the input variables for modeling ANN and MLR to get SR. Estimations of SR are evaluated with the meteorological values by using the statistical bases. The obtained results indicated that the ANN model could achieve a satisfactory performance when compared to the MLR model. Moreover, it is understood that more accurate results in estimation of SR are obtained in the use of satellite data, rather than the use of meteorological station data. Finally, the built ANN model is used to estimate the yearly average of daily SR over Turkey. As a result, satellite-based SR map for Turkey is generated.