As at the beginning of the growing season, vegetation fraction is low and the land’s surface is composed primarily of bare soil. Ts-day is usually much higher than Tmax during this period as more energy is partitioned into sensible heat flux from the soil as opposed to later in the year when crops have emerged and are transpiring (latent heat flux) . During the green-up phase of crops, the spatial coverage of vegetation increases, leading to transpirational cooling, increased latent heat fluxes, decreased sensible heat fluxes, and in general a reduced daytime GATD . During the senescence phase of crops, as maturity is reached, photosynthesis and transpiration decrease, the latent heat fluxes decreases, the sensible heat fluxes increase resulting in higher GATD . Thus, over crop areas Ts-day is typically much higher than Tmax at the beginning of the growing season, slightly lower than or equal to Tmax in the middle stage of the growing season and slightly higher than or equal to Tmax in the end of the growing seasons (Figure 6c). While in deciduous forest and developed area, Tmax increases with Ts-day proportionally and the seasonality effect is minimal (Figure 5a,c, Figure 6a,b).
Interestingly, Ts-night had higher correlation coefficient with daily Tmax than Ts-day in all the three land-cover types. Combining both Ts-day and Ts-night to estimate Tmax achieved even better accuracy than either variable individually. Compared to using only Ts-night, combining Ts-day and Ts-night have little improvement on Tmin estimation accuracy, as Ts-day was not relevant for Tmin estimation. Taken DOY into consideration, slight improvement of both Tmax and Tmin estimation accuracy was observed in crops and forest areas rather than developed areas. Both Ts-day and Ts-night from Terra are better explanatory variables for Tmax, while Ts-night from Aqua is a slight better (~0.2 °C) proxy for Tmin. The models had a general tendency to have lower performance of Tmax estimation in stations located in irrigated land and the areas with higher IDV, but a better performance of Tmax estimation during the period from June to August (the middle of the growing season) than either May or September (the beginning or end of the growing season) due to the spatial and temporal difference of air masses as well as irrigation. While there was no distinct spatial distribution pattern of Tmin estimation across the CornBelt. Some other factors such as cloud cover and other local conditions might also contribute to the difference of estimation accuracy of Tmin.
The correction using the gridded datasets did not affect the coefficient of determination values because the correction was based on a linear transformation, as shown in Equation (9), while the MAE and RMSE values were significantly reduced (Figures 3–8). When dailyairtemperature estimates were corrected using the NCEP/NCAR reanalysis data, MAE and RMSE values were reduced in all seasons for daily maximum airtemperature (Figures 3 and 4, ∆MAE = −0.28 to −1.93, ∆RMSE = −0.16 to −1.82), except LST during DJF. For daily minimum airtemperature, MAE and RMSE were improved only for AP and AP_shift during JJA. The correction of LST using the gridded datasets improved the estimation of maximum temperature better than minimum temperature, because surfacetemperature is known to be higher than airtemperature during the day, while it is close to airtemperature during the night [11,13]. The difference between surfacetemperature and airtemperature depends on a complex surface energy balance during the day, but the effect of solar radiation is minor during the night .
temperature, using landbased observation stations, especially in developing countries, and Nigeria in particular (Obinna, et al., 2013). As it is traditionally measured in the field at sparsely located, field observation stations yielded discrete LST measurements (Obinna, et al., 2013). This approach has long been adopted in Nigeria. Discreet measurements of LST are usually not a good representative of the LST of an area (Obinna, et al., 2013). Furthermore, weather station based techniques produce point estimates where as remote sensing methods produce actual values (Owen, Carlson, and Gillies, 1998 and Weligepolage, 2005). More often this is done by extrapolating the airtemperature data measured at weather station. Remote sensing might be a better alternative to the aforementioned methods (Ifatimehin, et’al 2010). The advantages of using remote sensed data are the availability of high resolution, reliable and repetitive coverage and proficiency of measurements of earth surface conditions (Owen, et al., 1998, Jakub et’al, 2015). Satellite-based thermal infrared (TIR) data is directly linked to the LST through the radiative transfer equation (National Aeronautic Space Agency, NASA 2015). The retrieval of the LST from remotely sensed TIR data has attracted much attention, and its history dates back to the 1970s (McMillin, 1975). Remote sensing is becoming a tool to reckon with in the evaluation and monitoring of environmental and ecological processes. Landsat is one of the most used data for environmental analysis. It is composed of seven bands for TM, eight for ETM and eleven for Landsat 8, they provide thermal data using just one long- wave infrared (LWIR) band, with a higher spatial resolution (NASA 2015). Although, there are other remote sensing data range from land observation to meteorological ones
The hourly airtemperature is measured with dry bulb thermometer and relative humidity is calculated from the dry and wet bulb thermometers readings. The maximum and minimum temperatures of a day are measured with the maximum and minimum thermometers respectively. The daily rate of evaporation is calculated from the difference in water levels inside an evaporation pan. The daily and hourly bright sunshine hours are measured from the burnt sunshine card which is placed under a crystal ball of a sunshine recorder. Hourly solar radiation is obtained from the readings of the solarimeter. Atmospheric pressure is taken hourly from a barometer. Hourly surface wind direction and speed are taken from the anemograph.
The details of “how” MCST identified and later corrected for the calibration drift, are explained in Sun et al. (2012) and references therein. We provide a simple explanation here. In addition to the radiometric calibration of each channel for each sensor, MCST quantifies the “response versus scan an- gle” (RVS). The RVS characterizes the imperfections and ge- ometrical issues that lead to a non-Lambertian response by the MODIS instrument, meaning that each observation must be corrected for the RVS corresponding to that particular viewing angle. Prior to launch, RVS for each channel, and for each MODIS sensor, was characterized in laboratory. Once in orbit, the calibration would be continually updated by mak- ing repeated observations of MODIS’ onboard reflectance calibrator (known as the solar diffuser) as well as the moon’s disc. It was assumed that the pre-launch RVS would remain throughout the mission. However, it is now understood that the angular characteristics of the solar diffuser (the reference for calibration) are also changing during MODIS lifetime, which would result in a time-dependent RVS. If unaccounted for, there would be a residual RVS error, which would lead to biases in L1B reflectance, leading to biases in aerosol or other products. This residual RVS error was identified by tak- ing long-term measurements of “pseudo-invariant” ground sites such as remote deserts (Chander et al., 2010), and com- paring instrument response at later dates with earlier mea- surements. It was in this way that MCST could confirm that there was a drift in the blue (0.47 µm) channel that was con- sistent with trend in retrieved AOD overland. Once the resid- ual drifts were identified they could be corrected for. As a result of these studies, the MCST introduced a new method (Sun et al., 2012) that was later adopted for deriving time and angular dependent calibration coefficients. This method has
Since most of the natural objects are not black, their ε irradiance power which is defined as the ratio of one object radiance to its black isothermal object radiance must be considered. Spectral radiance of a non-black object can be calculated by multiplication of irradiance power in its black isothermal object (Equation 1). Obviously, if the atmosphere had no influence on satellite views, surface LST could be calculated for certain irradiance power and radiation radiance. But in reality, in order to calculate surface LST accurately from satellite images, apart from irradiance power of the surface, we need to correct atmospheric influences. Different methods were suggested for these corrections since the 1970s most of which can be classified in three major groups: single band methods, separate window methods and multiple angle methods.
Nighttime PM (22:30) monthly mean land-surfacetemperature change on the Arctic (Figure 3(A)) shows in- creases in eleven out of 12 months with January having the largest increase in excess of 4 ˚C and August having the smallest increase (near zero) over the decade. February shows a modest decrease of 1 ˚C over the decade. In the sectors monthly land-surfacetemperature changes terrain-controlled variations. Eurasia (Figure 3(B)) has the largest magnitude increase of almost 8 ˚C during March by the end of the decade whereas the increase is small at the March beginning the decade. Eastern Russia-Western North America (Figure 3(C)) shows increases except for February and the ending decade March. Eastern North America-Western Europe (Figure 3(D)) shows December and February with decreases and increases in the remaining months over the decade.
The use of the  method for surfacetemperature is provide to be fruitful approach to studying inter-annual climate fluctuations, because they reveal time varying structure in the raw data or in the more traditional statis- tical analyses. Examination of the  method winter- time SAT over KSA has revealed support for the notion of extended “persistence” over several years, even though simple year-to-year persistence may be evident. The wintertime SAT of the area is characterized by warm periods 1993-2008 at all regions of KSA stations, and 1984-2008 in southern region stations. While cooling in the wintertime SAT appears for the short period of about 5 years, 1978-1982 and 1988-1992. A warm period was not uniform, continuous or of the same order. Recent warming has only occurred during the last two decades at most stations. These trends are in general consistence with the global trends in the mean surfacetemperature. The most probable cause of the observed warming in the recent climate change is a combination of internally and externally forced natural variability and anthropogenic sources.
Some pixels with high errors persist in the downscaled image, but the improvement in some areas is appreciable as qualitatively shown in the above sub-areas. To explore the downscal- ing performance in relation to the urban texture, the RMSE for pixels belonging to different land cover classes was evaluated. Three classes (built-up, vegetation and open water) were selected using the NDVI, NDBI and NDWI indices, settling a conservative index threshold based also on a visual inspection. An additional class of “mixed” pixels, not belonging to the previous three classes, was considered. Table 2 reports the RMSE comparing USGS and downscaled LST with respect to aircraft data.
Engine heat transfer phenomena have been studied extensively for many decades [3-8]. Numerous mathematical models have been proposed, including correlations based on dimensional analysis, which are widely accepted and, although they propose different heat fluxes, their evolution over the cycle is similar [7-8]. Many of these models include the gas-side wall temperature as a variable to obtain the heat flux through the chamber walls . In addition, computational fluid dynamics (CFD) and/or finite element method (FEM) codes used for heat transfer simulations require the estimation of this temperature to provide boundary conditions. Convergence is attained through an iterative process. [9-10]. Furthermore, thermal analyses require the gas side wall temperature to evaluate temperature distribution and the thermomechanical behaviour of components [11-13]. Other heat transfer analyses use the lumped capacitance method and also need the mean gas side temperature [14-15]. Even with very precise functions, it is not possible to obtain accurate heat flux [7,16] unless accurate wall temperature measurements are available.
resolve. Problems arise from resolution cell representation, station to station biases and consistency of data records. A more robust approach is to use a higher resolution remote sensing instrument to capture the spatial patterns. Airborne microwave radiometers at L-band frequency can achieve much finer resolution than their spaceborne counterparts. A field experiment for soil moisture validation of SMAP was conducted in southern Arizona in August 2015 called SMAP Validation Experiment 2015 (SMAPVEX15). In this experiment, an airborne L-band instrument PALS (Passive Active L-band Sensor) was deployed to measure an area consisting of three SMAP pixels on seven days. The SMAPVEX15 data set offers a uniquely appropriate reference soil moisture data set for testing the algorithm for two reasons. First, the soil moisture disaggregation methods utilizing LST perform optimally when surfacetemperature is controlled mainly by soil evaporation. This is generally the case in the SMAPVEX15 domain. Second, testing a downscaling algorithm requires at some spatial heterogeneity in the measured soil moisture fields. The experiment was designed to coincide with North American Monsoon, which resulted in small scale convective precipitation events that created very heterogeneous scenes in terms of soil moisture .
Afforestation has been proposed as a tool to mitigate climate change globally (UNFCCC, 2011), mainly because forests can store large amounts of carbon (Luyssaert et al., 2008; Le Quéré et al., 2017). In addition, changes in forest cover can cause a warming or cooling via an alteration of the exchange of energy and water between the Earth’s surface and the at- mosphere, i.e., the so-called biogeophysical effects (Bonan, 2008). Earth System models have been employed to assess how these biogeophysical effects affect the temperature of the surface (e.g., Bala et al., 2007; Pongratz et al., 2010; Davin and de Noblet-Ducoudré, 2010; Boisier et al., 2012; Devaraju et al., 2015; Li et al., 2016) and the temperature of the near-surfaceair (usually airtemperature 2 m above zero- plane displacement height) (e.g., Claussen et al., 2001; Gib- bard et al., 2005; Findell et al., 2006; Pitman et al., 2009; Bathiany et al., 2010; de Noblet-Ducoudré et al., 2012; Jones et al., 2013; Luyssaert et al., 2018). The different temper- ature variables that are considered in studies about defor- estation effects are relevant for different questions and ap- plications. Satellite-based studies on changes in radiometric surfacetemperature provide important information about the biophysical mechanisms of surface energy partitioning and thereby surface–atmosphere interactions (Duveiller et al., 2018). Compared to changes in surfacetemperature, changes in airtemperature may be considered more relevant for hu- man living conditions because of their importance, e.g., for the perceived temperature (e.g., Staiger et al., 2011). Within- and below-canopy airtemperature (which is not included in this study) is the most relevant variable for many organ- isms that live within forests (e.g., De Frenne et al., 2013; De Frenne et al., 2019). The coupling between ground temper- ature and airtemperature is strongly influenced by the type of vegetation that covers the surface (Baldocchi, 2013; Melo- Aguilar et al., 2018), but it remains unclear whether surfacetemperature and near-surfaceairtemperature respond differ- ently to deforestation in climate models. This is the focus of the present study.
The objective of this study was to analyze the relation of chlorophyll-a and sea surface water concentration distribution to the mackerel fish catch in the territorial waters of Kabupaten Indramayu and to estimate the map of mackerel fishing potential zone in the territorial waters of Kabupaten Indramayu. The method used in this research is survey method, descriptive discussion with quantitative and correlation approach. The research data includes sea surface water and chlorophyll-a which were obtained using satellite images from KPL Mina Sumitra Indramayu. The result of this study shows that the highest temporal distribution of sea surfacetemperature tends to occur in the transition season 1 (March-May). The temporal distribution of chlorophyll-a concentrations shows that values tend to be high in the West season (December-February). Distribution of sea surface temperatures tend to have a cooler pattern toward offshore waters and warmer towards coastal waters. Spatial distribution of chlorophyll-a concentration has a tendency to be smaller in offshore waters and higher in the coastal waters. In general, the correlation between sea surface water and chlorophyll-a on CPUE of mackerel fish was quite high. The correlation between sea surfacetemperature and CPUE of mackerel fish was 0.436. Correlation of chlorophyll-a concentration to CPUE of mackarel fish was 0,431. The estimation of the potential zone of mackerel fishing is located at the latitude line 5ºS and longitude line 108ºE.
Turkey is a country with an abundance of solar energy throughout the year for clean energy applications based on solar radiation. In this paper, the monthly average daily global radiation incident on a horizontal surface is determined using the FLM also in addition to the daily solar radiation for 12 months for individual years being used for the FLM approach. The meteorological data, such as daily values of ambient temperature and relative pressure duration of the evaluated cities for a year, are evaluated as input entry indicators. The FLM application for predicting the daily average solar radiation should fill the gap for the assessment of the efficiency of many applications of solar power processes at a certain site. The proposed novel methodology can provide more simplicity than many empirical models to evaluate solar radiation possibilities in provinces where a network of monitoring centers has not yet been installed in the country. The following concluding remarks are drawn from this paper:
DOI: 10.4236/am.2018.98069 1018 Applied Mathematics be used to describe the yearly mean temperature change. However, it does not have the ability to reflect any periodic temperature variation. The M2-type mod- el is the general Fourier series function. It assumes that the yearly mean temper- ature and amplitudes for the pre-selected, finite number of frequencies are con- stant. It might be accurate for a short period of time, such as one year. The problem with the M2-type model is that it cannot reflect the long-term average, the maximum and the minimum temperature changes with time as in Bhutiya- ni’s study . The M3-type model is an updated function from the M2-type model. It has the variable yearly mean temperature with time. However, it still does not have the ability to reflect the maximum and minimum temperature changes with time. The M4-type model is an improved form over the M2-type model in terms of allowing the variation of maximum and minimum tempera- tures, but still using the constant yearly mean temperature. The M5-type model combines the advantages of both the M3-type and M4-type of models.
This study used data recorded from a remote sensing satellite. Landsat platform use TM (Thematic Mapper) sensor. It is a multispectral scanning Earth surface. Images captured from such sensor usually has sharper spectral separation and greater image resolution. Other than that, it also has greater geometric accuracy. The TM data are scanned simultaneously by the TM sensor that is equipped with different spectral bands. Before the advent of this technology, land mapping information is done by using a plane that would cost. By using this technology mapping method can be done in a better way and save costs. Nature of technology requires data from satellite images to identify and use the landsurface through the spectral response.
In the central U.S., diﬀerent SAT anomalies can exist under similar atmospheric circulation conditions (Figure 1). To illustrate this, we select two Julys from the CESM-LE (July 1963 of member 15, and July 1925 of member 22) featuring similar midlatitude bands of high pressure at 500 hPa (Z500) with centers in the vicinity of the Aleutian islands and the west coast of the U.S. and a low-pressure center over western Canada, similar to the pattern described by McKinnon et al. (2016). SAT anomalies in the two cases diﬀer most notably in the central U.S., while they are broadly similar elsewhere. The configuration of SAT anomalies associated with the Z500 pattern indicate that atmospheric circulation anomalies are largely responsible for the warm (cool) anomalies in the western U.S. (Canada) (Figures 1c and 1d). The local warm anomaly over the central U.S. in case 2 (Figure 1f), which exceeds average central U.S. SAT by more than 5∘C, is not accounted for by dynam- ical adjustment and might hence be of thermodynamic origin. In contrast, there is no significant central U.S. SAT anomaly present in case 1 (Figure 1e). To investigate possible mechanisms explaining the diﬀerences in residual SAT anomaly over the central U.S., we compare area-averaged landsurface parameters in the region shown in Figures 1e and 1f (32.5–41.9∘N, 90–101.25∘W). We consider soil moisture, sensible and latent heat fluxes, the shortwave cloud radiative eﬀect, which is the diﬀerence between all-sky and clear-sky downward shortwave radiation at the surface (Cheruy et al., 2014), and the diurnal temperature range, which serves as proxy for local boundary layer moisture conditions (Dai et al., 1999; Lewis & Karoly, 2013). The landsurface anomalies are presented in terms of percent diﬀerence from their long-term averages.
3.1. Meteorological Station Data
Month-to-month temperature records were obtained from the meteorological stations of the Spanish National Meteorological Agency (AEMET)  and the Catalan Meteorological Service (SMC) . For long-term climatology, it is important to submit the data series to a rigorous quality control before mapping; this control has to be highly accurate to avoid unnecessary losses of spatial coverage while removing outliers that could negatively affect the resulting maps. In our case, spatio-temporal outlier detection was based on removing records outside three standard deviations from the mean values, combining the temporal perspective (one record was compared with all the records for the same station and for a specific month) with the spatial perspective (one record was compared with surrounding stations for a specific month and year). Finally, records that did not fulfill these two criteria were removed from the database.
When the recorded temperatures at meteorological stations are compared with the calculated temperatures, it becomes evident that use of MODIS sensor images and Sebal algorithm provide reasonable results for finding temperature in an area. Therefore the use of these images seems to be applicable for finding the needed information about the other places temperatures. In this research, because of a lack of coincidence in the studied images the resulted coordination as compared with actual coordination rates were low, resulting algorithms of low accuracy. Also, the errors resulting from the non-correspondence of the location of separative power in reflexive and thermal bands, geometry corrections, a lack of calibration of Sebal parameters in the studied regoin and no assurance of correction of the employed meteorological data also should be taken in to consideration. Employing a larger number and more update images with high location separative power, exact field measuring