Statistical Deformation Control for SPQC Monitoring of Sheet Metal Forming

There is increasing demand for air surface temperature (Ta) data that can capture information for a large area or for a region, since this kind of data is an important parameter for a wide range of applications. However, due to the sparse distribution of meteorological stations, especially in developing countries and remote regions (e.g. mountainous areas, the Arctic, or tropical rainforests), the spatial coverage information of Ta is often limited. Fortunately, remote sensing satellites can provide land surface temperature (LST) data, which is considered one of the most important and useful data sources for Ta retrieval over a region or large area. Among various remote sensors that can provide LST data (e.g. the Advanced Very High Resolution Radiometer (AVHRR) sensor onboard NOAA satellites, the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor onboard Terra satellites, Landsat (TM, ETM, and TIRS sensors) of Landsat satellites), the most popular and most often used is the LST from the MODerate Resolution Imaging Spectroradiometer (MODIS). AVHRR, ASTER, and Landsat each have their own limitations, since AVHRR lacks metadata files, ASTER is only available upon request and payment, and Landsat has a coarse temporal resolution of 16 days. Meanwhile two MODIS instruments, the first launched on 18 December 1999 and the second on 4 May 2002 aboard the Terra and Aqua platforms, respectively, are uniquely designed to provide free LST data with a moderate spatial resolution of about one kilometer and a very high temporal resolution (i.e. up to four global observations per day including daytime and nighttime data). Over the last two decades, MODIS LST data has successfully been used for Ta estimation in many regions, such as Europe, the United States, Canada, Africa, and the Tibetan Plateau. However in Vietnam, a developing country with very sparse meteorological stations, MODIS LST has rarely been applied to retrieve Ta. 
This research presents a comprehensive evaluation of the application of MODIS LST data for Ta estimation in northwestern Vietnam. From the increasing number of studies in the literature, several methods have been proposed, applied, and evaluated to retrieve Ta from MODIS LST data. However, to the best of our knowledge, there are no studies that present an overview of the application of MODIS LST data. We therefore conducted the first review of all methods that have been developed and applied over nearly the last two decades, as well as discussed the advantages and disadvantages of these methods. 
It is known that LST changes rapidly in both space and time, and that different regions can exhibit specific variances, since each region has a unique terrain. A number of studies have reported that land cover type and elevation are the most important variables that affect the relationship between LST and Ta, as well as the accuracy of Ta estimation using LST data. Therefore, we conducted a study to evaluate and investigate the variation in LST due to changes in elevation to create an overview about the LST data in northwest Vietnam. The results showed that the quantity of temperature change varied with increasing elevation from January to December in both the daytime and the nighttime. The LST increased from 3.8 °C to 6.1 °C and from 1.5 °C to 5.8 °C with a 1,000 m decrease in elevation at daytime and nighttime, respectively. In addition, land use/cover types also affected the variability of LST with changes in elevation. Therefore, in studies using MODIS LST data for Ta estimation, elevation, Julian day, and land cover types should be taken into consideration. 
There are four types of MODIS LST data available each day, however, only a handful of studies have compared the performance of each individual MODIS LST between the two different sky conditions (i.e. all clear sky condition and only good LST data conditions) as well as the different combination of the four MODIS LST data for Ta estimation with the same estimation methods, in the same study areas. Therefore, we implemented the next study, which evaluated and tested each individual LST data as well as all possible combinations of the four MODIS LST data from two distinct land surface characteristics and two sky conditions in northwestern Vietnam for 10 years (from 2004 to 2013), for daily Ta estimation. The results showed that Terra LST has higher correlation with Ta than Aqua LST (in both sky conditions), meaning that having a closer overpass time with Ta occurrence time does not guarantee a higher correlation. Using only good LST data produced higher accuracy of Ta estimation, however, if the percentage of good data is low (i.e. less than 30%), using all clear sky data will produce higher Ta-max estimations. In addition, it should be noted that the trade-off between good LST data and the spatial coverage of LST data should be taken into account when selecting LST data for Ta estimation. In the next study, we used all four MODIS LST data and ten auxiliary variables to estimate Ta-max and Ta-min in northwestern Vietnam. We evaluated the performance of MODIS LST both exclusively and with auxiliary variables. The results showed that not all variables improved the accuracy of Ta estimation. Besides the four MODIS LST, elevation and longitude were considered the most important variables for Ta-max estimation. However, for Ta-min estimation, the relative performance of the simplest model (using one variable) and the most complicated model (using ten variables) was similar. At best, Ta-min/Ta-max estimation achieved results of R2 = 0.88/0.93 and RMSE = 2.08/1.43 oC. It is clearly seen that with the most popular methods (linear regression model/statistical approaches) we can estimate Ta-max with very high accuracy by introducing auxiliary variables into the models, however, the accuracy of Ta-min estimation cannot be improved using this approach. Moreover, in recent years, the application of machine learning methods to Ta estimation using MODIS LST has received great attention from scientists because they can handle the complicated relationship between LST and Ta under different conditions. Therefore, we conducted a study to compare the performance of LM, RF, and CB for Ta (Ta-max, Ta-min, and Ta-mean estimation) in northwestern Vietnam for five years (from 2009 to 2013). The results suggested that when all four MODIS LST were used with or without auxiliary variables, the performance of LM, CB, and RF were similar. This study confirmed that the very high accuracy of Ta estimation (R2 > 0.93/0.80/0.89 and RMSE ~1.5/2.0/1.6 °C of Ta-max, Ta-min, and Ta-mean, respectively) could be achieved with a simple combination of the four LST, elevation, and Julian day data using a suitable algorithm. Obviously, the results of Ta-min were not as good as Ta-max estimation with any of the utilized approaches. For further research, other seldomly used variables such as nighttime light data, percentage of urban land cover, or distance to coasts should be considered and evaluated in order to improve the results of Ta estimation using MODIS LST.

Air Surface Temperature Estimation Using MODIS Land Surface Temperature Data in Northwest Vietnam

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