Validating remotely sensed sea surface temperature (SST) is a fundamental step in establishing reliable biological/physical models that can be used in different marine applications. Mapping SST using accurate models would assess in understanding critical mechanisms of marine and coastal zones, such as water circulations and biotic activities. This study set out to validate MODIS SSTs with a spatial resolution of 1-km in the Arabian Gulf (24–30° N, 48–57° E) and to assess how well direct comparison of dual matchups and triple collocation analyses perform. For the matchup process, three data sets, MODIS-Aqua, MODIS-Terra, and iQuam, were co-located and extracted for 1-pixel box centered at each actual in situ measurement location with a time difference window restricted to a maximum of ±3 h of the satellite overpass. Over the period July 2002 to May 2020, the MODIS SSTs (N = 3786 triplets) exhibited a slight cool night-time bias compared to iQuam SSTs, with a mean ± SD of −0.36 ± 0.77 °C for Aqua and −0.27 ± 0.83 °C for Terra. Daytime MODIS SST observations (N = 5186 triplets) had a lower negative bias for both Aqua (Bias = −0.052 °C, SD = 0.93 °C) and Terra (Bias = −0.24 °C, SD = 0.90 °C). Using extended triple collocation analysis, the statistical validation of system- and model-based products against in situ-based product indicated the highest ETC-based determination coefficients (ρt,X2 ≥ 0.98) with the lowest error variances (σε2 ≤ 0.32), whereas direct comparison underestimated the determination coefficients and overestimated the error estimates for all MODIS algorithms. The ETC-based error variances for MODIS Aqua/Terra NLSSTs were 0.25/0.19 and 0.26/0.32 in daytime and night-time, respectively. In addition, MODIS-Aqua was relatively more sensitive to the SST signal than MODIS-Terra at night and vice versa as seen in the unbiased signal-to-noise ratios for all observation types.

The early condition-based assessment of civil infrastructures plays an essential role in extending their service life, preventing undesirable sudden failures, and reducing maintenance and rehabilitation costs. One of the most commonly used and fastest nondestructive testing (NDT) techniques is infrared thermography (IRT), which has emerged as a powerful method for assessing general concrete quality and detecting subsurface damage in structural members. Nevertheless, the accurate detection and classification of localized defects is still a challenging task to achieve. The contribution made by enhancing defect detection using two-dimensional (2D) wavelet transformation (WT) as a post-processing method, however, has received little attention within the field of active IR thermography. In this study, we explored the use of continuous wavelet transform (CWT) to visualize how the wavelet function at different frequencies could enhance the damage features of thermal images. A concrete slab under an applied heat flux was tested experimentally by an IR camera with well-controlled excitation sources. The qualitative visualization of thermograms was translated into quantitative results by extracting, processing, and post-processing the values assigned to the pixels in the thermal images. With the assumption of there being no oriented damage features, an isotropic (non-directional) Mexican hat wavelet was utilized as the mother wavelet. The experimental results showed that the 2D-CWT method achieved strong detection performance in extracting discriminatory features (defective areas) from the acquired thermal images. Compared with raw thermograms, the resultant CWT-transformed images were less affected by the non-uniform heating effect, and the boundaries of the defects contrasted more strongly. The 2D-CWT method demonstrates good sensitivity when an appropriate wavelet type and scale factor are chosen. Due to the desire to detect localized defects, adjusting the scale factor of the wavelet is important to improve the efficiency of detection as lower scale factors provide the finer details of thermal images, whereas higher scale factors provide the general outline of internal defects. The findings of this study represent a further step toward improving thermographic data for more precise defect-detection imaging, and principally for large concrete structures, that can be verified easily using other NDT surveys.

No abstract

There is a recognized need to analyze the temporal changes of sea surface temperature in various water bodies, especially the semi-enclosed ones, because of the direct link between sea temperature and aquatic biodiversity. There has been substantial research undertaken on the role of time series analysis as a powerful technique for studying the characteristics of long-term SST changes at regular time intervals. The present paper aimed to study the monthly-averaged MODIS SST data (2001–2019) over Kuwait Bay, i.e., the northwestern corner of the Arabian Gulf. Because different approaches can yield different results, the analysis of the SST time series was conducted using time and frequency domains. The preliminary analysis of the time series reported a significant SST peak in August 2010 that reached nearly 34.2 °C (SD = 0.17 °C) due to the moderate intensity El Niño event in 2010. However, in the preceding year, we observed a cool SST anomaly in the range of –0.5 °C to –2.4 °C. From the SMK trend test, we found that monthly climatological SST in September exhibited a significant upward trend (𝑆9 = 103, 𝜏 = 0.6, 𝑃 = 0.0004). Pettitt’s changepoint test indicated a significant change in the central tendency of SST data after April 2012. The annual periodicity of the SST in Kuwait Bay was constant over the 19 years. Furthermore, a very weak periodicity of 6-month has been barely noticed. Our present results provide large-scale guidance that affirms the importance of highlighting the severe SST fluctuations in Kuwait’s water in order to understand and improve its marine environmental status.

No abstract