An Assessment of the Accuracy of MODIS Land Surface Temperature over Egypt Using Ground-Based Measurements

Space-based data have provided important advances in understanding climate systems and processes in arid and semi-arid regions, which are hot-spot regions in terms of climate change and variability. This study assessed the performance of land surface temperatures (LSTs), retrieved from the Moderate-Resolution Imaging Spectroradiometer (MODIS) Aqua platform, over Egypt. Eight-day composites of daytime and nighttime LST data were aggregated and validated against near-surface seasonal and annual observational maximum and minimum air temperatures using data from 34 meteorological stations spanning the period from July 2002 to June 2015. A variety of accuracy metrics were employed to evaluate the performance of LST, including the bias, normalized root-mean-square error (nRMSE), Yule–Kendall (YK) skewness measure, and Spearman’s rho coefficient. The ability of LST to reproduce the seasonal cycle, anomalies, temporal variability, and the distribution of warm and cold tails of observational temperatures was also evaluated. Overall, the results indicate better performance of the nighttime LSTs compared to the daytime LSTs. Specifically, while nighttime LST tended to underestimate the minimum air temperature during winter, spring, and autumn on the order of −1.3, −1.2, and −1.4 °C, respectively, daytime LST markedly overestimated the maximum air temperature in all seasons, with values mostly above 5 °C. Importantly, the results indicate that the performance of LST over Egypt varies considerably as a function of season, lithology, and land use. LST performs better during transitional seasons (i.e., spring and autumn) compared to solstices (i.e., winter and summer). The varying interactions and feedbacks between the land surface and the atmosphere, especially the differences between sensible and latent heat fluxes, contribute largely to these seasonal variations. Spatially, LST performs better in areas with sandstone formations and quaternary sediments and, conversely, shows lower accuracy in regions with limestone, igneous, and metamorphic rocks. This behavior can be expected in hybrid arid and semi-arid regions like Egypt, where bare rocks contribute to the majority of the Egyptian territory, with a lack of vegetation cover. The low surface albedo of igneous and limestone rocks may explain the remarkable overestimation of daytime temperature in these regions, compared to the bright formations of higher surface albedo (i.e., sandy deserts and quaternary rocks). Overall, recalling the limited coverage of meteorological stations in Egypt, this study demonstrates that LST obtained from the MODIS product can be trustworthily employed as a surrogate for or a supplementary source to near-surface measurements, particularly for minimum air temperature. On the other hand, some bias correction techniques should be applied to daytime LSTs. In general, the fine space-based climatic information provided by MODIS LST can be used for a detailed spatial assessment of climate variability in Egypt, with important applications in several disciplines such as water resource management, hydrological modeling, agricultural management and planning, urban climate, biodiversity, and energy consumption, amongst others. Also, this study can contribute to a better understanding of the applications of remote sensing technology in assessing climatic feedbacks and interactions in arid and semi-arid regions, opening new avenues for developing innovative algorithms and applications specifically addressing issues related to these regions

Surface area change detection of the Burullus Lagoon, North of the Nile Delta, Egypt, using water indices: A remote sensing approach

The Burullus Lagoon is one of the most severely impacted water bodies in the Nile Delta. A set of six satellite images acquired between 1973 and 2011 was employed to map the change of the surface area of the Burullus Lagoon in the Nile Delta using the water indices approach. In this paper we applied the non-traditional normalized difference water index (NDWI) and the modified normalized difference water index (MNDWI) to quantify the change in the water body area of the lagoon during the study period. Results showed that the lagoon lost 42.8% of its open water area due to the severe anthropogenic activities, such as the reclaiming of its southern margins for agricultural purposes and the filling caused by the discharge of agricultural wastes. Proper management should be adopted to sustain the vitality of the Burullus wetland ecosystem

Retrieval of monthly maximum and minimum air temperature using MODIS aqua land surface temperature data over the United Arab Emirates

Spatially distributed air temperature (Ta) data are essential for environmental studies. Ta data are collected from meteorological stations of sparse distribution. This problem can be overcome by using remotely sensed datasets at different scales. This study used land-based temperature measurements and satellite data for estimating Ta distribution over the United Arab Emirates. Land-based Ta data from 11 weather stations during 2003 to 2019 were used with MODIS Aqua LST for both daytime (LSTd) and nighttime (LSTn) data. The results indicate a significant correlation between LST and Ta with regression coefficients R2 > 0.94/0.96 and Root Mean Square Error about 1.75/0.97 °C of LSTd/Tmax and LSTn/Tmin, respectively. Large variability was observed between the daytime and nighttime mean temperature distribution indicating the importance of MODIS LST as a proxy for Ta. These countrywide Ta grids provide vital tools for the planning of environmental and economic developments in the era of global climate change

Analysis of the dynamics of land use change and its prediction based on the integration of remotely sensed data and CA-Markov model, in the upstream Citarum Watershed, West Java, Indonesia

In this research, the integration of remotely sensed data and Cellular Automata-Markov model (CA-Markov) have been used to analyze the dynamics of land use change and its prediction for the next year. Training phase for the CA-Markov model has been created based on the input pair of land use, which is the result of land use mapping using Maximum Likelihood (ML) algorithm. Three-map comparison has been used to evaluate process accuracy assessment of the training phase for the CA-Markov model. Furthermore, the simulation phase for the CA-Markov model can be used to predict land use map for the next year. The analyze of the dynamics of land use change and its prediction during the period 1990 to 2050 can be obtained that the land serves as a water absorbent surfaces such as primary forest, secondary forest and the mixed garden area continued to decline. Meanwhile, on build land area that can lead to reduced surface water absorbing tends to increase from year to year. The results of this research can be used as input for the next research, which aims to determine the impact of land use changes in hydrological conditions against flooding in the research area