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Background: Laterally spreading tumours (LSTs) in the colorectum are usually removed by endoscopic mucosal resection (EMR) even when large in size. LSTs with deeper submucosal (sm) invasion, however, should not be treated by EMR because of the higher risk of lymph node metastasis. Aims: To determine which endoscopic criteria, including high magnification pit pattern analysis, are associated with sm invasion in LSTs and clarify indications for EMR. Methods: Eight endoscopic criteria from 511 colorectal LSTs (granular type (LST-G type); non-granular type (LST-NG type)) were evaluated retrospectively for association with sm invasion, and compared with histopathological findings. Results: LST-NG type had a significantly higher frequency of sm invasion than LST-G type (14% v 7%; p<0.01). Presence of a large nodule in LST-G type was associated with higher sm invasion while pit pattern (invasive pattern), sclerous wall change, and larger tumour size were significantly associated with higher sm invasion in LST-NG type. In 19 LST-G type with sm invasion, sm penetration determined histopathologically occurred under the largest nodules (84%; 16/19) and depressed areas (16%; 3/19). Deepest sm penetration in 32 LST-NG type was either under depressed areas (72%; 23/32) or lymph follicular or multifocal sm invasion (28%; 1/32 and 8/32, respectively). Conclusions: When considering the most suitable therapeutic strategy for LST-G type, we recommend endoscopic piecemeal resection with the area including the large nodule resected first. In contrast, LST-NG type should be removed en bloc because of the higher potential for malignancy and greater difficulty in diagnosing sm depth and extent of invasion compared with LST-G type.

Background: Lateral spreading tumours are superficial spreading neoplasms now increasingly diagnosed using chromoscopic colonoscopy. The clinicopathological features and safety of endoscopic mucosal resection for lateral spreading tumours (G-type “aggregate” and F-type “flat”) has yet to be clarified in Western cohorts. Methods: Eighty two patients underwent magnification chromoscopic colonoscopy using the Olympus CF240Z by a single endoscopist. All patients had received a previous colonoscopy where an endoscopic diagnosis of lateral spreading tumour was made. All lesions were examined initially using indigo carmine chromoscopy to delineate contour followed by crystal violet for magnification crypt pattern analysis. A 20 MHz “mini probe” ultrasound was used if T2 disease was suspected. Following endoscopic mucosal resection, patients were followed up at 3, 6, 12, and 24 months using total colonoscopy. Results: Eighty two lateral spreading tumours were diagnosed in 80 patients (32% (26/82) F-type and 68% (56/82) G-type). G-type lesions were larger than F-type (G-type mean 42 (SD 14) mm v F-type 24 (6.4) mm; p<0.01). F-type lesions were more common in the right colon (F-type 77% (20/26) compared with G-type 39% (22/56); p<0.01) and more often associated with invasive disease (stage T2) (66% (10/15) v 33% (5/15); p<0.001). Fifty eight lesions underwent endoscopic mucosal resection (G-type 64% (37/58)/F-type 36% (21/58)). Local recurrent disease was detected in 17% of patients (10/58), all within six months of the index resection. Piecemeal resection and G-type morphology were significantly associated with recurrent disease (p<0.1). Overall “cure” rates for lateral spreading tumours using endoscopic mucosal resection at two years of follow-up was 96% (56/58). Conclusions: Endoscopic mucosal resection for lateral spreading tumours, staged as T1, is a safe and effective treatment despite their large size. Endoscopic mucosal resection may be an alternative to surgery in selected patients.

Land Surface Temperature (LST) is a substantial element indicating the relationship between the atmosphere and the land. This study aims to examine the efficiency of different LST algorithms, namely, Single Channel Algorithm (SCA), Mono Window Algorithm (MWA), and Radiative Transfer Equation (RTE), using both daytime and nighttime Landsat 8 data and in-situ measurements. Although many researchers conducted validation studies of daytime LST retrieved from Landsat 8 data, none of them considered nighttime LST retrieval and validation because of the lack of Land Surface Emissivity (LSE) data in the nighttime. Thus, in this paper, we propose using a daytime LSE image, whose acquisition is close to nighttime Thermal Infrared (TIR) data (the difference ranges from one day to four days), as an input in the algorithm for the nighttime LST retrieval. In addition to evaluating the three LST methods, we also investigated the effect of six Normalized Difference Vegetation Index (NDVI)-based LSE models in this study. Furthermore, sensitivity analyses were carried out for both in-situ measurements and LST methods for satellite data. Simultaneous ground-based LST measurements were collected from Atmospheric Radiation Measurement (ARM) and Surface Radiation Budget Network (SURFRAD) stations, located at different rural environments of the United States. Concerning the in-situ sensitivity results, the effect on LST of the uncertainty of the downwelling and upwelling radiance was almost identical in daytime and nighttime. Instead, the uncertainty effect of the broadband emissivity in the nighttime was half of the daytime. Concerning the satellite observations, the sensitivity of the LST methods to LSE proved that the variation of the LST error was smaller than daytime. The accuracy of the LST retrieval methods for daytime Landsat 8 data varied between 2.17 K Root Mean Square Error (RMSE) and 5.47 K RMSE considering all LST methods and LSE models. MWA with two different LSE models presented the best results for the daytime. Concerning the nighttime accuracy of the LST retrieval, the RMSE value ranged from 0.94 K to 3.34 K. SCA showed the best results, but MWA and RTE also provided very high accuracy. Compared to daytime, all LST retrieval methods applied to nighttime data provided highly accurate results with the different LSE models and a lower bias with respect to in-situ measurements.

Satellite‐based time series of land surface temperature (LST) have the potential to be an important tool to diagnose climate changes of the past several decades. Production of such a time series requires addressing several issues with using asynchronous satellite observations, including the diurnal cycle, clouds, and angular anisotropy. Here we evaluate the angular anisotropy of LST using one full year of simultaneous observations by two Geostationary Operational Environment Satellites, GOES‐EAST and GOES‐WEST, at the locations of five surface radiation (SURFRAD) stations. We develop a technique to convert directionally observed LST into direction‐independent equivalent physical temperature of the land surface. The anisotropy model consists of an isotropic kernel, an emissivity kernel (LST dependence on viewing angle), and a solar kernel (effect of directional inhomogeneity of observed temperature). Application of this model reduces differences of LST observed from two satellites and between the satellites and surface ground truth ‐ SURFRAD station observed LST. The techniques of angular adjustment and temporal interpolation of satellite observed LST open a path for blending together historical, current, and future observations of many geostationary and polar orbiters into a homogeneous multi‐decadal data set for climate change research. Key Points Introduced and assessed statistical model of directional anisotropy of LST Proposed technique for angular adjustment of satellite observed LST Proposed technique for temporal adjustment of satellite observed LST

This study examines the Land Surface Temperature (LST) trends in eight key Moroccan cities from 1990 to 2020, emphasizing the influential factors and disparities between coastal and inland areas. Geographically weighted regression (GWR), machine learning (ML) algorithms, namely XGBoost and LightGBM, and SHapley Additive exPlanations (SHAP) methods are utilized. The study observes that urban areas are often cooler due to the presence of urban heat sinks (UHSs), more noticeably in coastal cities. However, LST is seen to increase across all cities due to urbanization and the degradation of vegetation cover. The increase in LST is more pronounced in inland cities surrounded by barren landscapes. Interestingly, XGBoost frequently outperforms LightGBM in the analyses. ML models and SHAP demonstrate efficacy in deciphering urban heat dynamics despite data quality and model tuning challenges. The study’s results highlight the crucial role of ongoing urbanization, topography, and the existence of water bodies and vegetation in driving LST dynamics. These findings underscore the importance of sustainable urban planning and vegetation cover in mitigating urban heat, thus having significant policy implications. Despite its contributions, this study acknowledges certain limitations, primarily the use of data from only four discrete years, thereby overlooking inter-annual, seasonal, and diurnal variations in LST dynamics.

Climate change, a critical global environmental crisis, profoundly impacts ecosystems, particularly in regions with delicate environmental balances. This study focuses on the Jhelum basin in the north-western Himalayas, examining the extensive effects of climate change on glaciers, snow cover, land use and land cover (LULC), land surface temperature (LST), water resources, and natural hazards. Rising temperatures have accelerated glacier melting and altered precipitation patterns, with significant implications for local water supplies and agriculture. The study analyses climate data from the Indian Meteorological Department (1990 to 2020), revealing increasing trends in both maximum and minimum temperatures, alongside variable precipitation trends across different locations. The retreat of glaciers and the expansion of glacial lakes have been observed, with lower-elevation glaciers showing the most significant reduction. LULC changes indicate a shift from agricultural land to settlements and horticulture, while LST has risen, particularly in urbanized areas, reflecting the impact of urbanization and climate change. Furthermore, the increased frequency of extreme weather events, such as floods and landslides, exacerbates the region’s vulnerability, threatening infrastructure, biodiversity, and local communities. The findings highlight the necessity of comprehensive, integrated approaches to address climate change and ensure the resilience of the Jhelum basin. This research contributes valuable insights into the region’s changing environmental dynamics, essential for informed decision-making and effective adaptation strategies in response to the ongoing climate crisis.

LST has been fluctuating more quickly, resulting in the degradation of the climate and human life on a local–global scale. The main aim of this study is to examine SUHI formation and hotspot identification over Prayagraj city of India using seasonal Landsat imageries of 1987–2018. The interrelationship between six land indices (NDBI, EBBI, NDMI, NDVI, NDWI, and SAVI) and LST (using a mono-window algorithm) was investigated by analyzing correlation coefficients and directional profiling. NDVI dynamics showed that the forested area observed lower LST by 2.25–4.8 °C than the rest of the city landscape. NDBI dynamics showed that the built-up area kept higher LST by 1.8–3.9 °C than the rest of the city landscape (except sand/bare soils). SUHI was intensified in the city center to rural/suburban sites by 0.398–4.016 °C in summer and 0.45–2.24 °C in winter. Getis–Ord Gi* statistics indicated a remarkable loss of areal coverage of very cold, cold, and cool classes in summer and winter. MODIS night-time LST data showed strong SUHI formation at night in summer and winter. This study is expected to assist in unfolding the composition of the landscape for mitigating thermal anomalies and restoring environmental viability.

Land use and land cover (LULC) changes resulting from rapid urbanization are the foremost causes of increases in land surface temperature (LST) in urban areas. Exploring the impact of LULC changes on the spatiotemporal patterns of LST under future climate change scenarios is critical for sustainable urban development. This study aimed to project the LST of Nanjing for 2025 and 2030 under different climate change scenarios using simulated LULC and land coverage indicators. Thermal infrared data from Landsat images were used to derive spatiotemporal patterns of LST in Nanjing from 1990 to 2020. The patch-generating land use simulation (PLUS) model was applied to simulate the LULC of Nanjing for 2025 and 2030 using historical LULC data and spatial driving factors. We simulated the corresponding land coverage indicators using simulated LULC data. We then generated LSTs for 2025 and 2030 under different climate change scenarios by applying regression relationships between LST and land coverage indicators. The results show that the LST of Nanjing has been increasing since 1990, with the mean LST increased from 23.44 °C in 1990 to 25.40 °C in 2020, and the mean LST estimated to reach 26.73 °C in 2030 (SSP585 scenario, integrated scenario of SSP5 and RCP5.8). There were significant differences in the LST under different climate scenarios, with increases in LST gradually decreasing under the SSP126 scenario (integrated scenario of SSP1 and RCP2.6). LST growth was similar to the historical trend under the SSP245 scenario (integrated scenario of SSP2 and RCP4.5), and an extreme increase in LST was observed under the SSP585 scenario. Our results suggest that the increase in impervious surface area is the main reason for the LST increase and urban heat island (UHI) effect. Overall, we proposed a method to project future LST considering land use change effects and provide reasonable LST scenarios for Nanjing, which may be useful for mitigating the UHI effect.

Land surface temperature (LST) plays a major role in the study of surface energy balances. Remote sensing techniques provide ways to monitor LST at large scales. However, due to atmospheric influences, significant missing data exist in LST products retrieved from satellite thermal infrared (TIR) remotely sensed data. Although passive microwaves (PMWs) are able to overcome these atmospheric influences while estimating LST, the data are constrained by low spatial resolution. In this study, to obtain complete and high-quality LST data, the Bayesian Maximum Entropy (BME) method was introduced to merge 0.01° and 0.25° LSTs inversed from MODIS and AMSR-E data, respectively. The result showed that the missing LSTs in cloudy pixels were filled completely, and the availability of merged LSTs reaches 100%. Because the depths of LST and soil temperature measurements are different, before validating the merged LST, the station measurements were calibrated with an empirical equation between MODIS LST and 0~5 cm soil temperatures. The results showed that the accuracy of merged LSTs increased with the increasing quantity of utilized data, and as the availability of utilized data increased from 25.2% to 91.4%, the RMSEs of the merged data decreased from 4.53 °C to 2.31 °C. In addition, compared with the filling gap method in which MODIS LST gaps were filled with AMSR-E LST directly, the merged LSTs from the BME method showed better spatial continuity. The different penetration depths of TIR and PMWs may influence fusion performance and still require further studies.

An urban heat island (UHI) is a significant anthropogenic modification of urban land surfaces, and its geospatial pattern can increase the intensity of the heatwave effects. The complex mechanisms and interactivity of the land surface temperature in urban areas are still being examined. The urban–rural gradient analysis serves as a unique natural opportunity to identify and mitigate ecological worsening. Using Landsat Thematic Mapper (TM), Operational Land Imager/Thermal Infrared Sensor (OLI/TIRS) and Moderate Resolution Imaging Spectroradiometer (MODIS), Land Surface Temperature (LST) data in 2000, 2010, and 2019, we examined the spatial difference in daytime and nighttime LST trends along the urban–rural gradient in Greater Cairo, Egypt. Google Earth Engine (GEE) and machine learning techniques were employed to conduct the spatio-temporal analysis. The analysis results revealed that impervious surfaces (ISs) increased significantly from 564.14 km2 in 2000 to 869.35 km2 in 2019 in Greater Cairo. The size, aggregation, and complexity of patches of ISs, green space (GS), and bare land (BL) showed a strong correlation with the mean LST. The average urban–rural difference in mean LST was −3.59 °C in the daytime and 2.33 °C in the nighttime. In the daytime, Greater Cairo displayed the cool island effect, but in the nighttime, it showed the urban heat island effect. We estimated that dynamic human activities based on the urban structure are causing the spatial difference in the LST distribution between the day and night. The urban–rural gradient analysis indicated that this phenomenon became stronger from 2000 to 2019. Considering the drastic changes in the spatial patterns and the density of IS, GS, and BL, urban planners are urged to take immediate steps to mitigate increasing surface UHI; otherwise, urban dwellers might suffer from the severe effects of heatwaves.