The Adirondack Park in upstate New York is renowned for its strict land use laws aimed at preserving the pristine condition of its lakes. However, anthropogenic impacts continue to pose threats to lake health. This study investigates the health of selected Adirondack lakes by analyzing extensive field data collected in the late 1970s and 1980s, primarily focusing on lake acidification caused by coal plant emissions. Ongoing concerns regarding algal blooms, rising temperatures, and other threats necessitate a comprehensive understanding of climatological changes in the region. Remote sensing observations from multiple satellites, including the Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat series, are utilized to monitor lake health over long periods of time. This study explores the capabilities and usage of these satellites to assess land and lake surface parameters. By extracting satellite data, seasonal statistics are analyzed to uncover trends in lake temperature and seasonality. The findings highlight the importance of remote sensing in retrospectively measuring all seasons, identifying significant warming rates, and emphasizing the need for in situ measurements to validate satellite products and preserve the health of lakes within the Adirondack region.

The United States is continuing on the trajectory to be a racially and ethnically diverse society where non-Hispanic Whites are projected to become the largest group for the next four decades. Whilst the majority becomes the minority and vice versa, the same trend is not reflected in the Science, Technology, Engineering, and Mathematics (STEM) degree attainment or the STEM workforce. A recent report by the National Academies of Sciences, Engineering, and Medicine describes how minority-serving institutions are not widely targeted and highly utilized in the production of future STEM workers. The NSF reported that 22% of all science and engineering bachelor's degrees were awarded to underrepresented minorities. The distribution of bachelor's degrees consisted of Hispanics or Latinos receiving 13.5% of science and 10% of engineering degrees; Black or African Americans, 9% and 4%; and American Indians or Alaska Natives, 0.5% and 0.3%, respectively. From years 2004 to 2014, there has been a 11.6% decline of underrepresented minorities earning science and engineering bachelor's degree at high Hispanic enrollment institutions and a 26.5% decline at historically black colleges or universities. The NSF also reported that in 2015, African-American and Hispanic scientists and engineers working in science and engineering occupations were only five and six percent, respectively. Although studies have found modest gains in geoscience bachelor's degree recipients among women, the number of underrepresented minorities recipients is at 12% or less. Additionally, other studies reported that according to the Bureau of Labor Statistics, in 2017 underrepresented minorities comprised only a tenth of the geoscience workforce in the United States, and women comprised approximately a third of environmental scientists and geoscientists and a fifth of the four-year geoscience faculty. It is clear that for too long the geosciences (and STEM in general) has struggled to address issues of diversity, equity, and inclusion. At the Center for Remote Sensing and Earth System Sciences (ReSESS) at the New York City College of Technology, the catalyst of Service-Learning via Remote Sensing Research in underserved communities and with underrepresented minority students was used as a means of attracting and engaging students in remote sensing research. Results show that the DEI-service learning-remote sensing nexus increased awareness and, understanding in the geosciences, and it motivated underrepresented students to share their newfound knowledge with local citizens in environmental sustainability initiatives. Both students and citizens were enthralled by the engagement of remote sensing at the neighborhood scale.

Lake Surface Water Temperature (LSWT) is recognized as a critical climate change indicator. The changes in surface water temperature and the temperature of the surrounding land may have a climate change signature if there is consistency between changes in both temperatures. This proj ect focuses on the application of remote sensing to investigate the changes in lake surface water temperatures and their relationship with the surrounding land cover type to identify the main driving factors of these changes. In this study, 507 major global lakes have been investigated. An analysis of temperature variation over these lakes has been conducted using daily observations of Aqua MODIS from 2002 to 2018. Preliminary results show that 43.15% of the studied lakes are warming and about 51.00% of lakes are cooling. Furthermore, 62.53% of the lakes are shrinking, while 28.35% of them are expanding.

Land surface temperature (LST) is a crucial climate change indicator, and it is also used to evaluate the land-atmosphere energy budget. One of the advantages of remote sensing is its ability to monitor the variation of LST at high spatial and temporal resolutions. However, there are no available satellite LST products with both high spatial and temporal resolutions. This paper, therefore, proposes a method to obtain high spatial and temporal resolutions by combining Landsat 8's Thermal Infrared Sensor and the Geostationary Operational Environmental Satellite-R Series (GOES-R) LST data for urban regions. The obtained combined satellite product gives promising results as the model seems to perform well. The downscaled LST were validated with the Landsat 8 and in-situ LST observations. Differences between the predicted and the observed LSTs ranged from −0.09 to 3.30 K.

The United States is continuing on the trajectory to be a racially and ethnically diverse society where non-Hispanic Whites are projected to become the largest group for the next four decades. Whilst the majority becomes the minority and vice versa, the same trend is not reflected in the Science, Technology, Engineering, and Mathematics (STEM) degree attainment or the STEM workforce. A recent report by the National Academies of Sciences, Engineering, and Medicine describes how minority-serving institutions are not widely targeted and highly utilized in the production of future STEM workers. The NSF reported that 22% of all science and engineering bachelor's degrees were awarded to underrepresented minorities. The distribution of bachelor's degrees consisted of Hispanics or Latinos receiving 13.5% of science and 10% of engineering degrees; Black or African Americans, 9% and 4%; and American Indians or Alaska Natives, 0.5% and 0.3%, respectively. From years 2004 to 2014, there has been a 11.6% decline of underrepresented minorities earning science and engineering bachelor's degree at high Hispanic enrollment institutions and a 26.5% decline at historically black colleges or universities. The NSF also reported that in 2015, African-American and Hispanic scientists and engineers working in science and engineering occupations were only five and six percent, respectively. Although studies have found modest gains in geoscience bachelor's degree recipients among women, the number of underrepresented minorities recipients is at 12% or less. Additionally, other studies reported that according to the Bureau of Labor Statistics, in 2017 underrepresented minorities comprised only a tenth of the geoscience workforce in the United States, and women comprised approximately a third of environmental scientists and geoscientists and a fifth of the four-year geoscience faculty. It is clear that for too long the geosciences (and STEM in general) has struggled to address issues of diversity, equity, and inclusion. At the Center for Remote Sensing and Earth System Sciences (ReSESS) at the New York City College of Technology, the catalyst of Service-Learning via Remote Sensing Research in underserved communities and with underrepresented minority students was used as a means of attracting and engaging students in remote sensing research. Results show that the DEI-service learning-remote sensing nexus increased awareness and, understanding in the geosciences, and it motivated underrepresented students to share their newfound knowledge with local citizens in environmental sustainability initiatives. Both students and citizens were enthralled by the engagement of remote sensing at the neighborhood scale.

Studies of lake ice phenology have historically relied on limited in situ data. Satellite-derived temperature data provide an opportunity to better understand the climatological factors and trends behind ice phenology. Here we developed a model that uses daytime and nighttime surface temperature observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard Aqua to approximate ice in for 13 lakes and ice out for 58 lakes in Maine from 2002–2018. Ice-in and ice-out approximations were signaled by a moving average of the data crossing the 0°C threshold. The comparison of in situ and satellite-derived ice-out and ice-in dates were both highly correlated, but the amount of bias in those estimates was influenced by the number of days in a moving average of temperature. Systematic biases in the approximation model toward predicting ice in too early and ice out too late point to the importance of in-lake dynamics in modifying the timing of stabilization or melting of ice.

Land surface temperature (LST) and its diurnal variation are the critical factors in many aspects of climate study, surface energy balance, and environmental applications. Several satellite-based LST products are available for retrievals from regional to a global scale. However, due to the differences in sensor configurations and retrieval algorithms, these products may not necessarily be consistent. In this letter, the consistency of spatial and temporal skin temperature variations from two infrared satellite platforms has been evaluated over the contiguous United States (CONUS). Comparisons are made between the LST products from the newly launched Geostationary Operation Environmental Satellite R Series (GOES-R) advanced baseline imager (ABI) and the Moderate Resolution Imaging Spectroradiometer (MODIS) on both Aqua and Terra satellites which are polar orbiting. Overall, both products show a general agreement in their diurnal variations with differences mostly under 2 K. However, a temperature-dependent inconsistency has been detected. The MODIS LST product seems to estimate higher temperatures in the summer months while the GOES product estimates higher temperatures during the winter months. Moreover, the maximum observed diurnal differences could reach up to 10 K in mountainous regions. The results suggest that the corresponding temperature differences should be accounted for when LST diurnal variations are compared or generated from satellite observations.

In this study, the world first major 305 lakes have been investigated. An analysis of surface temperature variation over the global lakes have been conducted using observations from the Moderate Resolution Imaging Spectroradiometer (MODIS). MODIS Land Surface Temperature (LST) provides surface temperature data twice a day since 2002. The data products were first processed to obtain the average daily temperature over the lakes and their surrounding land areas from July 2002 to May 2018. A statistical approach was applied to calculate the temperature trends of the lake water, the surrounding land. Moreover, the relationship between the LST trends and potential driving factors such as the land cover changes in the lakes' basins, lakes areas, depth, and latitude were investigated. The primary results show that lakes water temperature are warming faster than the surrounding land temperature. Furthermore, 67.54% of lakes are shrinking while 24.92% are growing.

The purpose of this study is to estimate high temporal and high spatial resolution land surface temperature (LST) over different surface types in urban regions. The goal is to estimate high resolution LST by combining Landsat 8 and the Geostationary Operational Environmental Satellite-R Series (GOES-R) infrared-based LST. Landsat 8 provides higher spatial resolution (30 m) estimates of skin temperature every 16 days. However, GOES-R which has lower spatial resolution (2 km) has much higher temporal resolution (5 min). The research project aims to match the dates that both GOES-R and Landsat LSTs to find their spatial relationship to develop the downscaling of GOES-R LST. The downscaling approach will account for systematic biases between Landsat and GOES-R LST products.