Inner City communities have traditionally been overlooked and often forgotten in the satellite and ground-based remote sensing revolution. For too long, undergraduate minority students from these communities have been marginalized, ostracized, and unprepared to participate in the plethora of remote sensing applications that yet remain new, foreign, and mysterious. Even basic understanding of the amazing tool of remote sensing is unknown to them. These students lack the exposure, the awareness, the understanding, and the engagement in the foray of remote sensing and the unique lens it provides to probe and unearth new ways of gaining knowledge about the environment. To adequately prepare inner-city students to become members of the next generation of geoscientists, to empower and sustain underserved neighborhoods, and to assist in replenishing the geoscience workforce, unique paradigms of teaching and learning about remote sensing are needed.At the Center for Remote Sensing and Earth System Sciences (ReSESS) at the New York City College of Technology (City Tech), remote sensing is being used as a catalyst to attract and to engage students from underserved communities in studying urban climate within their local neighborhoods. Results indicate that this approach of using remote sensing to attract students to urban climate increased awareness and understanding in the geosciences, and it motivated students to share their newfound knowledge in environmental sustainability initiatives with their fellow local citizens.

The difference between air temperature and land surface temperature (LST) across New York City from 2003 to 2022 was analyzed using Automated Surface Observing Systems, NASA’s Moderate Resolution Imaging Spectroradiometer, and ground-based measurements of LST and air temperature. Using one-to-one plots that compared LST with air temperature at eight stations around the New York City region, LST differed the most from air temperature in the summer compared to the winter (slopes on the order of 0.5 instead of 0.9) and in urban locations compared to sub-urban/rural locations (slopes on the order of 0.8 instead of 1.0). Results from field campaigns yield more or less uniform air temperatures, which led to a relaxed LST vs. air temperature slope. More broadley, LST being greater than air temperature likely arises from anthropogenic energy in urban environments, which highlights the importance in socioeconomic decisions relating to land cover type.

For too many undergraduate minority students, remote sensing and its plethora of applications to geophysics yet remain new, foreign, and mysterious. These students, even with the requisite STEM backgrounds, lack the exposure, the awareness, the understanding, and the engagement in the foray of remote sensing and the unique lens it provides to probe and unearth new ways of gaining knowledge about the environment. To strengthen undergraduate education, to adequately prepare candidates to become the next generation of geoscientists, to empower and sustain underserved neighborhoods, and to assist in replenishing the geoscience workforce, unique paradigms of teaching and learning about remote sensing are needed. At the Center for Remote Sensing and Earth System Sciences (ReSESS) at the New York City College of Technology (City Tech), remote sensing is used as a catalyst to attract and to engage students from underserved communities in studying urban climate within their local neighborhoods. Results indicate that this approach of using remote sensing to attract students to urban climate increased awareness and understanding in the geosciences, and it motivated students to share their newfound knowledge in environmental sustainability initiatives with their fellow local citizens.

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.

Urban Heat Islands (UHIs) are more likely to occur in historically redlined communities that have been systemically denied necessary infrastructure to reduce heat. In the UHI neighborhood of Bedford-Stuyvesant in Brooklyn, the community has responded to a lack of cooling infrastructure by opening fire hydrants and building community gardens, which are both Blue/Green Infrastructure (BGI) known to mitigate UHIs by evapotranspiration. In seeking to quantify the cooling impact of hydrants and community gardens, the satellite data from Landsat 8 was evaluated for its ability to capture the cooling impact of these small-scale BGI’s at a 30 meter resolution. Quantifying these cooling effects empowers community-based efforts of heat mitigation in spaces where the existing municipal urban infrastructure is inadequate to protect neighborhoods, and it validates what the community members of Bed-Stuy already know: that community gardens and open fire hydrants are inherently valuable as climate change exacerbates the UHI effect.

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.

Passive microwave radiometers provide brightness temperature (TB) measurements in a large spectral range with relatively high temporal resolution. Because of their significant role in environmental studies, it is imperative to have a higher temporal resolution and more consistent datasets. This research aims to build a TB diurnal cycle at the Global scale. The shape (amplitude and phase) of the diurnal cycle for each month is obtained by merging several days of Global Precipitation Measurement (GPM) Microwave Imager (GMI) TB measurements because the acquisition times of GMI vary from day to day. This preliminary shape will later be improved by using the other sensors with daily fixed acquisition times such as three Special Sensor Microwave Imager/Sounder (SSMIS) and Advanced Microwave Scanning Radiometer 2 (AMSR2). The final product is a highly frequent diurnal cycle that can help prediction of the time of the freeze thaw transition states.

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.

For too many undergraduate minority students, remote sensing and its plethora of applications to geophysics yet remain new, foreign, and mysterious. These students, even with the requisite STEM backgrounds, lack the exposure, the awareness, the understanding, and the engagement into the foray of remote sensing and the unique lens it provides to probe and unearth new ways of gaining knowledge about the environment. To strengthen undergraduate education, to adequately prepare candidates to become the next generation of geoscientists, and to assist in replenishing the geoscience workforce, unique paradigms of teaching and learning about remote sensing are needed. At the Center for Remote Sensing and Earth System Sciences (ReSESS) at the New York City College of Technology, the catalyst of Service-Learning within a local community was used as a means of attracting and engaging students in remote sensing research. Results show that the service learning-remote sensing nexus increased awareness and, understanding in the geosciences, and it motivated 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.

Land surface temperature data from Landsat 8 and from MODIS on the Aqua and Terra satellites were compared with ground-based surface temperature data from five urban sites within a New York City neighborhood. The ground data validated the spatial heterogeneity of surface temperatures from Landsat 8; however, all three satellite sensors significantly underestimated the measured surface temperatures. The sensors reasonably matched the temperature of shaded areas, but they undervalued the overall temperature. These results suggest that, while Landsat 8 and other satellites are helpful tools for mapping urban temperature variations, the absolute temperature readings need validation, for they may not capture the high temperatures of sun-exposed urban areas.

Passive microwave brightness temperature (TB) radiometers are widely used to retrieve several atmospheric and surface parameters such as precipitation, soil moisture, freeze and thaw, water vapor, air temperature profile, and land surface emissivity. Since TBs are measured at different microwave frequencies with various instruments, incident angles, footprints, spatial resolutions, and radiometric characteristics, a combination of data from different microwave sensors could be inconsistent. For this reason, this study primarily uses the nonsynchronous Global Precipitation Measurement (GPM) Microwave Imager (GMI) measurements to construct the diurnal cycle of TBs for each month. This diurnal pattern could be used as a point of reference to validate and calibrate the diurnal cycle of TBs from fusing the measurements of other sensors with daily fixed acquisition times (SSM/I, SSMIS, AMSR2, and etc.). The data from these sensors should also be merged and harmonized in order to build a comprehensive global diurnal cycle of passive microwave TBs. This highly frequent diurnal cycle will eventually help predict the emissivity estimation and potentially further advance the accurate prediction of the estimated time of the freeze/thaw (FT) transition states. Global comparison of TBs obtained from different sensors shows a moderate variance with a significant dependence on land cover type. The results of this study can enhance the temporal detection of freeze and thaw which is more helpful during the transition times when multiple FT changes may occur within a day.

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.

At all academic levels, recruiting, retaining, and graduating minority students in sufficient numbers within the geosciences remain a monumental and failed task of the geoscience community. This continued dismal failure culminates in the persistence of an ethnic (and far too often gender) homogeneity among the community's participants. The paucity of diversity in the geosciences does not bode well for the sustainability, the vitality, and the global reach of the field. Inclusiveness and accessibility in the geosciences are critical, and innovative approaches/methodologies are urgently needed to ameliorate this vexing problem. Results from a pioneering program in New York City show that a rigorous and exciting introduction to satellite and ground-based remote sensing can be successfully used to attract, to raise geoscience awareness, and to engage non-geoscience, underrepresented minority students majoring in Science, Technology, Engineering, and Mathematics (STEM) in productive geoscience participation: research, peer-reviewed publications, conference presentations, and workforce and graduate school entrances. The results also show that program participants: a) developed a deeper interest in the geosciences; b) became more aware of remote sensing as a research tool, and c) connected remote sensing to a host of real world, geophysical phenomena and applications.

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.

The states of the earth surface in terms of freeze and thaw (FT) cycles especially in high-latitude regions have a crucial role in many applications such as biogeochemical transitions, hydrology and ecosystem studies. This study uses Synthetic Aperture Radar (SAR) c-band backscatter data from Sentinel 1 from April 2014 to December 2017 to detect high-resolution freeze/thaw states in Alaska. The contrasts between frozen and thawed seasons are used to define FT states. Comprehensive in situ observations of soil temperature, air temperature, snow depth, and soil moisture were obtained to develop appropriate sigma (dB) thresholds of backscattering between freeze and thaw states. The developed thresholds were used to detect FT in Alaska, USA. The results of this method revealed that the estimates are reasonably able to detect the states of surface when compared with ground measurements from SNOw TELemetry (SNOTEL) observations. The developed method that mainly relies on using ground measurements from different land cover type shows an improvement with respect to previous methods that had used the average of frozen and thawed backscattering scenarios as FT references.

For one year, underrepresented minority students majoring in Science, Technology, Engineering, and Mathematics (STEM) were engaged in a rigorous geoscience program that trained and equipped them with basic geoscience knowledge and geoscience workforce skills that adequately prepared them for entry level geoscience career building positions. Preliminary results from this young program with its small sample size (with a sample size of 10, caution should be exercised in generalizing these results) are extremely encouraging; They show that the program's geoscience remote sensing research internship produced significant student gains in a variety of remote sensing skills that range from interpreting, manipulating, and analyzing remote sensing data to conducting and presenting remote sensing research. Some of these students have also now joined the geoscience workforce as interns.

The states of the Earth surface in terms of high-latitude freeze and thaw (FT) cycles significantly impact many physical applications that include biogeochemical transitions, hydrological phenomena, and ecosystem evolution. We have shown that land surface emissivity estimates have great potential for use in the detection of FT states since that parameter primarily depends on surface characteristics instead of on direct use of brightness temperatures. This study aims to investigate the potential of merging passive microwave sensors and their land surface emissivity estimates from Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E), Special Sensor Microwave Imager (SSM/I), AMSR2, and the Global Precipitation Measurement (GPM) Microwave Imager (GMI) to provide high temporal resolution (sub-daily) FT states. This factor is of critical importance and usage, primarily during the transitions between freeze and thaw that frequently occur at sub-daily time-frames in spring seasons. Data fusion techinques were used to construct diurnal estimates in order to accurately predicting the exact time of the freeze-thaw transition for a variety of land cover types and geographical regions. The results revealed that emissivity difference values between low and high frequencies (such as 10.7 GHz and 89GHz) at horizontal polarization from multiple platforms have a strong correlation with ground-based soil temperature diurnal values at 5-cm depth. Evaluation of the proposed approach with independent ground observations from year 2015 to 2017 showed that the data fusion of land surface emissivities in high-latitudes was able to notably capture the frequent FT transitions.

Diurnal variations of land surface temperature (LST) have a significant role in a wide range of applications. This study used a fifteen-year series (January 2003 to December 2017) daily observation of LST from MODIS (Moderate Resolution Imaging Spectroradiometer) instrument on board the Aqua and Terra satellites. A spline interpolation method was applied to each year's data to estimate the LST every 30 minutes. The diurnal cycle of LST is computed from the spatially and temporally consistent data at a global scale. The diurnal temperature range and time of occurrence of maximum hour is also calculated for each pixel. The trend of these parameters is compared to a 10-class land cover vegetation and fairly acceptable correlation is found. Analysis of fifteen years diurnal LST record shows a variant global decrease in DTR, but with a few point having increase in amplitude especially in Southern Africa and South America.

The many applications of remote sensing techniques to unearth understanding of the environment has not only increased by leaps and bounds over the past two or so decades, but they have also now become indispensable to routine and comprehensive geophysical studies. Today, all geophysical disciplines utilize remote sensing applications in some form or another, and the trend in such usage is certainly expected to be positive going forward. It is, therefore, critical that the next generation of remote sensing tool developers and users be adequately attracted, recruited, retained, and trained to meet current 21stcentury environmental challenges and those challenges that lie ahead. It is also equally important (for a plethora of reasons) that this prospective new cohort of remote sensing developers and users be both ethnically diverse and highly skilled. To this end, the New York City College of Technology (City Tech) of the City University of New York has used a National Science Foundation Research Experiences for Undergraduates (NSF REU) grant to develop and implement an intrusive remote sensing research mentoring program that targets minority students. Programmatic results indicate that the remote sensing mentoring program has been highly successful in increasing the mentees' remote sensing knowledge and research skills, their communication of remote sensing concepts and ideas, and their research creativity, autonomy, and intellect.

Urban heat islands cause that built up areas experience warmer temperature than their surrounding rural regions. This issue can adversely affect the energy consumption and public health especially in highly populated cities. The aim of this research study is to characterize the effect and the response of each surface type in the cities to increase our understanding of climate, anthropogenic heat, and urban heat islands. Flux tower observations as well as satellite-based remote sensing measurements are two source of valuable information. Flux towers are rarely deployed in the cities or built up environment and mostly take measurements in natural surfaces. Here we deploy several flux towers on different surface in New York City to enhance our understanding about the reaction of each surface to the energy balance. Complete energy balance stations are installed over distinct materials such as concrete, asphalt, and rooftops. This study can help to provide a novel approach to use ground observations and map the maxima and minima air temperature in New York City using satellite measurements. Satellites also provide many measurements from the earth surface at various spatial resolutions. MODIS data sets particularly deliver skin temperature. Moreover, satellite observations from Landsat 8 are utilized to classify the city surfaces to distinct defined surfaces where ground observations were obtained. The mapped temperatures will be linked to MODIS surface temperatures to develop a model that can downscale MODIS skin temperatures to fine resolution air temperature over urban regions. The evaluation of results against independent ground observations reveals that the proposed method is promising for studying surface energy balance in urban regions.

Monitoring freeze-thaw (FT) transitions in high latitude regions are critical to enhancing our knowledge about the prediction of biogeochemical transitions, carbon dynamics, climate change, and impacts on boreal-arctic ecosystems. Since land surface emissivity depends primarily on the surface characteristics, it would contains valuable information about the surface, especially regarding freeze and thaw states. The surface characteristics in terms of microwave emission changes whenever water undergoes phase changes at constant temperature. This study aims to investigate the potential of using emissivity estimates from various microwave sensors such as the Advanced Microwave Scanning Radiometer — Earth Observing System (AMSR-E), Special Sensor Microwave Imager (SSM/I), AMSR2, and the Global Precipitation Measurement (GPM) Microwave Imager (GMI). It employs data fusion techinques to construct diurnal estimates in order to accurately predicting the exact time of the freeze-thaw transition for each land cover type and region. The results reveal that emissivity difference values from low and high frequencies (such as 6.9GHz and 89GHz) at horizontal polarization have a strong correlation with ground-based soil temperature values at 5cm depth. A novel threshold-based approach specific to different land cover types is proposed for daily FT detection from the use of three years (August 2012–July 2015) of emissivity estimates at different frequencies. Ground-based soil temperature observations are used as reference to develop threshold values for FT states. Preliminary evaluation of the proposed approach with independent ground observations for the year 2015 shows that the use of land emissivity estimates for high-latitude FT detection is promising with fine temporal resolution (at least 4 times a day).

Reversing the dramatic decrease in geoscience interest, awareness, participation, and preparation among students at all levels in the United States has become a paramount priority, particularly since recent studies ([1], [2], and [3]) project that this critical regression is expected to continue well into this young century. Investments by the U.S. National Science Foundation's Opportunities for Enhancing Diversity in the Geosciences program ([4], [5], [6], [7], [8], [9]) have yielded innovative insights, practical strategies, and replicable models that are designed to broaden student access, participation, and success at various stages of the geoscience pipeline. However, despite these transformative initiatives, more needs to be done to ensure that evidence-based practices are utilized to attract students and to increase their persistence and achievement in the geosciences. At the New York City College of Technology (City Tech) of the City University of New York, a new, pioneering, piloted pathway model in which undergraduates majoring in STEM disciplines are being equipped with vital geoscience and remote sensing skills for the geoscience workforce is successfully on-going. Components of this work are presented in this manuscript.

Freeze and thaw (FT) processes have profound impact on the terrestrial water cycle, net primary productivity, carbon cycle, surface energy budget and hence the global climate system. The available passive microwave based FT states data are basically developed from brightness temperatures, which themselves affected by atmospheric water vapor content. Since land surface emissivity estimates derived from passive microwave observations are free from atmospheric effects, the use of land emissivity in FT states detection is promising. The objective of this study is to estimate land surface emissivity from the Advanced Microwave Scanning Radiometer-2 observations and to investigate its potential for high-latitude FT states detection. The instantaneous land surface emissivity is computed using an improved algorithm along with near-simultaneous ancillary data sets. The difference of estimated land emissivity between higher and lower frequency channels shows great potential for FT states detection.

A reliable estimate of emissivity is critical for a wide range of applications for the atmosphere, the biosphere, the lithosphere, the cryosphere, and the hydrosphere. This study uses three years (August 2012 to July 2015) of data from the Advanced Microwave Scanning Radiometer-2 sensor that is onboard the Global Change Observation Mission 1st Water satellite to explore estimates of instantaneous global land emissivity. A method is adopted to remove the known inconsistency in penetration depths between microwave brightness temperatures and infrared-based ancillary data that could cause differences between day and night emissivity estimates. After removing the diurnal atmospheric effects, the resulting retrieved cloud-free land emissivities realistically represent well-known large-scale features. As expected, the polarization differences of estimated emissivities show noticeable seasonal variations over the deciduous woodland and grassland regions due to changes in vegetation density. The potential of estimated emissivities for high-latitude snow detection and freeze/thaw state identification is also demonstrated.

To understand the atmospheric phenomena such as rain rate, cloud liquid water, and total precipitable water from satellite microwave observations, the surface contribution should be accounted and be removed from the microwave signal. The objective of this proposed research is to develop a land surface emissivity that facilitates providing this information. The emissivity product will improve the Goddard PROFiling algorithm (GPROF) precipitation estimates. It makes use of microwave measurements from newly launched Global Precipitation Mission (GPM) Microwave Imager (GMI) sensor to produce an emissivity database for a range of frequencies from 6.9 GHz (C band) to high frequencies such as 183 GHz. The goal of this work is to inter-compare four global land surface emissivity products over various land-cover conditions to assess their consistency. The intercompared retrieved land emissivity products were generated over five-year period (2003-2007) using observations from the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E), Special Sensor Microwave Imager (SSM/I), The Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and Windsat. First, all products were reprocessed in the same projection and spatial resolution as they were generated from sensors with various configurations. Then, the mean value and standard deviations of monthly emissivity values were calculated for each product to assess the spatial distribution of the consistencies/inconsistencies among the products across the globe. The emissivity products were also compared to soil moisture estimates and satellite-based vegetation index to assess their sensitivities to the changes in land surface conditions.

Uncertainties in the retrievals of microwave land-surface emissivities are quantified over two types of land surfaces: desert and tropical rainforest. Retrievals from satellite-based microwave imagers, including the Special Sensor Microwave Imager, the Tropical Rainfall Measuring Mission Microwave Imager, and the Advanced Microwave Scanning Radiometer for Earth Observing System, are studied. Our results show that there are considerable differences between the retrievals from different sensors and from different groups over these two land-surface types. In addition, the mean emissivity values show different spectral behavior across the frequencies. With the true emissivity assumed largely constant over both of the two sites throughout the study period, the differences are largely attributed to the systematic and random errors in the retrievals. Generally, these retrievals tend to agree better at lower frequencies than at higher ones, with systematic differences ranging 1%-4% (3-12 K) over desert and 1%-7% (3-20 K) over rainforest. The random errors within each retrieval dataset are in the range of 0.5%-2% (2-6 K). In particular, at 85.5/89.0 GHz, there are very large differences between the different retrieval datasets, and within each retrieval dataset itself. Further investigation reveals that these differences are most likely caused by rain/cloud contamination, which can lead to random errors up to 10-17 K under the most severe conditions.

Microwave land surface emissivity is one of the key inputs in Numerical Weather Prediction (NWP) models. It can be used also to classify land surface and subsurface properties such as soil moisture. The objective of this study is to develop global land effective emissivity product using passive microwave observations from the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E). Ancillary data such as land surface skin temperature and cloud flag were obtained from the International Satellite Cloud Climatology Project (ISCCP) data set are used in the analysis. Atmospheric parameters were obtained from TOVS observations to account for the upwelling and downwelling atmospheric emissions as well as atmospheric transmission. Instantaneous land emissivity maps were determined over cloud free pixels for each ascending and descending overpass. In addition, a global clear sky composite is produced on monthly basis. The derived maps show acceptable agreement with the global pattern of land cover types. The emissivity maps also agree reasonably well with the existing SSM/I based product.