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 urban heat island (UHI) effect, with its substantial energy, health, and societal implications, is a major environmental concern in urban regions, particularly in historically underserved and socially vulnerable communities. This study examines the linkage between redlining and the UHI effect by investigating socioeconomic, sociodemographic, policy, and land surface temperature characteristics. Using remote sensing observations from multiple satellites, we employ a Heat Vulnerability Index to assess heat vulnerability disparities across New York City, incorporating poverty, race, summer surface temperatures, and green spaces. Our findings reveal that regions historically designated as "hazardous" due to redlining practices experience an average of 2 degrees Celsius higher surface temperatures compared to more favored areas. Additionally, redlined neighborhoods exhibit smaller green areas, indicating disparities in vegetation cover. While some progress has been made over time, the persistence of these disparities highlights the importance of incorporating these factors in heat stress mitigation and adaptation strategies for heterogeneous urban areas in terms of land cover and socioeconomics.

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.

The presence of Harmful Algal Blooms (HABs) occurs when colonies of cyanobacteria grow out of control and produce toxic or harmful effects on humans, fish and livestock. They are among most important factors that threaten water quality of lakes. The New Jersey Department of Environmental Protection (NJDEP) has been monitoring 800+ lakes since 2005. A possible alternative manner of monitoring the water's algae levels would be remote sensing from Landsat-8 and Sentinel-2 observations. Here, we utilized these observations and in situ data to examine the effectiveness of existing algorithms to find a regionally robust method that is applicable for NJ lakes. The methods consist of experimental equations that use several visible and near-infrared remote sensing data. Two selected methods were found generally able to predict Chl-a variations; however, they seem to have different performance accuracy when they are used for shallow and deep parts of the lakes. The results indicate the remote sensing observations could be used for monitoring lakes water quality.

Understanding land-atmosphere interactions in extremely hot environment offers insights on how such interactions will change in a warmer world. For this reason, scientists from a wide range of fields, including hydrology, meteorology, ecology and geology, have been interested in identifying the hottest places on Earth. A study back in 2006 based on the moderate resolution imaging spectroradiometer (MODIS) land surface temperature (LST) data identified the Lut Desert in Iran as the “thermal pole of the Earth.” Since then, Lut Desert has been regarded as the hottest place on Earth with the record temperature of 70.7 °C observed in 2005. Using the latest MODIS-derived LST collection 6 which offers an improved LST estimates with a high spatial resolution (1 km), we investigate the hottest temperatures, as well as its diurnal variability in Lut Desert. The results show that Lut Desert is much hotter than previously thought with a record LST of 80.83 °C in 2018 (approximately 10 °C higher than previously reported LST) mainly due to improvements in the new LST estimations from space, and use of higher spatial resolution of the data. Further, our results show that Lut Desert has an incredible diurnal variability range, up to around 71 °C depending on the season.

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.

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.

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).

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.

Spatial and temporal variability in landscape freeze- thaw (FT) status at higher latitudes and elevations significantly impacts land surface water mobility and surface energy partitioning, with major consequences for regional climate, hydrological, ecological, and biogeochemical processes. With the development of new-generation spaceborne remote sensing instruments, future L-band missions, including the NASA Soil Moisture Active and Passive mission, will provide new operational retrievals of landscape FT state dynamics at moderate (~3 km) spatial resolution. We applied theoretical simulations of L-band radar backscatter using first-order radiative transfer models with two and three-layer modeling schemes to develop a modified seasonal threshold algorithm (STA) and FT classification study over Alaska using 100-m-resolution satellite Phased Array L-band Synthetic Aperture Radar (PALSAR) observations. The backscatter threshold distinguishes between frozen and nonfrozen states, and it is used to classify the predominant frozen or thawed status of a grid cell. An Alaska FT map for April 2007 was generated from PALSAR (ScanSAR) observations and showed a regionally consistent but finer FT spatial pattern than an alternative surface air temperature-based classification derived from global reanalysis data. Validation of the STA-based FT classification against regional soil climate stations indicated approximately 80% and 75% spatial classification accuracy values in relation to respective station air temperature and soil temperature measurement-based FT estimates. An investigation of relative spatial scale effects on FT classification accuracy indicates that the relationship between grid cell size and classified frozen or thawed area follows a general logarithmic function.