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Analysis of Global Drought and Land-Cover/ Land-Use Change Using Satellite Passive Microwave Observations

Undergraduate #253
Discipline: Ecology Environmental and Earth Sciences
Subcategory: Climate Change

Yanna Chen - New York City College of Technology
Co-Author(s): Hamidreza Norouzi, Reginald Blake, and Satya Prakash, New York City College of Technology, Brooklyn, NY Amir Aghakouchak, University of California Irvine, Irvine, CA



The anomalous changes in land cover and droughts affect the global climate system that are currently among the major concerns affecting the world. Therefore, recognizing the ever increasing environmental changes and their impacts on sustainable development issues is crucial. The Earth-observation satellites along with ground-based observations provide a unique opportunity to monitor these changes at global and regional scales. The purpose of this research is to investigate the potential of using passive microwave brightness temperature and emissivity data sets from satellite observations to monitor recent land cover/ land-use changes and droughts. The passive microwave observations at various frequencies from the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), AMSR2, and the Special Sensor Microwave Imager (SSM/I) have been formulated to calculate the global land surface emissivity for the last three decades. In this study, the lower frequencies are used because at such frequencies, the signals are more sensitive to surface properties such as soil moisture and vegetation. The Brightness Temperature Microwave Polarization Difference Index (MPDI) are computed for both vertical and horizontal polarization values which is considered similar to the emissivity data set. Both MPDI and emissivity estimates for three decades are then compared against the independent drought indicators from the Global Integrated Drought Monitoring and Prediction System (GIDMaPS) based on precipitation and soil moisture data records. Moreover, a drought severity test is performed using suitable statistical techniques that previously was deployed on precipitation and soil moisture data sets. Besides, the two-way ANOVA test is used to directly compare with brightness temperature, emissivity, precipitation and soil moisture data sets. The various linear combinational estimation models and the contrast models are created. A preliminary analysis has been performed to investigate the potential application of passive microwave land emissivity estimates in drought monitoring.

Funder Acknowledgement(s): Supported by Baccalaureate Student Research Scholars Program (BRSP); The National Science Foundation's Research Experiences for Undergraduates (NSF REU) Grant No. AGS-1062934; The National Oceanic and Atmospheric Administration Cooperative Remote Sensing Science and Technology Center (NOAA-CREST) for supporting this project. NOAA CREST - Cooperative Agreement No: NA11SEC4810004.

Faculty Advisor: Hamidreza Norouzi, hnorouzi@citytec.cuny.edu

Role: I am participating the whole research. From collecting the data from the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), AMSR2, and the Special Sensor Microwave Imager (SSM/I) at various frequencies to analyze the global precipitation, soil moisture, brightness temperature and emissivity by using different methods. Finally, a drought severity test is performed using suitable statistical techniques that previously was deployed on precipitation and soil moisture data sets. Currently, I am creating the various linear combinational estimation models and the contrast models to directly compare with brightness temperature, emissivity, precipitation and soil moisture data sets.

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This material is based upon work supported by the National Science Foundation (NSF) under Grant No. DUE-1930047. Any opinions, findings, interpretations, conclusions or recommendations expressed in this material are those of its authors and do not represent the views of the AAAS Board of Directors, the Council of AAAS, AAAS’ membership or the National Science Foundation.

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