H117
Remote Sensing Data and Tools to Enhance Modeling and Resilience of Food-Energy-Water Nexus

Session ID#: 83087

Session Description:
Food, energy, and water (FEW) are complex interconnected systems. New innovative tools are critically needed and currently emerging to provide appropriate modeling approaches to understand FEW systems that also require the integration of different types of datasets at multiple spatial and temporal scales. Recent advances in remote sensing datasets and tools in terms of availability and accessibility are capable to enhance FEWS modeling and its resilience to a wide range of drivers of FEW including urbanization, economic growth, shifting consumption patterns, and extreme events like drought and flood. Remote sensing of evapotranspiration, crop yield, rangeland productivity, and drought help to evaluate water use for food production and energy consumption. This session invites topics that consider innovative remote sensing approaches coupled with FEWS modeling for early detection of disturbances to FEWS and help engaging stakeholders and decision-makers to develop management options for resilient FEWS at multiple spatial and temporal scales.
Index Terms:

1847 Modeling [HYDROLOGY]
1855 Remote sensing [HYDROLOGY]
1899 General or miscellaneous [HYDROLOGY]
1906 Computational models, algorithms [INFORMATICS]

Primary Convener:  Hatim M.E. Geli, New Mexico State University Main Campus, Las Cruces, NM, United States
Convener:  Kamini Yadav, New Mexico State University Main Campus, Las Cruces, NM, United States
Primary Liaison:  Hatim M.E. Geli, New Mexico State University Main Campus, Las Cruces, NM, United States
 
An integrated analytic framework for water and crop production in agroecosystems (499126)
Ramesh Dhungel1, Rob Aiken1, Xiaomao Lin1, Seth Kutikoff1 and Guanqiang Zuo2, (1)Kansas State University, Manhattan, KS, United States, (2)Heilongjiang Bayi Agricultural University, Daqing, China
 
Assessing the Crop, Energy and Water Nexus in the Central Valley California Using Remote Sensing Products (530149)
Ruopu Li, Southern Illinois University Carbondale, Carbondale, IL, United States, Sarfaraz Alam, University of California Los Angeles, Civil and Environmental Engineering, Los Angeles, CA, United States and Mekonnen Gebremichael, University of California, Los Angeles, CA, United States
 
Detection of Rangeland Degradation in New Mexico using Time Series Segmentation and Residual Analysis (TSS-RESTREND) (545601)
Melakeneh Gelet Gedefaw, Hatim M.E. Geli and Kamini Yadav, New Mexico State University Main Campus, Las Cruces, NM, United States
 
Understanding the Dynamic Behavior of New Mexico’s Food-Energy-Water Resources in Response to Drought Using Remote Sensing (552975)
Kamini Yadav and Hatim M.E. Geli, New Mexico State University Main Campus, Las Cruces, NM, United States
 
Machine learning based estimation of land productivity in the continental US using biophysical predictors (615038)
Pan Yang1,2, Qiankun Zhao1 and Ximing Cai1, (1)University of Illinois at Urbana Champaign, Department of Civil and Environmental Engineering, Urbana, IL, United States, (2)University of Illinois at Urbana Champaign, DOE Center for Advanced Bioenergy and Bioproducts Innovation, Urbana, IL, United States
See more of: Hydrology
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