Spatial variability in microwave radiometric signatures of growing corn and soybean during SMAPVEX16-microwex

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Spatial variability in microwave radiometric signatures of growing corn and soybean during SMAPVEX16-microwex

Conference Paper (2017)

Author(s)

Pang Wei Liu (University of Florida)

Jasmeet Judge (University of Florida)

Subit Chakrabarti (University of Florida)

Roger Deroo (University of Michigan)

Susan Steele-Dunne (TU Delft - Civil Engineering & Geosciences)

Brian Hornbuckle (Iowa State University)

Andreas Colliander (California Institute of Technology)

Sidharth Misra (California Institute of Technology)

Scott Tripp (California Institute of Technology)

Barron Latham (California Institute of Technology)

Ross Williamson (California Institute of Technology)

Isaac Ramos (California Institute of Technology)

Simon Yueh (California Institute of Technology)

Anthony England (University of Michigan-Dearborn)

Research Group

Water Resources

Soil Moisture Passive Microwave Remote Sensing Scale Variability Vegetation Opacity

DOI related publication

https://doi.org/10.1109/IGARSS.2017.8127865 Final published version

To reference this document use

https://resolver.tudelft.nl/uuid:e2b4fd49-f7e7-46b8-a4f4-bccc6abba65e

More Info

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Publication Year

2017

Language

English

Research Group

Water Resources

Volume number

2017-July

Article number

8127865

Pages (from-to)

3953-3956

ISBN (electronic)

9781509049516

Event

37th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2017 (2017-07-23 - 2017-07-28), Fort Worth, United States

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Abstract

In this study, the impact of spatial variability due to the heterogeneity of vegetation in the agricultural region on passive microwave signatures available at various scales are explored using the brightness temperature (T_B) observed from ground, air, and space. These observations were conducted during a growing season of corn and soybean in South Fork watershed, Iowa, as part of the NASA-Soil Moisture Active Passive Validation Experiment (SMAPVEX16). Both empirical and physically-based microwave emission models are used to understand the effects of vegetation on T_B for corn and soybean using ground-based T_B observations. The modeled T_B will be upscaled based upon the USDA crop layer map to compare with the T_B observed in the coarse scales.