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Field Observations During the Second Microwave Water and Energy Balance Experiment (MicroWEX-2): from March 17 through June 3, 2004

Jasmeet Judge, Joaquin Casanova, Tzu-Yun Lin, Kai-Jen Calvin Tien, Mi-young Jang, Orlando Lanni, and Larry Miller

Abstract—Circular 1480

For accurate prediction of weather and near-term climate, root-zone soil moisture is one of the most crucial components driving the surface hydrological processes. Soil moisture in the top meter is also very important because it governs moisture and energy fluxes at the land-atmosphere interface and it plays a significant role in partitioning of the precipitation into runoff and infiltration.

Energy and moisture fluxes at the land surface can be estimated by Soil-Vegetation-Atmosphere-Transfer (SVAT) models. These models are typically used in conjunction with climate prediction models and hydrological models. Even though the biophysics of moisture and energy transport is well-captured in most current SVAT models, the computational errors accumulate over time and the model estimates of soil moisture diverge from reality. One promising way to improve significantly the model estimates of soil moisture is by assimilating remotely sensed data that is sensitive to soil moisture, for example microwave brightness temperatures, and updating the model state variables.

The microwave brightness at low frequencies (< 10 GHz) is very sensitive to soil moisture in the top few centimeters in most vegetated surfaces. Many studies have been conducted in agricultural areas such as bare soil, grass, soybean, wheat, pasture, and corn to understand the relationship between soil moisture and microwave remote sensing. Most of these experiments conducted in agricultural regions have been short-term experiments that captured only a part of growing seasons. It is important to know how microwave brightness signature varies with soil moisture, evapotranspiration (ET), and biomass in a dynamic agricultural canopy with a significant biomass (4-6 kgm2) throughout the growing season.

The goal of MicroWEX-2 was to understand the land-atmosphere interactions during the growing season of corn, and their effect on observed microwave brightness signatures at 6.7 GHz, matching that of the satellite based microwave radiometer, AMSR. Specific objectives of MicroWEX-2 are:

  1. To collect passive microwave and other ancillary data to develop and calibrate a dynamic microwave brightness model for corn.
  2. To collect energy and moisture flux data at land surface and in soil to develop and calibrate a dynamic SVAT model for corn.
  3. To evaluate feasibility of soil moisture retrievals using passive microwave data at 6.7 GHz for the growing corn canopy.

The publication contains the detailed information regarding the sensors deployed and data collected during the MicroWEX-2. To view the entire publication, see the accompanying PDF file on EDIS: https://edis.ifas.ufl.edu/pdffiles/AE/AE36000.pdf

For related documents, please visit:

https://edis.ifas.ufl.edu/AE288

Field Data Report for the First Microwave Water and Energy Balance Experiment (MicroWEX-1), July 17–December 16, 2003, Citra, Florida

https://edis.ifas.ufl.edu/pdffiles/AE/AE36100.pdf

Field Observations During the Third Microwave Water and Energy Balance Experiment (MicroWEX-3): June 16–December 21, 2004

https://edis.ifas.ufl.edu/pdffiles/AE/AE36200.pdf

Field Observations During the Fourth Microwave Water and Energy Balance Experiment (MicroWEX-4): March 10–June 14, 2005

https://edis.ifas.ufl.edu/pdffiles/AE/AE36300.pdf

Measurement of Soil Surface Roughness During the Fourth Microwave Water and Energy Balance Experiment: April 18–June 13, 2005

Publication #CIR1480

Release Date:September 22, 2020

Related Experts

Judge, Jasmeet

Specialist/SSA/RSA

University of Florida

  • Critical Issue: Other
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About this Publication

This document is CIR1480, one of a series of the Department of Agricultural and Biological Engineering, UF/IFAS Extension. Original publication date November 2005. Visit the EDIS website at https://edis.ifas.ufl.edu for the currently supported version of this publication.

About the Authors

Jasmeet Judge, assistant professor and Director of the Center for Remote Sensing; Joaquin Casanova, undergraduate research assistant; Tzu-yun Lin, graduate research assistant; Kai-Jen Calvin Tien, graduate research assistant; Mi-young Jang, graduate research assistant; Orlando Lanni, engineer; and Larry Miller, engineer; Department of Agricultural and Biological Engineering, UF/IFAS Extension, Gainesville, FL 32611.

Contacts

  • Jasmeet Judge