
Tara Bongiovanni, Heather Enos, Alejandro Monsivais-Huertero, Blaire Colvin, Karthik Nagarajan, Jasmeet Judge, Pang-Wei Liu, Juan Fernandez-Diaz, Roger De Roo, Yuriy Goykhman, Xueyang Duan, Daniel Preston, Ramesh Shrestha, Clint Slatton, Mahta Moghaddam, and Anthony England2
Full text of this publication can be found at http://edis.ifas.ufl.edu/pdffiles/AE/AE47600.pdf.
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 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 numerical weather and near-term climate prediction models and surface-subsurface hydrological models. Even though the biophysics of moisture and energy transport is well-captured in most current SVAT models, the errors in initialization, forcings, and computation accumulate over time, and the model estimates of soil moisture in the root zone diverge from reality. Remotely sensed microwave observations can be assimilated in these models to improve root zone soil moisture estimates.
The microwave signatures at low frequencies, particularly at 1.4 GHz (L-band), are 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. It is important to know how microwave signatures vary with soil moisture, evapotranspiration (ET), and biomass in a dynamic agricultural canopy with a significant biomass (4–6 kg/m2) throughout the growing season.
The goal of MicroWEX-8 was to conduct a pilot study that incorporated active and passive microwave observations as well as Light Detection and Ranging (LiDAR) observations for a growing season of sweet corn. The variety of instruments would allow for further understanding of the land-atmosphere interactions during the growing season and their effect on observed passive microwave signatures at 6.7 GHz and 1.4 GHz, active microwave signatures at 1.14 GHz, and LiDAR scans. These observations match that of the satellite-based passive microwave radiometers, Advanced Microwave Scanning Radiometer (AMSR), and the Soil Moisture and Ocean Salinity (SMOS) mission, respectively, and the upcoming NASA Soil Moisture Active Passive (SMAP) mission. Specific objectives of MicroWEX-8 included the following:
1. To field-test the micrometeorological, microwave radiometer, radar, LiDAR, and other sensors to characterize errors.
2. To collect passive and active microwave, LiDAR, and other ancillary data to develop preliminary algorithms to estimate microwave signatures for corn.
3. To evaluate feasibilty of soil moisture retrievals using passive microwave data at 6.7 and 1.4 GHz and active microwave data at 1.14 GHz for the growing corn canopy.
Related publications can be found on the Microwave Water and Energy Balance Experiments topic page: http://edis.ifas.ufl.edu/TOPIC_Microwave_Water_and_Energy_Balance_Experiments
This document is AE476, one of a series of the Agricultural and Biological Engineering Department, Florida Cooperative Extension Service, Institute of Food and Agricultural Sciences, University of Florida. Original publication date July 2011. Visit the EDIS website at http://edis.ifas.ufl.edu.
Heather Enos and Blaire Colvin are undergraduate research assistants of the Center for Remote Sensing (CRS) at the University of Florida (UF); Tara Bongiovanni and Pang-Wei Liu are graduate research assistants of CRS at UF; Alejandro Monsivais-Huertero and Karthik Nagarajan are post-doctoral researchers of CRS at UF; Jasmeet Judge is an assistant professor and director of CRS at UF (email: jasmeet@ufl.edu); Juan Fernandez-Diaz is a graduate research assistant of the National Center for Airborne Laser Mapping (NCALM) currently located at the University of Houston (UH); Roger De Roo is a professor at the University of Michigan (UM); Yuriy Goykhman and Xueyang Duan are graduate research assistants at UM; Daniel Preston is an engineering technician of CRS at UF; Ramesh Shrestha is a professor director of NCALM at UH; Clint Slatton is an associate professor at UF; Mahta Moghaddam is a professor at UM; Anthony England is a professor at UM. Heather Enos, Blaire Colvin, Tara Bongiovanni, Alejandro Monsivais-Huertero, Karthik Nagarajan, Jasmeet Judge, Pang-Wei Liu, and Daniel Preston are affiliated with the Agricultural and Biological Engineering Department, Institute of Food and Agricultural Sciences, University of Florida, Gainesville, FL 32611. Juan Fernandez-Diaz and Ramesh Shrestha are affiliated with the Civil Engineering Department, University of Houston, Houston, TX 77204. Roger De Roo, Yuriy Goykhman, Xueyang Duan, Mahta Moghaddam, and Anthony England are affiliated with the Electrical Engineering and Computer Sciences Department, University of Michigan, Ann Arbor, MI 48109. Clint Slatton is affiliated with the Electrical and Computer Engineering and the Civil and Coastal Engineering Department, University of Florida, Gainesville, FL 32611.
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