Past Projects at CReSIS
APM EAGER: WHEAT PHENOME/GENOME SENSING/MODELING VIA MICROWAVE SCATTERING INVERSION
Lead Institution: Kansas State University
Carlton Leuschen (KU –PI, Fernando Rodriguez-Morales, Co-I)
Sponsor: USDA/National Institute of Food and Agriculture
12/01/2016-11/30/2019
Wheat is a globally important grain at the forefront of food security issues. However, like rice, maize, and sorghum, it is achieving barely 50% of the annual yield progress rate necessary to meet food needs widely forecast for 2050. For over ten years an approach melding ecophysiological and quantitative genetic modeling has been evolving the potential to accelerate breeding rates of gain. This entails a two-step process that first fits crop models to data and then association maps the resulting parameter values to genetic markers. However, technological limits impede collection of the large amounts of needed plant trait data, especially the geometry of dense plant canopies. Targeting Kansas wheat breeding trials, this project is a proof-of-concept test combining microwave radar sensing with a novel, inversion algorithm to ameliorate the situation. The basic rationale is that (1) it is unnecessary to sense the 3D position, angle, and size of every tiller and leaf in a trial plot - rather one desires the genetic markers and effect sizes associated with these quantities' statistical distributions; (2) models interrelating markers and morphology exist; (3) if radar calculations for plant canopies can be accelerated, then the models in (2) can be inverted to yield genetics in a single-step; and (4) an extension of the Analytical Element Method (AEM) from hydrology to electromagnetic (EM) wave propagation can provide such a speed up. Briefly, the AEM exactly solves the field equations for very simple shapes that are then combined to yield machine accurate-answers for complex geometries. Unlike solvers in common use, the AEM only calculates solutions at the specific points of interest, thus hugely reducing computational loads. Prior work has found AEM solutions for EM waves in two dimensions. This project will extend those solutions to full 3D.Concurrently, an existing wheat model that predicts highly realistic plant shapes will be modified so its outputs are expressed in terms of the AEM basic shapes. A three-layer model will then be built comprising [genetic markers : plant shapes : EM fields] and solved by probabilistic methods. This will yield the genetic markers most associated with the plant shapes sensed by radar. The method will be tested by team members with radar expertise using the facilities of the Center for Remote Sensing of Ice Sheets. Experiments in a large anechoic chamber will compare AEM predictions to actual radar reflectance data for simplified targets. The EM properties of wheat at radar frequencies will also be measured in the chamber using small, movable plots. Based on these data, a prototype field system will be constructed and used to gather plot data in a field trial conducted as part of the on-going Kansas wheat breeding program. Two tests will be performed. First the radar data will be association mapped directly to detect any responses to genetically determined canopy features. If positive results are found, they will be compared to published phenotypic mapping studies and hypotheses developed as to features to which the radar might be responding. The second test will solve the three layer model described above and also compare the results to literature.
Controls on Iceberg Distribution Around Greenland
PI- Leigh Stearns
Sponsor: NASA
5/9/2016 - 5/8/2019
Objective: The over-arching objective of this proposal is to quantify calving behavior and iceberg
distribution around the Greenland Ice Sheet.
Approach: Iceberg calving accounts for roughly 50% of the ice mass loss from the Greenland Ice
Sheet; however, little is known about how iceberg calving varies seasonally and spatially. The size
of icebergs that break o glacier termini are dictated by geometry, ice velocity, submarine melt
rates and buoyancy conditions. The freshwater ux derived from icebergs, which is largely dictated
by their size and distribution, impacts fjord circulation and sea ice formation. In order to improve
our understanding of ice-ocean interactions, a comprehensive database of glacier calving and iceberg
properties is needed. We propose using freely available satellite imagery and automated image
processing software to build a glacier/iceberg database that will be easily accessible for public use.
We will use this data to address calving parameterizations, but the data has wide applicability for
physical oceanography, climate models, ecology, sea ice science and polar shipping.
Uniqueness: Processes that occur at the ice-ocean boundary are complicated and observations
are often disjointed. In this project, we analyze satellite imagery to create a database of calving
behavior and iceberg size distribution { essential parameters for glacier, ocean and climate models.
Value to NASA: Glacier termini, and the characteristics of icebergs they produce, play an important
role in ice sheet mass balance and sea ice formation { two key elements of this NASA
solicitation. Icebergs provide a direct link between the ice sheets, atmosphere and oceans, however
they are poorly quantied in the Arctic. In particular, the dataset produced through this project
will link the dynamics of marine terminating glaciers and the distribution of icebergs around the
coast of Greenland. Understanding the trajectory of mass ux from the ice sheet to the oceans is
crucial for the development of fjord circulation models, as well as coupled climate models