Professor (2016 - 2018) Department of Earth System Science and Policy, University of North Dakota
Adjunct Professor (January 1, 2018 - December 31, 2019) Department of Atmospheric Sciences, University of North Dakota
Graduate Director (2012 - 2016) Department of Earth System Science and Policy, University of North Dakota
Associate Professor (2007-2016) Department of Earth System Science and Policy, University of North Dakota
Assistant Professor (2003-2007) Department of Earth System Science and Policy, University of North Dakota
Assistant Research Professor (2002-2003) Earth System Science Institute, University of North Dakota
Lecturer (1995-1996) Ocean Remote Sensing Institute, Ocean University of China
Visiting scholar (1994) School of Marine Science and Technology, Tokai University, Japan
Assistant Engineer (1989-1993) Ocean Remote Sensing Institute, Ocean University of China
ESSP 502, 502R, 502L (10 credits): Hydrological Cycle, Recitation, Laboratory
ESSP 530 (3 credits): Principles in Environmental Physics
ESSP 599 (3 credits): Aquatic Optics
ESSP 333 (3 credits): Oceanography
Propagating through the ocean, light interact with water and its constituents, causing physical (heating), chemical (photochemical reaction) and biological (photosynthesis) changes. The ocean contains a diverse mixture of dissolved and particulate matter that constantly change in time and space. Each of these constituents exerts an influence on the spectral absorption, angular scattering, and polarization state of light. Therefore, the optical measurements implicitly contain embedded information regarding the concentration, sizes, composition, shape and structure of these materials and their dynamics. A major focus of my research is to decipher these linkages to retrieve useful information about various water constituents from optical measurements. When extended to optical data obtained remotely from in situ or air- and space-borne platforms, such measurements offer observational capabilities over a much broader range of temporal and spatial scales over which regional and global investigation of the ocean can be conducted.While always passionate about studying ocean, I have also applied my knowledge in optics to water bodies that are much smaller than the oceans but are more regionally relevant. I'm interested in detecting, monitoring and/or mitigating changes of water quality in lakes, rivers and wetlands as a result of changes in regional climate and land use.With a B.S. degree in computer science, I have always enjoyed programming. I have developed a couple of web-based decision support systems utlizing remote sensing data for precision agriculture.
Research projects (Title, Agency, Duration and Summary)
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1. Optically resolving size and composition distribution of particles in the dissolved-particulate continuum from 20 nm to 20 mm to improve the estimate of carbon flux (PI, NASA, 12/01/2017 - 11/30/2020)
When it comes to mass/carbon fluxes out of the surface layer, all particles are not created equal. Their composition and size distribution play critical roles. In essence, this project will use recent optical technological and theoretical advances to describe the key aspects of particles that control their fluxes from the ocean surface, contrast the export fluxes from different particle types, and relate those to broad classes of phytoplankton communities present in the surface layers for which satellite remote sensing algorithms exist.
The mass flux is typically estimated using the size distribution of particles of sizes > 1 μm. The effect of particle composition on sinking speed and downward transport of non-sinking dissolved and small particulate organic carbon are two of the priorities recently identified for future research towards a transformative understanding of the biological pump [Burd et al., 2016]. To better understand how variation in particle size distribution impact carbon export estimates and therefore to better understand if size or composition is the main driver of the export, we propose to apply the latest technological and theoretical advancements in the field of ocean optics to quantify in situ size and density distributions of particles in the dissolved-particulate continuum from 20 nm to 20 mm. Specifically, we propose to achieve the following objectives:
Objective (i): Measure the vertical distribution of the spectral absorption coefficient (a) and volume scattering function (VSF, β), which will be augmented by additional optical observation of an imaging flow cytometer and an underwater vision profiler (UVP).
Objective ii: Estimate the size and density distributions of particles of sizes from 20 nm – 200 μm from the VSF data, the particles size distribution from 5 - 100 μm from the flow cytometer data, the particle size distribution from 100 μm to 20 mm from the UVP data, and phytoplankton size fractions from the absorption data.
Objective iii: Estimate the total and size-fractioned mass flux using the optically derived results of particle size and density distributions from Objective (ii). The size-fractioned flux will be contrasted between (1) dissolved vs. particulate; (2) small (< 100 μm) vs. large (> 100 μm) particles; and (3) communities dominated by different classes of phytoplankton.
Objective iv: Apply statistical analysis to investigate (1) how the mass flux estimated based on VSF-inversion results (i.e., size and density distribution from 0.02 – 200 μm) relates to the total flux and (2) how the phytoplankton community (macro-, nano-, and pico-) that can be retrieved from the absorption measurements relates to the total flux.
Our ultimate goal is to test if the total mass/carbon flux can be adequately estimated using the biogeochemical variables that can be retrieved from the measurements of inherent optical properties of absorption and volume scattering function in the surface water. This is directly related the EXPORTS’s overarching hypothesis - carbon export from the eutrophic zone and its fate within twilight zone can be predicted knowing characteristics of the surface ocean ecosystem.
2. Deriving phytoplankton size classes, detrital matter, particulate organic matter and particulate inorganic matter from ocean color observation (Science-PI, NASA, 12/01/2017 - 11/30/2020)
We propose to develop advanced inversion algorithms to infer phytoplankton size classes, colored detrital matter, dissolved and particulate matter from ocean color-derived spectral absorption and backscattering coefficients to support the studies of oceanic carbon cycle. The proposed work is built upon our prior and current research linking the inherent optical properties that are measured in the field with their biogeochemical origins in the ocean. We will extend these field-based results to satellite-based algorithms by achieving the following specific objectives:
Objective 1: Develop a satellite-based algorithm to estimate micro-, nano-, and pico-phytoplankton concentrations as well as colored detrital matter (CDM) absorption from the spectral absorption coefficient.
Objective 2: Develop a satellite-based algorithm to estimate the mass concentrations of particulate organic matter (POM) and particulate inorganic matter (PIM) as well as the fractional backscattering by very small particles (VSP) from the spectral backscattering coefficient. Here particulates are operationally defined as those retained by filtration (e.g., pore size = 0.2 μm) and VSPs as filtrate.
Objective 3: Conduct additional field experiments and use available NASA’s satellite products to validate and quantify the uncertainties of the proposed algorithms and estimated products.
Our overarching goal is to apply physical process based understanding to develop new retrieval algorithms for a better prediction of biogeochemical stocks from remotely sensed spectral absorption and backscattering coefficients. The mechanistic nature of the algorithms allow them to be easily adapted for the future ocean color missions, such as the Plankton, Aerosol, Clouds and ocean Ecosystem (PACE) mission.
3. Understanding natural varaibility of VSFs and its impact on biogeochemical retrieval from ocean color (PI, NASA, 12/01/2014 - 11/30/2018)
A key challenge in applying ocean color remote sensing for assessing biogeochemical stocks in the ocean is to link the signal “seen”
by satellite or airborne sensors with the optically active water constituents and their biogeochemical origin. The linkage between
ocean color and biogeochemical stocks is established via the inherent optical properties of water, most importantly the volume
scattering function (beta, m-1 sr-1) and the total absorption coefficient (a, m-1). While our ability to understand and separate the various
components of absorption has improved over the last decades, the major challenge remains in the understanding of the sources of
variability in the volume scattering function, and particularly the backscattering that are directly relevant to ocean color observation.
This has hindered our ability to derive IOPs accurately and to interpret their variability biogeochemically.
Of IOPs, the VSF is most difficult to measure with only few data available. The scarcity of data has led to unrealistic assumptions,
such as that the phase function of particles can be represented by the average of Petzold’s data. This in turn has led to uncertainties
in understanding the roles played by particles of different type in generating remote sensing reflectance and the color of the ocean.
Recent technological and theoretical advances have allowed us 1) to measure the full angular scattering over a diverse aquatic
environments and 2) to interpret the measurements in terms of particle size distribution and composition. We have greatly improved
our understanding of natural variability of the VSF and the biogeochemical origin of the variability. However, these improved
knowledge has yet to applied to ocean color. The objective of this study is to understand the natural variability of the VSF and its
impact on biogeochemical interpretation of ocean color. The answers to this question will help to constrain two major uncertainties
affecting both the current and future PACE ocean color missions: bidirectional effect and sources of backscattering.
Our approach is centered on in-depth analysis of the field measurements of complete sets of IOPs (including full range VSFs) and
biogeochemical stocks covering various aquatic environments through both forward and inverse modeling. The information of
biogeochemical stocks is also contained in the detailed angular pattern of the VSF and can be retrieved by VSF-inversion. Applying
forward modeling to simulate spectral backscattering from the inversion results will tell what biogeochemical information about
particles (such as the size or the type) is retained in, and hence can be possibly retrieved from, the backscattering coefficient derived
from ocean color. Comparisons of the modeled and measured biogeochemical stocks and comparisons of the modeled and measured
spectral backscattering will aid in the interpretation of the modeling results and will also provide a basis for validating, and possibly
refining, the overall modeling approach.
The proposed study addresses a fundamental, yet poorly known, linkage between the optical scattering and biogeochemical properties
of natural waters. The potential outcome of the study can not only advance our understanding of the VSF as a key IOP parameter
but also improve the performance of existing ocean color algorithms by further constraining the uncertainty associated with angular
scattering as well as to guide the development of new approaches for ocean color algorithms.
4. Inferring marine particle properties from polarized volume scattering functions (PI, NSF, 09/25/2015 - 09/24/2018)
Particles in the ocean alter the propagation of light, influencing its color, polarization states and spatial distribution through absorption and scattering. Moreover, the size and compositional distribution of particles in the ocean are fundamental properties of marine systems, affecting ecological trophic interactions, transports of organic matter and elements. Particle characteristics not only affect but reflect changes in many biogeochemical processes in the ocean. It has been challenging to characterize marine particles directly in the field, but recent development in using angular scattering pattern by particles offers great potential. This is a proposal to (1) develop an inversion method to infer particle properties from polarized measurements of the volume scattering functions; and (2) evaluate its accuracy at quantifying the size distributions and composition of particulate subfractions.
Intellectual Merit: The angular patterns of the scattered intensity and polarization state of the scattered light by particles can be described in terms of a 4×4 Mueller matrix (S) that is fundamentally determined by the sizes, shapes, composition, and structures of the particles. Therefore, the properties of the particles can be potentially inferred from measurements of S. Unfortunately, the complete Mueller matrix of oceanic waters has been seldom measured. Even the most commonly measured component, the volume scattering function (element S11) representing the angular distribution of unpolarized light, was scarcely measured until recently (Zhang et al. 2011 & 2012). Zhang et al. (2011) developed an inversion technique to derive size distributions (0.02–200 μm) and composition information of particles from the VSF. Tested in the coastal waters of the US, the inversion results are consistent with the independent measurements of total particle size distribution and chlorophyll concentration (Zhang et al. 2012 & 2013). The recent advent of the LISST-VSF (Sequoia Scientific, Inc.), a commercial field instrument, presents a great opportunity for us to push the development of the inversion technique forward. The LISST-VSF measures three components, S11, S12 and S22 of the Muller matrix , with S12 describing how particles affect the angular scattering of linearly polarized light and S22 delineating cross-polarization between parallel and vertical polarized light. We propose to incorporate the additional information provided by S12 and S22 using state-of-the-art light scattering modeling capabilities (Yang et al. 2013, Bi et al. 2013) to further constrain the inversion with the following: (i) better knowledge in particle shapes (spherical vs. non-spherical); (ii) reduced uncertainty in non-unique solutions because different particle populations could produce similar S11; and (iii) further improved capability to characterize particles in the size range of 0.02–200 μm. The proposed study will greatly enhance our ability to quantify size distributions and refractive indices (closely linked to particle densities) for particle groups such as phytoplankton cells, detrital particles, mineral particles, bubbles, and emulsified oil (if present). Obtaining information about the unperturbed size distributions, concentration, and densities of multiple individual subpopulations in situ will have a broad impact in the ocean science community.
5. Regional climate change and its impacts on hydrological cycle (Co-PI, NSF, 08/01/2014 - 07/30/2019)
Center for Regional Climate Studies (CRCS) is one of the two core research themes under the NSF EPSCoR RII grant.
The goal of the Center for Regional Climate Studies (CRCS) is to develop and apply integrated methods for assessing and predicting climate variation impacts on regional hydrological systems and agricultural production. Following this main theme, six research areas were identified that include:
- Analysis of regional climate variations and data uncertainty
- Prediction of hydrological changes for extreme conditions
- Integration of hydrologic modeling across regional and local scales
- Assessment of crop productivity response to climate and hydrological variations
- Prediction of agricultural autonomous adaptation in response to changing climate and crop productivity
- Exploration of feedback mechanisms between environment and land use changes.
My study is focused on the area 3, investigating changes in climate and land use on water quantity and quality in our region.
6. Quantifying sources of optical backscattering in support of remote-sensing applications for water quality (PI, NASA, 07/26/2013 - 07/25/2019)
A key challenge in applying ocean color remote sensing for monitoring and assessing water quality is to link the signal “seen” by satellite or airborne sensors with the optically active water constituents and their biogeochemical origin. While our ability to understand and separate the various components of absorption has improved over the last decades, the major challenge remains in the understanding of the sources of variability in the backscattering coefficient. This has hindered our ability to understand and interpret the variability in backscattering and remote-sensing reflectance, including the fields of particulate backscattering coefficient, which play a large role in determining both the magnitude and spectral shape of remote-sensing reflectance in coastal marine environments and inland water bodies. This proposal aims specifically at understanding the biogeochemical sources of variability in the backscattering coefficient, a key indicator of water quality, across a range of water types.
From first principles, particle size is a major determinant of scattering because the size of optically significant aquatic particles can vary over at least 4 orders of magnitude, while the real part of refractive index relative to water vary within a relatively narrow range from about 1.02 to 1.20. While early theoretical predictions suggest that submicron particles account for the majority of the particulate backscattering in open ocean, some recent studies indicate that large particles, including phytoplankton, can be a significant contributor. The difficulties in addressing the roles of different types of particles and differently-sized particles in backscattering result from both the experimental challenges to measure separate contributions of various particle types and theoretical challenges to model scattering by arbitrarily shaped heterogeneous particles. The specific objective of this study is to quantify the role played by relatively large particles (> 2-5 micron) in regulating the magnitude and spectral behavior of backscattering coefficient with special emphasis on coastal marine environments and inland water bodies.
Our approach is centered on comprehensive measurements and characterization of natural samples of particulate assemblages in terms of (i) particle size distribution, (ii) particle composition, and (iii) full volume scattering function. The measurements involving size fractionation will allow us to verify experimentally the contribution of large particles. The measurements will be complemented by modeling effort to compute the VSF from the measured particle size distribution and composition and to invert the PSDs from the measured VSFs. Comparisons of the modeled results and measurements will aid in the interpretation of measurements and will also provide a basis for validating, and possibly refining, the overall modeling approach.
Two types of laboratory-based experiments will be conducted. The first will be to examine optically diverse natural water samples collected in oceanic (California coastal waters) and inland aquatic (Great Lakes) environments. The second will be conducted to examine backscattering properties of several phytoplankton species, characterized by different cell size and morphology. The two types of experiments complement each other, with the former focusing on sizes and composition of particles through a series of size fractionation of natural samples and the latter on phytoplankton species, the major aquatic constituents that are optically active.
The proposed study addresses a fundamental, yet poorly known, linkage between the optical backscattering and biogeochemical properties of natural waters. The potential outcome of the study can greatly improve our understanding of the backscattering process in coastal and oceanic environments and thus provide guidance on what and how the biogeochemical state of water can be inferred from the remotely-sensed backscattering coefficient.
7. Evaluating how market and policy affect cellulosic biofuel feedstock production in marginal land of Upper Midwest and its consequences on water sustainability in a changing climate (Co-I, USDA, 09/01/2015 - 08/31/2018)
The crucial issue of growing biofuel feedstock on agricultural land is the possibility of creating competitions for land and water with current existing crop production. The urgent need of producing new-generation energy crops is likely to be met with expansion of agriculture into previously unsuitable and less fertile lands such as uncultivated cropland, marginal land, idle land, and Conservation Reserve Program (CRP) land. However, how this potential alteration of land use and management affect water sustainability, particularly under the looming specter of climate change, remains unknown. The overall goal of this proposed project is to assess the impact of cultivating cellulosic bioenergy crops driven by the crop market/bioenergy policy on water sustainability. The potential impact will be analyzed and contrasted in two river basins in the Upper Midwest – the Red River of the North basin (bordering MN, ND, and SD) and the Republican River basin (bordering KS, NE and CO), each with distinctive hydrological characteristics and irrigation scheme–to understand the water implications (both positive and negative) and identify the most sustainable biomass feedstock for the region’s water resource. The project will focus on growing switchgrass and collecting crop residuals from wheat and sorghum under various management practices (crop rotation, fertilization inputs, tillage, residual collection, and irrigation) on marginal land. To accomplish the goal, we will develop a coupled economics-hydrology model with inputs derived from both satellite and field observations to: 1) estimate how the land use on marginal land would change in response to the market and policy regarding the production of advanced biofuel; 2) assess how such land use change would affect water quality and quantity in the watersheds. This novel analytical framework will provide decision support for evaluating hydrologic consequences of biofuel feedstock production under various policy and climate scenarios. The outcome from this project will help policymakers, farmers, and the society better understand the environmental benefit/consequences in meeting the EISA of 2007’s goal of advanced biofuel.
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- Remote sensing of gas flaring in Bakken field (UND Seed Grant)
- Drought monitoring and prediction using NOAH land surface model and GRACE satellite observation (USGS)
- Northern Great Plains Center for People and the Environment - Phase IV (NASA)
- Combining scintillometry and eddy covariance to evaluate and validate remotely sensed evapotranspiration rate at various spatial scales (NASA)
- The feasibility of combining NASA satellite data, general circulation models, and hydrologic models to inform decision making for flood mitigation of the Devils Lake Basin of North Dakota (NASA)
- Impact of Subsurface Drainage on Water Availability in the Red River Basin (USDA)
- Northern Great Plains Center for People and the Environment - Phase III (NASA)
- Sustainable Systems for the Northern Great Plains (USDA)
- Development of Atmospheric Correction Algorithm for the UND AgCam Sensor (NASA)
- To Evaluate and Update the Infomart Decision Support Tool for Variable Rate Fertilizer Application (NASA)
- An Infomart for Precision Agriculture: Zoning Maps for Variable Rate Fertilizer Application (NASA/USRA)
- Digital Northern Great Plains (NASA)
- ND View, America View Consortium (USGS)
- Synergy Team Express: Crop and Range Alert System (NASA)
Peer-Reviewed Journal Publications and Book Chapters
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52. Poulin, C., X. Zhang, P. Yang, and Y. Huot, Diel variations of the attenuation, backscattering and absorption coefficients of four phytoplankton species and comparison with spherical, coated spherical and hexahedral particle optical models. Journal of Quantitative Spectroscopy and Radiative Transfer, 2018. 217: 288-304 doi: 10.1016/j.jqsrt.2018.05.035. PDF
51. Burke, M., M. Shahabi, Y. Xu, H. Zheng, X. Zhang, and J. VanLooy, Identifying the Driving Factors of Water Quality in a Sub-Watershed of the Republican River Basin, Kansas USA. International Journal of Environmental Research and Public Health, 2018. 15(5): 1041 doi:10.3390/ijerph15051041.
50. Muller-Karger, F.E., E. Hestir, C. Ade, K. Turpie, D.A. Roberts, D. Siegel, R.J. Miller, D. Humm, N. Izenberg, M. Keller, F. Morgan, R. Frouin, A.G. Dekker, R. Gardner, J. Goodman, B. Schaeffer, B.A. Franz, N. Pahlevan, A.G. Mannino, J.A. Concha, S.G. Ackleson, K.C. Cavanaugh, A. Romanou, M. Tzortziou, E.S. Boss, R. Pavlick, A. Freeman, C.S. Rousseaux, J. Dunne, M.C. Long, E. Klein, G.A. McKinley, J. Goes, R. Letelier, M. Kavanaugh, M. Roffer, A. Bracher, K.R. Arrigo, H. Dierssen, X. Zhang, F.W. Davis, B. Best, R. Guralnick, J. Moisan, H.M. Sosik, R. Kudela, C.B. Mouw, A.H. Barnard, S. Palacios, C. Roesler, E.G. Drakou, W. Appeltans, and W. Jetz, Satellite sensor requirements for monitoring essential biodiversity variables of coastal ecosystems. Ecological Applications, 2018. 28(3): 749-760 doi: 10.1002/eap.1682.
49. Werdell, P.J., L.I.W. McKinna, E. Boss, S.G. Ackleson, S.E. Craig, W.W. Gregg, Z. Lee, S. Maritorena, C.S. Roesler, C.S. Rousseaux, D. Stramski, J.M. Sullivan, M.S. Twardowski, M. Tzortziou, and X. Zhang, An overview of approaches and challenges for retrieving marine inherent optical properties from ocean color remote sensing. Progress in Oceanography, 2018. 160: 186-212 doi: 10.1016/j.pocean.2018.01.001. PDF
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48. He, S., X. Zhang, Y. Xiong, and D. Gray, A Bidirectional Subsurface Remote-Sensing Reflectance Model Explicitly Accounting for Particle Backscattering Shapes. Journal of Geophysical Research: Oceans, 2017. 122 doi: 10.1002/2017JC013313. Matlab code
47. Sun, B., P. Yang, G.W. Kattawar, and X. Zhang, Physical-geometric optics method for large size faceted particles. Optics Express, 2017. 25(20): 24044-24060 doi: 10.1364/OE.25.024044. PDF
46. Xiong, Y., X. Zhang, S. He, and D.J. Gray, Re-examining the effect of particle phase functions on the remote-sensing reflectance. Applied Optics, 2017. 56(24): 6881-6888 doi: 10.1364/AO.56.006881.
45. Shabani, A., X. Zhang, and M. Ell, Modeling Water Quantity and Sulfate Concentrations in the Devils Lake Watershed Using Coupled SWAT and CE-QUAL-W2. Journal of the American Water Resources Association, 2017. 53(4): 748-760 doi: 10.1111/1752-1688.12535.
44. Zhang, X., R.H. Stavn, A.U. Falster, J.J. Rick, D. Gray, and R.W. Gould Jr, Size distributions of coastal ocean suspended particulate inorganic matter: Amorphous silica and clay minerals and their dynamics. Estuarine, Coastal and Shelf Science, 2017. 189: 243-251 doi: 10.1016/j.ecss.2017.03.025. PDF
43. Xu, G., B. Sun, S.D. Brooks, P. Yang, G.W. Kattawar, and X. Zhang, Modeling the inherent optical properties of aquatic particles using an irregular hexahedral ensemble. Journal of Quantitative Spectroscopy and Radiative Transfer, 2017. 191: 30-39 doi: 10.1016/j.jqsrt.2017.01.020. PDF
42. Zhang, X., G.R. Fournier, and D.J. Gray, Interpretation of scattering by oceanic particles around 120 degrees and its implication in ocean color studies. Optics Express, 2017. 25(4): A191-A199 doi: 10.1364/OE.25.00A191. PDF
41. Zhang, X., S. He, A. Shabani, P.-W. Zhai, and K. Du, Spectral sea surface reflectance of skylight. Optics Express, 2017. 25(4): A1-A13 doi: 10.1364/OE.25.0000A1. PDF
40. Molodtsova, T., S. Molodtsov, A. Kirilenko, X. Zhang, and J. VanLooy, Evaluating flood potential with GRACE in the United States. Natural Hazards and Earth System Sciences, 2016. 16(4): 1011-1018 doi: 10.5194/nhess-16-1011-2016. PDF
39. Guo, H., C. Dou, X. Zhang, C. Han, and X. Yue, Earth observation from the manned low Earth orbit platforms. ISPRS Journal of Photogrammetry and Remote Sensing, 2016. 115: 103-118 doi: 10.1016/j.isprsjprs.2015.11.004.
38. Zhang, X. and D.J. Gray, Backscattering by very small particles in coastal waters. Journal of Geophysical Research: Oceans, 2015. 120(10): 6914-6926 doi: 10.1002/2015JC010936.
37. Zhang, X., B. Scheving, B. Shoghli, C. Zygarlicke, and C. Wocken, Quantifying Gas Flaring CH4 Consumption Using VIIRS. Remote Sensing, 2015. 7(8): 9529-9541. PDF
36. Zhang, X., Y. Huot, A. Bricaud, and H.M. Sosik, Inversion of spectral absorption coefficients to infer phytoplankton size classes, chlorophyll concentration, and detrital matter. Applied Optics, 2015. 54(18): 5805-5816.
35. Christensen, M., J. Zhang, J.S. Reid, X. Zhang, E.J. Hyer, and A. Smirnov, A Theoretical Study of the Effect of Oceanic Bubbles on the Enhanced Aerosol Optical Depth Band over High Latitude Southern Oceans as Detected From MODIS and MISR. Atmospheric Measurement Techniques, 2015. 8(5):(2149-2160) doi: 10.5194/amt-8-2149-2015. PDF
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34. Zhang, X., E. Boss, and D.J. Gray, Significance of scattering by oceanic particles at angles around 120 degree. Optics Express, 2014. 22(25): 31329-31336 doi: 10.1364/OE.22.031329. PDF
33. Zhang, X., R. Stavn, A. Falster, and D. Gray, New Insight into Particulate Mineral and Organic Matter in Coastal Ocean Waters through Optical Inversion. Estuarine, Coastal and Shelf Science, 2014. 149:1-12 doi: 10.1016/j.ecss.2014.06.003. PDF
32. Zhang, X., S. Seelan, and J. Nowatzki, Technological innovations bringing spatial technology to precision agriculture in the Northern Great Plains. Technology and Innovation, 2014. 16(1): 27-35. PDF
31. Dou, C., X. Zhang, H. Guo, C. Han, and M. Liu, Improving the Geolocation Algorithm for Sensors Onboard the ISS: Effect of Drift Angle. Remote Sensing, 2014. 6(6): 4647-4659. PDF
30. Zheng, H., D. Barta, and X. Zhang, Lesson learned from adaptation response to Devils Lake flooding in North Dakota, USA. Regional Environmental Change, 2014. 14(1): 185-194 doi: 10.1007/s10113-013-0474-y.
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29. Zhang, X., Y. Huot, D.J. Gray, A. Weidemann, and W.J. Rhea, Biogeochemical origins of particles obtained from the inversion of the volume scattering function and spectral absorption in coastal waters. Biogeosciences, 2013. 10: 6029-6043 doi: 10.5194/bg-10-6029-2013 PDF
28. Proulx, R., M. Knudson, A. Kirilenko, J. VanLooy, and X. Zhang, Characterizing the Terrestrial Water Budget in the Prairie Coteau Using GRACE, NOAH, Landsat, and Groundwater Well Data. Water Resources Research, 2013. 49(9): 5756-5764 doi:10.1002/wrcr.20455
27. Dou, C., X. Zhang, H. Kim, J. Ranganathan, D. Olsen, and H. Guo, Geolocation Algorithm for Earth Observation Sensors onboard International Space Station. Photogrammetric Engineering & Remote Sensing, 2013. 79(7): 625-637
26. Rijal, S., X. Zhang, X. Jia (2013), Estimating surface soil water content in the Red River Valley of the North using Landsat 5 TM data. Soil Science Society of America Journal, 2013. 77(4): 1133-1143 doi: 10.2136/sssaj2012.0295
25. Jia, X., T. Scherer, D. Lin, X. Zhang, and I. Rijal, Comparison of Reference Evapotranspiration Calculations for Southeastern North Dakota. Irrigation & Drainage System Engineering, 2013. 2(3): 112 doi: 10.4172/2168-9768.1000112.
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24. Rijal, I., X. Jia, X. Zhang, D. Steele, T. Scherer, and A. Akyuz, Effects of Subsurface Drainage on Evapotranspiration for Corn and Soybean Crops in Southeastern North Dakota. Journal of Irrigation and Drainage Engineering, 2012. 138(12): 1060-1067 doi: 10.1061/(ASCE)IR.1943-4774.0000508.
23. Lemons, R., A. Hewitt, G. Kharel, C. New, A. Kirilenko, and X. Zhang (2012), Evaluation of satellite-derived agro-climate variables in the Northern Great Plains, Geocarto International, 27(8), 613-626. PDF
22. Zhang, X., Molecular Light Scattering by Pure Sea Water, in Light Scattering Reviews 7, A. Kokhanovsky, Editor. 2013, Springer: Heidelberg. p. 225-243.
21. Zhang, X., D. Gray, Y. Huot, Y. You, and L. Bi, Comparison of optically derived particle size distributions: scattering over the full angular range versus diffraction at near forward angles. Applied Optics, 2012. 51(21), 5085-5099. PDF
20. Twardowski, M., X. Zhang, S. Vagle, J. Sullivan, S. Freeman, H. Czerski, Y. You, L. Bi, and G. Kattawar (2012), The optical volume scattering function in a surf zone inverted to derive sediment and bubble particle subpopulations, J. Geophys. Res., 117, C00H17. PDF
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19. Xiaodong Zhang, Ho Jin Kim, Clinton Streeter, David A. Claypool, Ramesh Sivanpillai & Santhosh Seelan (2011). Near real-time high-resolution airborne camera, AEROCam, for precision agriculture, Geocarto International, 26(7), 537-551. PDF
18. Czerski, H., M. Twardowski, X. Zhang, and S. Vagle (2011), Resolving size distributions of bubbles with radii less than 30 um with optical and acoustical methods, J. Geophys. Res., 116, C00H11, doi:10.1029/2011JC007177. PDF
17. Xiaodong Zhang, Michael Twardowski, and Marlon Lewis (2011), Retrieving composition and sizes of oceanic particle subpopulations from the volume scattering function, Applied Optics, 50(9), 1240-1259. PDF
16. Xiaodong Zhang, Digital Northern Great Plains and Zone Mapping Application for Precision Agriculture, in GIS Applications in Agriculture - Nutrient Management for Energy Efficiency, David Clay & John Shanahan (eds.), CRC Press, 123-133, 2011.
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15. George A. Seielstad, David E. Clay, Kevin Dalsted, Rick L. Lawrence, Douglas R. Olsen, Xiaodong Zhang, Providing Precision Crop and Range Protection in the US Northern Great Plains, in Precision Crop Protection - the Challenge and Use of Heterogeneity, E.-C. Oerke et al. (eds.), Springer, 367-384, 2010. PDF
14. Xiaodong Zhang, Santhosh Seelan, George Seielstad. Web-Based System Delivers Near Real Time Remote Sensing Data. Earth Imaging Journal. 2010, 7(4), 28-30. PDF
13. Xiaodong Zhang, Xinhua Jia, Junyu Yang, and Lianbo Hu, Evaluation of MOST functions and roughness length parameterization on sensible heat flux measured by large aperture scintillometer over a corn field, Agricultural and Forest Meteorology, 2010, 150, 1182-1191. PDF
12. Xiaodong Zhang, Santhosh Seelan, George Seielstad. Digital Northern Great Plains: A Web-Based System Delivering Near Real Time Remote Sensing Data for Precision Agriculture. Remote Sensing. 2010, 2(3), 861-873. Access
11. Xiaodong Zhang, Lijian Shi, Xinhua Jia, George Seielstad, and Craig Helgason, Zone mapping application for precision- farming: a decision support tool for variable rate application, Precision Agriculture, 2010, 11(2), 103-114. Access
10. Doug Olsen, Changyong Dou, Xiaodong Zhang, Lianbo Hu, Hojin Kim, and Edward Hildum, Radiometric calibration for AgCam, Remote Sensing, 2010, 2(2), 464-477. Access.
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5. Xiaodong Zhang, M. Lewis, W.P. Bissett, B. Johnson, and D. Kohler, Optical Influence of Ship Wakes, Applied Optics,2004, 43(15), 3122-3132. PDF
4. Xiaodong Zhang, M. Lewis, M. Lee, B. Johnson, and G. Korotaev, The volume scattering function of natural bubble populations, Limnology and Oceanography, 2002, 47(5), 1273-1282. PDF
3. Xiaodong Zhang, M. Lewis, B. Johnson, Influence of bubbles on scattering of light in the ocean, Applied Optics, 1998, 37(27), 6525-6536. PDF
2. Xiaoodng Zhang, Y. Okada, N. Kimura, H. Fukushima, Y. Senga, Y. Sugimori, and M. He, Comparison of pigment concentration between CZCS-estimation and ship-observation in the waters around Japan: Test of an improved atmospheric correction method, Journal of Advanced Marine Science and Technology Society, 1995, 1(2), 14-25. PDF
1. He, Ming-Xia., C. Zhao, Xiaodong Zhang, Ocean wave directional spectra by optical methods, In Oceanology of China Seas, Zhou Di et al. (Eds.), Vol. 2, Academic, 557-564, 1994. PDF
- 2011 North Dakota Spirit Faculty Achievement Award
- Senior Member of Optical Society of America
- 2010 North Dakota Innovate Idea Champion
PhD - Department of Oceanography, Dalhousie University, Canada (2001)
MS - Department of Oceanography, Dalhousie University, Canada (1998)
BS - Department of Computer Science, Nanjing University, China (1989)
Currently in my group
--- These are the people who I had pleasure of working with before and have either graduated or left
Sergey Gulbin, Dr. Shuanyan He, Jiaxia Wu, Junyu Yang, Santosh Rijal, Tedros Berthane, Kate Overmoe, Michael Michno, Lawrence Burkett, Dr. Lianbo Hu, Dr. Changyong Dou, Lijian Shi, Kalpesh Raicha, Ganesh Pulicherla, Jing Wang, Qian Sha
These programs were developed during the course of my research and I believe they will be useful for a wider community. They are free to use, but I'd appreciate if you can acknowledge the source.
- Parameterized Zaneveld rrs model: The BRDF of rrs is fundamentally dependent on the shape of VSFs. The Zaneveld's theoretical rrs model explicitly accounts for the shape of VSFs. We parameterized the Zaneveld's rrs model based on Hydrolight simulations using 114 VSFs measured in three coastal waters around the US and in oceanic waters of North Atlantic Ocean.
- Scattering of pure water: Instead of using partial derivative of the refractive index with respect to the pressure which can be measured relatively easier, we used partial derivative of index with respect to the density to model the thermal fluctuation of the refractive index. The later can be derived theoretically and the results agreed with Morel's measurements within 2% for a range of values of depolarization coefficient from 0.039 to 0.051.
- Scattering of pure seawater: This is a theoretical analysis of effect of salinity on light scattering by pure seawater. The variation is due to decreasing contribution from density fluctuation and increasing contribution from concentration fluctuation, with the latter effect dominant. The net effect is non-linear, though not significant. The results agree with Morel's measurement within 1%.
- Scattering of pure seawater at high salinity: Again this is a theoretical analysis of effect of salinity on light scattering by pure seawater. It differs from the previous study in two aspects. Gibbs energy function is used to derived all the thermodynamic parameters that are associated with density fluctuation. This will ensure all the inter-relationships amongh different thermodynamic parameters are satisfied. Also the model applies to waters with salinity as high as 120 g/kg. The scattering increases with salinity in a non-linear fashion (bending down at high salinity) and linear extrapolation will overestimate by 30% when salinity is 120 g/kg.
- Scattering by solutions of major sea salts. The salts include: NaCl, MgCl2, MgSO4, Na2SO4, K2SO4, CaCl2 and KCl. Because of ionic interactions among different salts, the molecular scattering does not follow the simple addition rule that applies to bulk inherent optical properties, with the total less than the summation of the parts. Scattering by solutions of NaCl, the major sea salt, is about 6.7% and 4% lower than seawater of the same mass concentration and of the same refractive index, respectively. The model applies to salt solutions with concentration as high as 120 g/kg.
- Mie scattering codes in Matlab for both spherical and coated spherical particles. The code is numerically stable even for large size parameters and uses an analytical formual to calculate the backscattering efficiency.
Devils Lake and its Flooding since 1993: Observation and Modelling
- In addition to the rising water level, water quality is another issue that has to be considered in mitigating the Devils Lake flood impact. The overall salinity level, particularly that of sulfate, are significantly higher than those of the surrounding water bodies of Sheyenne River and Red River, limiting the amount of water from Devils Lake that can be released into the Sheyenne River. Funded by a NASA project studying water quality in the Devils Lake, a buoy is deployed in the Stump Lake to monitor the change of water quality including temperature, salinity, turbidity, dissolved oxygen level, and chlorophyll concentration. The weather parameters of wind speeds and direction and air temperature are also monitored. The results, together with other observation of regional weather and land change, will help us to better understand how the quality of water changes in response to the weather and long term climate changes.
- A short video looking at the flooding issue from the perspectives of global climate change and local residents. This was produced by a group of summer school students taught by my colleague, Dr. Andrei Kirilenko
- A series of Landsat images. David Barta, our graduate student at ESSP, analyzed the Landsat images to delineate the boundary of the rising lake. Craig Helgason and Jane Peterson helped to create the animation.
- Precipitation from NASA TMPA (TRMM Multi-sensor Precipitation Analysis) data. Cumulative daily precipitation for the last decade shows that there are interannual variability with total yearly precipitation varying between 350 mm (14 inch) and 550 mm (22 inch). The precipitation for the year 2010 is very similar to the mean precipitation for the last decade. Devils Lake has been rising steadily since 1993, and the satellite data shows that the pattern and the amount of precipitation remain relatively steady, or in other words, the wet cycle which has been going for the past 17 years shows no sign of change. So far, the precipitation for the year 2011 (up to 06/30) is slightly lower than the average of past 10 years. However, the snow fall in January was higher than the average.
- Surface air temperature from NASA AIRS data. Cumulative growing degree days above freezing gives a measure of total solar heat received for those days with mean temperature above freezing. Typically, snow will not melt until late March or early April; and the temperature drops below freezing during late October or early November. The number of days during which the temperature falls within a range is shown here.
- Monthly and yearly evapotranspiration estimated from MODIS from 2000 to 2010 using the model developed by University of Montana. The data for the years after 2010 will be updated soon. Click on image to download data of a particular month or year.
- More to come