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Informing grassland ecosystem modeling with in-situ and remote sensing observations

ENVIRONMENTAL res.ARCH LETTERS 2025年第3期20卷 034004-034004页

作者： Arteaga, Johny Hartman, Melannie D. Parton, William J. Chen, Maosi Gao, Wei Colorado State Univ USDA UV B Monitoring & Res Program Nat Resource Ecol Lab Ft Collins CO 80521 USA Colorado State Univ Nat Resource Ecol Lab Ft Collins CO 80523 USA Colorado State Univ Dept Ecosyst Sci & Sustainabil Ft Collins CO 80523 USA

We simulated historical grassland aboveground plant productivity (ANPP) across the midwestern and western contiguous United States using the DayCent-uv ecosystem model. For this study we developed new methods for informing DayCent-uv of growing season length and validating its plant productivity estimates for grasslands by utilizing a wide range of data sources at multiple scales, from field observations to remotely sensed satellite data. The model's phenology was informed by the MODIS MCD12Q2 product, which showed good agreement with in-situ observations of growing season commencement and duration across different grassland ecosystems, and with observed historical trends. Model res.lts from each simulated grid cell were compared to a remote-sensing estimate of grassland plant productivity offered by the Rangeland Analysis Platform (RAP). We determined that a modified RAP ANPP calculation that incorporated total annual precipitation instead of mean annual temperature to estimate the fraction of total productivity allocated to roots improved temporal correlations between RAP and field measurements and between RAP and DayCent-uv, We found that RAP provides a valuable data set for evaluating grassland ANPP predictions from ecosystem and other types of models because it provides estimates of grassland plant productivity over large spatial regions and a long historical period and captures.temporal variablilty in plant production. This work provides the foundation for using the DayCent-uv model to predict climate change impacts on grassland cecosystem dynamics in the contiguous US.

关键词： Rangeland Analysis Platform long-term ANPP comparisons DayCent ecosystem modeling

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A dynamical system to model land use interactions by using population dynamics: application on vulnerable grasslands

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PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES 2024年第2301期480卷

作者： Arteaga, Johny Agudelo, Jhonny brazeiro, Alejandro Fort, Hugo Univ Tecnol Uruguay ITR Ctr Sur Durazno 97000 Uruguay Colorado State Univ USDA UV B Monitoring & Res Program Nat Resource Ecol Lab Ft Collins CO 80523 USA Univ Republica Grp Sistemas Complejos & Fis Estadist Inst Fis Igua 4225 Montevideo 11400 Uruguay Univ Republica Fac Ciencias Montevideo Uruguay

We introduce a deterministic set of equations, the Lotka-Volterra (LV) generalized equations, to explain or predict the temporal dynamics and competition between land use and land cover (LULC) classes. We evaluated this new approach in two regions of Uruguay where human activities have reduced natural grasslands. We infer the interaction coefficients from the LV model through the pairwise maximum entropy method using the spatial covariance matrices obtained from annual remote-sensing LULC maps. The model performed well in predicting short-term temporal changes, with errors similar to those obtained using traditional Markov chains. This model introduces a novel avenue for assisting LULC modelling in improving the temporal dynamics.

关键词： land use and land cover pairwise maximum entropy modelling Lotka-Volterra modelling Markov chains Mapbiomas Pampa

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Nonlinear hydrological time series modeling to forecast river level dynamics in the Rio Negro Uruguay basin

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CHAOS 2024年第5期34卷 053132-053132页

作者： Duque, Johan S. Santos, Rafael Arteaga, Johny Oyarzabal, Ricardo S. Santos, Leonardo b. L. Natl Inst Space Res INPE BR-12227010 Sao Jose Dos Campos Brazil Univ Tecnol Uruguay UTEC ITR CS Durazno 97000 Uruguay Colorado State Univ USDA UV B Monitoring & Res Program Nat Resource Ecol Lab Ft Collins CO 80523 USA Natl Ctr Monitoring & Early Warning Nat Disasters BR-12630000 Cemaden Sao Jose Dos Ca Brazil

Floods significantly impact the well-being and development of communities. Hence, understanding their causes and establishing methodologies for risk prevention is a critical challenge for effective warning systems. Complex systems such as hydrological basins are modeled through hydrological models that have been utilized to understand water recharge of aquifers, available volume of dams, and floods in diverse regions. Acquiring real-time hydrometeorological data from basins and rivers is vital for establishing data-driven-based models as tools for the prediction of river-level dynamics and for understanding its nonlinear behavior. This paper introduces a hydrological model based on a multilayer perceptron neural network as a useful tool for time series modeling and forecasting river levels in three stations of the Rio Negro basin in Uruguay. Daily time series of river levels and rainfall serve as the input data for the model. The assessment of the models is based on metrics such as the Nash-Sutcliffe coefficient, the root mean square error, percent bias, and volumetric efficiency. The outputs exhibit varying model performance and accuracy during the prediction period across different sub-basin scales, revealing the neural network's ability to learn river dynamics. Lagged time series analysis demonstrates the potential for chaos in river-level time series over extended time periods, mainly when predicting dam-related scenarios, which shows physical connections between the dynamical system and the data-based model such as the evolution of the system over time.

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Changes in tropospheric air quality related to the protection of stratospheric ozone in a changing climate

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PHOTOCHEMICAL & PHOTObIOLOGICAL SCIENCES 2023年第5期22卷 1129-1176页

作者： Madronich, S. Sulzberger, b. Longstreth, J. D. Schikowski, T. Andersen, M. P. Sulbaek Solomon, K. R. Wilson, S. R. Natl Ctr Atmospher Res Boulder CO 80305 USA Colorado State Univ USDA UV B Monitoring & Res Program Nat Resource Ecol Lab Ft Collins CO 80523 USA Swiss Fed Inst Aquat Sci & Technol CH-8600 Dubendorf Switzerland Inst Global Risk Res LLC Bethesda MD USA IUF Leibniz Res Inst Environm Med Dusseldorf Germany Calif State Univ Dept Chem & Biochem Northridge CA USA Univ Guelph Sch Environm Sci Guelph ON Canada Univ Wollongong Sch Earth Atmospher & Life Sci Wollongong Australia

Ultraviolet (uv) radiation drives the net production of tropospheric ozone (O-3) and a large fraction of particulate matter (PM) including sulfate, nitrate, and secondary organic aerosols. Ground-level O-3 and PM are detrimental to human health, leading to several million premature deaths per year globally, and have adverse effects on plants and the yields of crops. The Montreal Protocol has prevented large increases in uv radiation that would have had major impacts on air quality. Future scenarios in which stratospheric O-3 returns to 1980 values or even exceeds them (the so-called super-recovery) will tend to ameliorate urban ground-level O-3 slightly but worsen it in rural areas. Furthermore, recovery of stratospheric O-3 is expected to increase the amount of O-3 transported into the troposphere by meteorological processes that are sensitive to climate change. uv radiation also generates hydroxyl radicals (OH) that control the amounts of many environmentally important chemicals in the atmosphere including some greenhouse gases, e.g., methane (CH4), and some short-lived ozone-depleting substances (ODSs). Recent modeling studies have shown that the increases in uv radiation associated with the depletion of stratospheric ozone over 1980-2020 have contributed a small increase (similar to 3%) to the globally averaged concentrations of OH. Replacements for ODSs include chemicals that react with OH radicals, hence preventing the transport of these chemicals to the stratosphere. Some of these chemicals, e.g., hydrofluorocarbons that are currently being phased out, and hydrofluoroolefins now used increasingly, decompose into products whose fate in the environment warrants further investigation. One such product, trifluoroacetic acid (TFA), has no obvious pathway of degradation and might accumulate in some water bodies, but is unlikely to cause adverse effects out to 2100. [GRAPHICS] .

关键词： Ozone

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Missing Pixel Reconstruction on Landsat 8 Analysis Ready Data Land Surface Temperature Image Patches Using Source-Augmented Partial Convolution

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REMOTE SENSING 2020年第19期12卷 3143-3143页

作者： Chen, Maosi Sun, Zhibin Newell, benjamin H. Corr, Chelsea A. Gao, Wei Colorado State Univ USDA UV B Monitoring & Res Program Nat Resource Ecol Lab Ft Collins CO 80523 USA Colorado State Univ Geospatial Centroid Ft Collins CO 80523 USA

Missing pixels is a common issue in satellite images. Taking Landsat 8 Analysis Ready Data (ARD) Land Surface Temperature (LST) image as an example, the Source-Augmented Partial Convolution v2 model (SAPC2) is developed to reconstruct missing pixels in the target LST image with the assistance of a collocated complete source image. SAPC2 utilizes the partial convolution enabled U-Net as its framework and accommodates the source into the framework by: (1) performing the shared partial convolution on both the source and the target in encoders;and (2) merging the source and the target by using the partial merge layer to create complete skip connection images for the corres.onding decoders. The optimized SAPC2 shows superior performance to four baseline models (i.e., SAPC1, SAPC2-OPC, SAPC2-SC, and STS-CNN) in terms of nine validation metrics. For example, the masked MSE of SAPC2 is 7%, 20%, 44%, and 59% lower than that of the four baseline models. On the six scrutinized cases, the repaired target images generated by SAPC2 have the fewest artifacts near the mask boundary and the best recovery of color scales and fine textures.compared with the four baseline models.

关键词： source-augmented partial convolution SAPC2 partial merge layer satellite image inpainting landsat 8 analysis ready data (ARD) land surface temperature (LST)

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Ensemble Learning via Higher Order Singular Value Decomposition for Integrating Data and Classifier Fusion in Water Quality monitoring

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH ObSERVATIONS AND REMOTE SENSING 2021年 14卷 3345-3360页

作者： Sun, Zhibin Chang, Ni-bin Chen, Chi-Farn Mostafiz, Chandan Gao, Wei Colorado State Univ Nat Resource Ecol Lab USDA UV B Monitoring & Res Program Ft Collins CO 80523 USA Univ Cent Florida Dept Civil & Environm Engn Orlando FL 32816 USA Natl Cent Univ Ctr Space & Remote Sensing Res Taoyuan 32001 Taiwan Colorado State Univ Dept Ecosyst Sci & Sustainabil Ft Collins CO 80523 USA

Current multimodal learning in smart feature extraction and classification has res.aped the landscape of remote sensing. The recent developments in smart feature extraction mainly rely on different machine learning and data mining algorithms as powerful classifiers to improve prediction accuracy. This article pres.nts an innovative ensemble learning algorithm for integrated data and classifier fusion via higher order singular value decomposition (IDCF-HOSVD). based on the fused data, different analytical, semiempirical, and empirical classifiers can be selected and applied to perform information retrieval that can be further synergized via a tensor flow based feature extraction scheme over the classifier space. When pres.rving core fused image patterns via HOSVD, the final step of IDCF-HOSVD helps rank the contributions from different classifiers via nonlinear correlation information entropy. Practical implementation of the IDCF-HOSVD algorithm was assessed through its application to map the seasonal water quality conditions in Lake Nicaragua, Central America.

关键词： Lakes Feature extraction Water quality Remote sensing monitoring Classification algorithms Spatial res.lution Classifier fusion ensemble learning higher order singular value decomposition (HOSVD) multimodal learning water quality monitoring

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Dashboard for Agricultural Water Use and Nutrient Management-A Predictive Decision Support System to Improve Crop Production in a Changing Climate

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bULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY 2024年第2期105卷 E432-E441页

作者： Liang, Xin-Zhong Gower, Drew Kennedy, Jennifer A. Kenney, Melissa Maddox, Michael C. Gerst, Michael balboa, Guillermo becker, Talon Cai, Ximing Elmore, Roger Gao, Wei He, Yufeng Liang, Kang Lotton, Shane Malayil, Leena Matthews, Megan L. Meadow, Alison M. Neale, Christopher M. U. Newman, Greg Sapkota, Amy Rebecca Shin, Sanghoon Straube, Jonathan Sun, Chao Wu, You Yang, Yun Zhang, Xuesong Univ Maryland Dept Atmospher & Ocean Sci College Pk MD 20740 USA Univ Maryland Earth Syst Sci Interdisciplinary Ctr College Pk MD 20740 USA Univ Maryland Dept Geog Sci College Pk MD USA Univ Minnesota Inst Environm St Paul MN USA Univ Nebraska Dept Agron & Hort Lincoln NE USA Univ Illinois Cooperat Extens Serv Urbana IL USA Univ Illinois Dept Civil & Environm Engn Urbana IL USA Colorado State Univ Dept Ecosyst Sci & Sustainabil Nat Resource Ecol Lab Ft Collins CO USA Colorado State Univ USDA UV B Monitoring & Res Program Nat Resource Ecol Lab Ft Collins CO USA UnCommon Farms Brighton IL USA Univ Maryland Maryland Inst Appl Environm Hlth Sch Publ Hlth College Pk MD USA Univ Arizona Off Societal Impact Tucson AZ USA Univ Nebraska Daugherty Water Food Global Inst Lincoln NE USA Mississippi State Univ Dept Forestry Starkville MS USA USDA Hydrol & Remote Sensing Lab ARS Beltsville MD USA

Climate change pres.nts huge challenges to the already -complex decisions faced by U.S. agricultural producers, as seasonal weather patterns increasingly deviate from historical tendencies. Under usda funding, a transdisciplinary team of res.archers, extension experts, educators, and stakeholders is developing a climate decision support Dashboard for Agricultural Water use and Nutrient management (DAWN) to provide Corn belt farmers with better predictive information. DAWN's goal is to provide credible, usable information to support decisions by creating infrastructure to make subseasonal-to-seasonal forecasts accessible. DAWN uses an integrated approach to 1) engage stakeholders to coproduce a decision support and information delivery system;2) build a coupled modeling system to repres.nt and transfer holistic systems knowledge into effective tools;3) produce reliable forecasts to help stakeholders optimize crop productivity and environmental quality;and 4) integrate res.arch and extension into experiential, transdisciplinary education. This article pres.nts DAWN's framework for integrating climate-agriculture res.arch, extension, and education to bridge science and service. We also pres.nt key challenges to the creation and delivery of decision support, specifically in infrastructure development, coproduction and trust building with stakeholders, product design, effective communication, and moving tools toward use.

关键词： Climate prediction Agriculture Climate services Communications/ decision making Decision support Water res.urces

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Satellite remote sensing of aerosol optical depth: advances, challenges, and perspectives

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CRITICAL REVIEWS IN ENVIRONMENTAL SCIENCE AND TECHNOLOGY 2020年第16期50卷 1640-1725页

作者： Wei, Xiaoli Chang, Ni-bin bai, Kaixu Gao, Wei East China Normal Univ Minist Educ Key Lab Geog Informat Sci Shanghai Peoples R China East China Normal Univ Sch Geog Sci Shanghai Peoples R China Univ Cent Florida Dept Civil Environm & Construct Engn Orlando FL 32816 USA Colorado State Univ USDA UV B Monitoring & Res Program Nat Resource Ecol Lab Ft Collins CO 80523 USA Colorado State Univ Dept Ecosyst Sci & Sustainabil Ft Collins CO 80523 USA

Aerosol optical depth (AOD) is widely recognized as a critical indicator in understanding atmospheric physics and regional air quality because of its capability for quantifying aerosol loading in the atmosphere. Retrieving AOD from space-borne sensors' observations has become the primary technique for monitoring aerosol loading on a large scale. There is currently a renewed interes. in designing new satellite sensors and developing more advanced retrieval algorithms to measure AOD from space in order to better quantify concentrations of particulate matters (PMs) for advanced air quality management, environmental health assessment, and climate change studies. However, retrieving high-res.lution AOD at varying scales is still a challenging task due to the low signal-to-noise ratio in sensing, algorithmic synthesis constraints, downscaling issues, and data gaps res.lting from adverse impacts such as cloud contamination. Current state-of-the-art technologies still do not permit delicate urban-scale environmental health studies based on appropriate AOD-PMs relationships. This review paper provides a holistic view of the major advances in AOD measurements, elucidates the limitations of current AOD products, pres.nts the challenges with res.ect to the derivation of high-res.lution AOD, and highlights perspectives regarding the possible improvements of satellite-based AOD estimation.

关键词： aerosol optical depth particulate matter air quality inverse modeling remote sensing

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Seasonal grassland productivity forecast for the US Great Plains using Grass-Cast

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ECOSPHERE 2020年第11期11卷 1-23页

作者： Hartman, Melannie D. Parton, William J. Derner, Justin D. Schulte, Darin K. Smith, William K. Peck, Dannele E. Day, Ken A. Del Grosso, Stephen J. Lutz, Susan Fuchs, brian A. Chen, Maosi Gao, Wei Colorado State Univ Nat Resource Ecol Lab Ft Collins CO 80523 USA Colorado State Univ Nat Resource Ecol Lab USDA UV B Monitoring & Res Program Ft Collins CO 80523 USA USDA ARS Rangeland Resources & Syst Res Unit Cheyenne WY 82009 USA Univ Arizona Sch Nat Resources & Environm Tucson AZ 85721 USA USDA Northern Plains Climate Hub Ft Collins CO 80526 USA Dept Environm & Sci Climate Variabil Unit Div Sci & Technol Brisbane Qld Australia USDA ARS Soil Management & Sugar Beet Res Unit Ft Collins CO 80526 USA Univ Nebraska Lincoln Natl Drought Mitigat Ctr Lincoln NE 68583 USA Colorado State Univ Dept Ecosyst Sci & Sustainabil Ft Collins CO 80523 USA

Every spring, ranchers in the drought-prone U.S. Great Plains face the same difficult challenge-trying to estimate how much forage will be available for livestock to graze during the upcoming summer grazing season. To reduce this uncertainty in predicting forage availability, we developed an innovative new grassland productivity forecast system, named Grass-Cast, to provide science-informed estimates of growing season aboveground net primary production (ANPP). Grass-Cast uses over 30 yr of historical data including weather and the satellite-derived normalized vegetation difference index (NDVI)-combined with ecosystem modeling and seasonal precipitation forecasts-to predict if rangelands in individual counties are likely to produce below-normal, near-normal, or above-normal amounts of grass biomass (lbs/ac). Grass-Cast also provides a view of rangeland productivity in the broader region, to assist in larger-scale decision-making-such as where forage res.urces for grazing might be more plentiful if a rancher's own region is at risk of drought. Grass-Cast is updated approximately every two weeks from April through July. Each Grass-Cast forecast provides three scenarios of ANPP for the upcoming growing season based on different precipitation outlooks. Near real-time 8-d NDVI can be used to supplement Grass-Cast in predicting cumulative growing season NDVI and ANPP starting in mid-April for the Southern Great Plains and mid-May to early June for the Central and Northern Great Plains. Here, we pres.nt the scientific basis and methods for Grass-Cast along with the county-level production forecasts from 2017 and 2018 for ten states in the U.S. Great Plains. The correlation between early growing season forecasts and the end-of-growing season ANPP estimate is >50% by late May or early June. In a retrospective evaluation, we compared Grass-Cast end-of-growing season ANPP res.lts to an independent dataset and found that the two agreed 69% of the time over a 20-yr period. Althou

关键词： actual evapotranspiration adaptive management DayCent model decision‐ making forecast Grass‐ Cast grassland production Great Plains NDVI rangelands

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Estimating Error Covariance and Correlation Region in uv Irradiance Data Fusion by Combining TOMS-OMI and uvMRP Ground Observations

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2018年第1期56卷 355-370页

作者： Sun, Zhibin Davis, John Gao, Wei Colorado State Univ USDA UV B Monitoring & Res Program Nat Resource Ecol Lab Ft Collins CO 80523 USA Colorado State Univ Dept Ecosyst Sci & Sustainabil USDA UV B Monitoring & Res Program Nat Resource Ecol Lab Ft Collins CO 80523 USA

Surface ultraviolet (uv) observations can be obtained from satellite or ground observations. This paper uses one data fusion technique (similar to Kalman filter) to combine the advantages from both sources of observations, aiming at achieving a better estimate of surface uv. In this paper, new mathematical methods and algorithms were developed to estimate the error covariance and correlation region, which are the most important components in this data fusion technique. This technique was applied to the satellite data from the Total Ozone Mapping Spectrometer (TOMS)-Ozone monitoring Instrument (OMI) combined with ground measurements from uv-b monitoring and res.arch program (uvMRP) within the region of continental U.S. from 2005 to 2015. Numerical experiments showed that the technique is effective, and TOMS-OMI data were improved by combining uvMRP data. In addition, the innovative ensemble-based method is generic and can be applied to other fields for data fusion/assimilation.

关键词： Correlation region data assimilation data fusion error covariance Total Ozone Mapping Spectrometer (TOMS)-Ozone monitoring Instrument (OMI) ultraviolet (uv) irradiance uv-b monitoring and res.arch program (uvMRP)

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