@article{ai data readiness workshop_2024, DOI={10.5281/zenodo.13899184}, journal={Zenodo}, year={2024}, month={Oct} }
 @article{ai data readiness workshop_2024, DOI={10.5281/zenodo.13899183}, journal={Zenodo}, year={2024}, month={Oct} }
 @article{co-creating the future cmip (aogs24 townhall)_2024, DOI={10.5281/zenodo.12557927}, journal={Zenodo}, year={2024}, month={Jun} }
 @article{co-creating the future cmip (aogs24 townhall)_2024, DOI={10.5281/zenodo.12557928}, journal={Zenodo}, year={2024}, month={Jun} }
 @article{rao_redmon_khin_2024, title={Community-Driven Development of Tools to Improve AI-Readiness of the Open Environmental Data}, url={https://doi.org/10.5194/egusphere-egu24-14071}, DOI={10.5194/egusphere-egu24-14071}, abstractNote={As artificial intelligence (AI) and machine learning (ML) gaining broad interests in the Earth and space science community, the demand for AI-ready data can support the development of responsible AI/ML applications with open environmental data. Through a broad community collaboration under Earth Science Information Partners, we have developed an AI-readiness checklist as a community guideline for the development of AI-ready open environmental data. The checklist was initially based on an early draft of an AI-ready matrix developed by the OSTP Open Science Sub-committee but has been modified notably based on feedback from data users and AI/ML practitioners. The current version of the AI-readiness checklist can be used to holistically assess the documentation, quality, access, and pre-processing of a given dataset. The AI-readiness assessment result can be then summarized into a data card that provides human-readable metrics to assist users in determining if the dataset meets the user's need for their AI/ML development. The next milestone of this community-driven effort is to develop a community-driven convention by building on the existing data conventions and standards to fill the data management gap to support AI-ready data management. In this presentation, we will also showcase a collection of AI-ready climate datasets applying the AI-readiness checklist and data card concept to support AI/ML applications in climate sciences. The AI-readiness development process requires active community engagement with data repositories, domain scientists, and AI/ML practitioners to establish a flexible framework to ensure the rapid evolution of AI/ML technologies can be addressed in modern data management.}, author={Rao, Yuhan and Redmon, Rob and Khin, Eric}, year={2024}, month={Mar} }
 @article{making the most of cmip data: access, analysis, and tools_2024, DOI={10.5281/zenodo.12558015}, journal={Zenodo}, year={2024}, month={Jun} }
 @article{making the most of cmip data: access, analysis, and tools_2024, DOI={10.5281/zenodo.12558016}, journal={Zenodo}, year={2024}, month={Jun} }
 @article{nasa tops open science 101 instructor led training_2024, DOI={10.5281/zenodo.12752289}, journal={Zenodo}, year={2024}, month={Jul} }
 @article{nasa tops open science 101 instructor led training_2024, DOI={10.5281/zenodo.12752290}, journal={Zenodo}, year={2024}, month={Jul} }
 @article{elger_kingsley_inadomi_raia_rao_rolf_schuster_shingledecker_2024, title={Preserving open data as a world heritage to secure our future}, url={https://doi.org/10.22541/essoar.172788649.90106607/v1}, DOI={10.22541/essoar.172788649.90106607/v1}, author={Elger, Kirsten and Kingsley, Danny and Inadomi, Beth and Raia, Natalie and Rao, Yuhan and Rolf, Esther and Schuster, Douglas and Shingledecker, Susan}, year={2024}, month={Oct} }
 @article{sustainability in model sharing_2024, DOI={10.5281/zenodo.11109197}, journal={Zenodo}, year={2024}, month={May} }
 @article{sustainability in model sharing_2024, DOI={10.5281/zenodo.11109198}, journal={Zenodo}, year={2024}, month={May} }
 @article{ten simple rules to promote good model-sharing practices_2024, DOI={10.5281/zenodo.12665701}, journal={Open Modeling Foundation}, year={2024}, month={Jul} }
 @article{ten simple rules to promote good model-sharing practices_2024, DOI={10.5281/zenodo.12665700}, journal={Open Modeling Foundation}, year={2024}, month={Jul} }
 @article{ten simple rules to promote good model-sharing practices_2024, DOI={10.5281/zenodo.12943228}, journal={Open Modeling Foundation}, year={2024}, month={Jul} }
 @article{sun_brink_carande_koren_cristea_jorgenson_janga_asamani_achan_mahoney_et al._2024, title={Towards practical artificial intelligence in Earth sciences}, volume={9}, ISSN={["1573-1499"]}, DOI={10.1007/s10596-024-10317-7}, abstractNote={Abstract Although Artificial Intelligence (AI) projects are common and desired by many institutions and research teams, there are still relatively few success stories of AI in practical use for the Earth science community. Many AI practitioners in Earth science are trapped in the prototyping stage and their results have not yet been adopted by users. Many scientists are still hesitating to use AI in their research routine. This paper aims to capture the landscape of AI-powered geospatial data sciences by discussing the current and upcoming needs of the Earth and environmental community, such as what practical AI should look like, how to realize practical AI based on the current technical and data restrictions, and the expected outcome of AI projects and their long-term benefits and problems. This paper also discusses unavoidable changes in the near future concerning AI, such as the fast evolution of AI foundation models and AI laws, and how the Earth and environmental community should adapt to these changes. This paper provides an important reference to the geospatial data science community to adjust their research road maps, find best practices, boost the FAIRness (Findable, Accessible, Interoperable, and Reusable) aspects of AI research, and reasonably allocate human and computational resources to increase the practicality and efficiency of Earth AI research.}, journal={COMPUTATIONAL GEOSCIENCES}, author={Sun, Ziheng and Brink, Talya and Carande, Wendy and Koren, Gerbrand and Cristea, Nicoleta and Jorgenson, Corin and Janga, Bhargavi and Asamani, Gokul Prathin and Achan, Sanjana and Mahoney, Mike and et al.}, year={2024}, month={Sep} }
 @book{rao_redmon_dale_haupt_hopkinson_bostrom_boukabara_geenen_hall_smith_et al._2023, title={Developing Digital Twins for Earth Systems: Purpose, Requisites, and Benefits}, url={https://arxiv.org/abs/2306.11175}, DOI={10.48550/ARXIV.2306.11175}, abstractNote={The accelerated change in our planet due to human activities has led to grand societal challenges including health crises, intensified extreme weather events, food security, environmental injustice, etc. Digital twin systems combined with emerging technologies such as artificial intelligence and edge computing provide opportunities to support planning and decision-making to address these challenges. Digital twins for Earth systems (DT4ESs) are defined as the digital representation of the complex integrated Earth system including both natural processes and human activities. They have the potential to enable a diverse range of users to explore what-if scenarios across spatial and temporal scales to improve our understanding, prediction, mitigation, and adaptation to grand societal challenges. The 4th NOAA AI Workshop convened around 100 members who are developing or interested in participating in the development of DT4ES to discuss a shared community vision and path forward on fostering a future ecosystem of interoperable DT4ES. This paper summarizes the workshop discussions around DT4ES. We first defined the foundational features of a viable digital twins for Earth system that can be used to guide the development of various use cases of DT4ES. Finally, we made practical recommendations for the community on different aspects of collaboration in order to enable a future ecosystem of interoperable DT4ES, including equity-centered use case development, community-driven investigation of interoperability for DT4ES, trust-oriented co-development, and developing a community of practice.}, institution={arXiv}, author={Rao, Yuhan and Redmon, Rob and Dale, Kirstine and Haupt, Sue E. and Hopkinson, Aaron and Bostrom, Ann and Boukabara, Sid and Geenen, Thomas and Hall, David M. and Smith, Benjamin D. and et al.}, year={2023}, month={Jun} }
 @article{hanson_stall_cutcher-gershenfeld_vrouwenvelder_wirz_rao_peng_2023, title={Garbage in, garbage out: mitigating risks and maximizing benefits of AI in research}, volume={623}, ISSN={["1476-4687"]}, url={https://doi.org/10.1038/d41586-023-03316-8}, DOI={10.1038/d41586-023-03316-8}, abstractNote={Artificial-intelligence tools are transforming data-driven science — better ethical standards and more robust data curation are needed to fuel the boom and prevent a bust. Artificial-intelligence tools are transforming data-driven science — better ethical standards and more robust data curation are needed to fuel the boom and prevent a bust.}, number={7985}, journal={NATURE}, author={Hanson, Brooks and Stall, Shelley and Cutcher-Gershenfeld, Joel and Vrouwenvelder, Kristina and Wirz, Christopher and Rao, Yuhan and Peng, Ge}, year={2023}, month={Nov}, pages={28–31} }
 @article{nasa tops open science 101_2023, DOI={10.5281/zenodo.10161527}, journal={Zenodo}, year={2023}, month={Dec} }
 @article{nasa tops open science 101_2023, DOI={10.5281/zenodo.10161526}, journal={Zenodo}, year={2023}, month={Dec} }
 @misc{almarzouq_azevedo_batalha_bayer_bell_bhogal_black_brown_campitelli_chegini_et al._2023, title={Opensciency - A core open science curriculum by and for the research community}, url={https://zenodo.org/record/7662732}, DOI={10.5281/ZENODO.7662732}, journal={Zenodo}, publisher={Zenodo}, author={Almarzouq, Batool and Azevedo, Flavio and Batalha, Natasha and Bayer, Johanna and Bell, Tomo and Bhogal, Saranjeet and Black, Melissa and Brown, Sierra and Campitelli, Elio and Chegini, Taher and et al.}, year={2023}, month={Feb} }
 @article{wertis_sugg_runkle_rao_2023, title={Socio-Environmental Determinants of Mental and Behavioral Disorders in Youth: A Machine Learning Approach}, volume={7}, ISSN={["2471-1403"]}, url={http://dx.doi.org/10.1029/2023gh000839}, DOI={10.1029/2023gh000839}, abstractNote={AbstractGrowing evidence indicates that extreme environmental conditions in summer months have an adverse impact on mental and behavioral disorders (MBD), but there is limited research looking at youth populations. The objective of this study was to apply machine learning approaches to identify key variables that predict MBD‐related emergency room (ER) visits in youths in select North Carolina cities among adolescent populations. Daily MBD‐related ER visits, which totaled over 42,000 records, were paired with daily environmental conditions, as well as sociodemographic variables to determine if certain conditions lead to higher vulnerability to exacerbated mental health disorders. Four machine learning models (i.e., generalized linear model, generalized additive model, extreme gradient boosting, random forest) were used to assess the predictive performance of multiple environmental and sociodemographic variables on MBD‐related ER visits for all cities. The best‐performing machine learning model was then applied to each of the six individual cities. As a subanalysis, a distributed lag nonlinear model was used to confirm results. In the all cities scenario, sociodemographic variables contributed the greatest to the overall MBD prediction. In the individual cities scenario, four cities had a 24‐hr difference in the maximum temperature, and two of the cities had a 24‐hr difference in the minimum temperature, maximum temperature, or Normalized Difference Vegetation Index as a leading predictor of MBD ER visits. Results can inform the use of machine learning models for predicting MBD during high‐temperature events and identify variables that affect youth MBD responses during these events.}, number={9}, journal={GEOHEALTH}, publisher={American Geophysical Union (AGU)}, author={Wertis, Luke and Sugg, Margaret M. and Runkle, Jennifer D. and Rao, Douglas}, year={2023}, month={Sep} }
 @article{mcgovern_gagne_wirz_ebert-uphoff_bostrom_rao_schumacher_flora_chase_mamalakis_et al._2023, title={Trustworthy Artificial Intelligence for Environmental Sciences An Innovative Approach for Summer School}, volume={104}, ISSN={["1520-0477"]}, DOI={10.1175/BAMS-D-22-0225.1}, abstractNote={Abstract
Many of our generation’s most pressing environmental science problems are wicked problems, which means they cannot be cleanly isolated and solved with a single “correct” answer. The NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography (AI2ES) seeks to address such problems by developing synergistic approaches with a team of scientists from three disciplines: environmental science (including atmospheric, ocean, and other physical sciences), artificial intelligence (AI), and social science including risk communication. As part of our work, we developed a novel approach to summer school, held from 27 to 30 June 2022. The goal of this summer school was to teach a new generation of environmental scientists how to cross disciplines and develop approaches that integrate all three disciplinary perspectives and approaches in order to solve environmental science problems. In addition to a lecture series that focused on the synthesis of AI, environmental science, and risk communication, this year’s summer school included a unique “trust-a-thon” component where participants gained hands-on experience applying both risk communication and explainable AI techniques to pretrained machine learning models. We had 677 participants from 63 countries register and attend online. Lecture topics included trust and trustworthiness (day 1), explainability and interpretability (day 2), data and workflows (day 3), and uncertainty quantification (day 4). For the trust-a-thon, we developed challenge problems for three different application domains: 1) severe storms, 2) tropical cyclones, and 3) space weather. Each domain had associated user persona to guide user-centered development.}, number={6}, journal={BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY}, author={McGovern, Amy and Gagne, David John and Wirz, Christopher D. and Ebert-Uphoff, Imme and Bostrom, Ann and Rao, Yuhan and Schumacher, Andrea and Flora, Montgomery and Chase, Randy and Mamalakis, Antonios and et al.}, year={2023}, month={Jun}, pages={E1222–E1231} }
 @misc{sun_sandoval_crystal-ornelas_mousavi_wang_lin_cristea_tong_carande_ma_et al._2022, title={A review of Earth Artificial Intelligence}, volume={159}, ISSN={["1873-7803"]}, DOI={10.1016/j.cageo.2022.105034}, abstractNote={In recent years, Earth system sciences are urgently calling for innovation on improving accuracy, enhancing model intelligence level, scaling up operation, and reducing costs in many subdomains amid the exponentially accumulated datasets and the promising artificial intelligence (AI) revolution in computer science. This paper presents work led by the NASA Earth Science Data Systems Working Groups and ESIP machine learning cluster to give a comprehensive overview of AI in Earth sciences. It holistically introduces the current status, technology, use cases, challenges, and opportunities, and provides all the levels of AI practitioners in geosciences with an overall big picture and to “blow away the fog to get a clearer vision” about the future development of Earth AI. The paper covers all the majorspheres in the Earth system and investigates representative AI research in each domain. Widely used AI algorithms and computing cyberinfrastructure are briefly introduced. The mandatory steps in a typical workflow of specializing AI to solve Earth scientific problems are decomposed and analyzed. Eventually, it concludes with the grand challenges and reveals the opportunities to give some guidance and pre-warnings on allocating resources wisely to achieve the ambitious Earth AI goals in the future.}, journal={COMPUTERS & GEOSCIENCES}, author={Sun, Ziheng and Sandoval, Laura and Crystal-Ornelas, Robert and Mousavi, S. Mostafa and Wang, Jinbo and Lin, Cindy and Cristea, Nicoleta and Tong, Daniel and Carande, Wendy Hawley and Ma, Xiaogang and et al.}, year={2022}, month={Feb} }
 @article{jain_mindlin_koren_gulizia_steadman_langendijk_osman_abid_rao_rabanal_2022, title={Are We at Risk of Losing the Current Generation of Climate Researchers to Data Science?}, volume={3}, ISSN={["2576-604X"]}, url={https://doi.org/10.1029/2022AV000676}, DOI={10.1029/2022AV000676}, abstractNote={AbstractClimate model output has progressively increased in size over the past decades and is expected to continue to rise in the future. Consequently, the research time expended by Early Career Researchers (ECRs) on data‐intensive activities is displacing the time spent in fostering novel scientific ideas and expanding the frontiers of climate sciences. Here, we highlight an urgent need for a better balance between data‐intensive and foundational climate science activities, more open‐ended research opportunities that reinforce the scientific freedom of the ECRs, and strong coordinated action to provide infrastructure and resources to the ECRs working in under‐resourced environments.}, number={4}, journal={AGU ADVANCES}, author={Jain, Shipra and Mindlin, Julia and Koren, Gerbrand and Gulizia, Carla and Steadman, Claudia and Langendijk, Gaby S. and Osman, Marisol and Abid, Muhammad A. and Rao, Yuhan and Rabanal, Valentina}, year={2022}, month={Aug} }
 @article{dong_chen_chen_yin_zhang_xu_rao_shen_chen_stein_2022, title={Bias of area counted from sub-pixel map: Origin and correction}, volume={6}, ISSN={["2666-0172"]}, DOI={10.1016/j.srs.2022.100069}, abstractNote={With the increasingly widespread use of sub-pixel mapping techniques in land cover/use mapping, more accurate area information is often required for a specific land cover type in a particular study region. However, the bias of area counted from sub-pixel maps (called area bias below), and the inadequate understanding of the area bias's origin and influential factors pose a challenge to using this information accurately. Traditional model-assisted estimators combining the map and the reference sample showed unreliable performances in the case of small sample sizes collected in target regions. This work presented a theoretical analysis of the origin of area bias. It then proposed a novel bias-adjusted estimator which can effectively deal with the small sample sizes. The theoretical analysis illustrated that area bias mainly originates from two terms, i.e., the abundance-dependent error and the probability distribution of abundances. We next developed a stratified bias-adjusted area estimator named the two-term method (TTM) by incorporating the sub-pixel map and a reference sample obtained from both target and external regions. We validated the effects of different sub-pixel mapping methods, different spatial resolutions, the varying spatial structures of statistical units on area bias, and the performance of TTM in correcting the biased areas in multiple cases. The results showed that area bias varied from zero to approximately 20% with the variation of three influential factors. TTM effectively corrected the biased area values to nearly the true values, showing approximate equivalence with the traditional stratified regression estimator (STRE) when adequate reference samples are collected sorely inside target regions. However, in cases of small samples from target regions, TTM showed significant superiority over STRE in reducing the variance and MSE due to the incorporation of external reference samples. We conclude that the theoretical analysis resulted in a better understanding of area bias counted from sub-pixel maps and an improved area estimator for dealing with the cases of small sample sizes inside target regions.}, journal={SCIENCE OF REMOTE SENSING}, author={Dong, Qi and Chen, Xuehong and Chen, Jin and Yin, Dameng and Zhang, Chishan and Xu, Fei and Rao, Yuhan and Shen, Miaogen and Chen, Yang and Stein, Alfred}, year={2022}, month={Dec} }
 @article{watson-parris_rao_olivie_seland_nowack_camps-valls_stier_bouabid_dewey_fons_et al._2022, title={ClimateBench v1.0: A Benchmark for Data-Driven Climate Projections}, volume={14}, ISSN={["1942-2466"]}, url={https://doi.org/10.1029/2021MS002954}, DOI={10.1029/2021MS002954}, abstractNote={AbstractMany different emission pathways exist that are compatible with the Paris climate agreement, and many more are possible that miss that target. While some of the most complex Earth System Models have simulated a small selection of Shared Socioeconomic Pathways, it is impractical to use these expensive models to fully explore the space of possibilities. Such explorations therefore mostly rely on one‐dimensional impulse response models, or simple pattern scaling approaches to approximate the physical climate response to a given scenario. Here we present ClimateBench—the first benchmarking framework based on a suite of Coupled Model Intercomparison Project, AerChemMIP and Detection‐Attribution Model Intercomparison Project simulations performed by a full complexity Earth System Model, and a set of baseline machine learning models that emulate its response to a variety of forcers. These emulators can predict annual mean global distributions of temperature, diurnal temperature range and precipitation (including extreme precipitation) given a wide range of emissions and concentrations of carbon dioxide, methane and aerosols, allowing them to efficiently probe previously unexplored scenarios. We discuss the accuracy and interpretability of these emulators and consider their robustness to physical constraints such as total energy conservation. Future opportunities incorporating such physical constraints directly in the machine learning models and using the emulators for detection and attribution studies are also discussed. This opens a wide range of opportunities to improve prediction, robustness and mathematical tractability. We hope that by laying out the principles of climate model emulation with clear examples and metrics we encourage engagement from statisticians and machine learning specialists keen to tackle this important and demanding challenge.}, number={10}, journal={JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS}, author={Watson-Parris, D. and Rao, Y. and Olivie, D. and Seland, O. and Nowack, P. and Camps-Valls, G. and Stier, P. and Bouabid, S. and Dewey, M. and Fons, E. and et al.}, year={2022}, month={Oct} }
 @article{hills_damerow_ahmmed_catolico_chakraborty_coward_crystal-ornelas_duncan_goparaju_lin_et al._2022, title={Earth and Space Science Informatics Perspectives on Integrated, Coordinated, Open, Networked (ICON) Science}, volume={9}, ISSN={["2333-5084"]}, DOI={10.1029/2021EA002108}, abstractNote={AbstractThis article is composed of three independent commentaries about the state of Integrated, Coordinated, Open, Networked (ICON) principles (Goldman, et al., 2021b, https://doi.org/10.1029/2021EO153180) in Earth and Space Science Informatics (ESSI) and includes discussion on the opportunities and challenges of adopting them. Each commentary focuses on a different topic: (Section 2) Global collaboration, cyberinfrastructure, and data sharing; (Section 3) Machine learning for multiscale modeling; (Section 4) Aerial and satellite remote sensing for advancing Earth system model development by integrating field and ancillary data. ESSI addresses data management practices, computation and analysis, and hardware and software infrastructure. Our role in ICON science therefore involves collaborative work to assess, design, implement, and promote practices and tools that enable effective data management, discovery, integration, and reuse for interdisciplinary work in Earth and space science disciplines. Networks of diverse people with expertise across Earth, space, and data science disciplines are essential for efficient and ethical exchanges of findable, accessible, interoperable, and reusable (FAIR) research products and practices. Our challenge is then to coordinate the development of standards, curation practices, and tools that enable integrating and reusing multiple data types, software, multi‐scale models, and machine learning approaches across disciplines in a way that is as open and/or FAIR as ethically possible. This is a major endeavor that could greatly increase the pace and potential of interdisciplinary scientific discovery.}, number={4}, journal={EARTH AND SPACE SCIENCE}, author={Hills, D. J. and Damerow, J. E. and Ahmmed, B. and Catolico, N. and Chakraborty, S. and Coward, C. M. and Crystal-Ornelas, R. and Duncan, W. D. and Goparaju, L. N. and Lin, C. and et al.}, year={2022}, month={Apr} }
 @article{opensciency - a core open science curriculum by and for the research community_2022, DOI={10.34734/fzj-2024-04045}, journal={Forschungzentrum Jülich}, year={2022} }
 @misc{mcgovern_gagne_ebert-uphoff_bostrom_wirz_rao_schumacher_flora_cains_chase_et al._2022, title={Trustworthy Artificial Intelligence for Environmental Science (TAI4ES) Summer School 2022}, url={https://zenodo.org/record/6784187}, DOI={10.5281/ZENODO.6784187}, publisher={Zenodo}, author={McGovern, Amy and Gagne, David and Ebert-Uphoff, Imme and Bostrom, Ann and Wirz, Christopher and Rao, Douglas and Schumacher, Andrea and Flora, Montgomery and Cains, Mariana and Chase, Randy and et al.}, year={2022}, month={Jun} }
 @article{jiang_shen_ciais_campioli_penuelas_korner_cao_piao_liu_wang_et al._2022, title={Warming does not delay the start of autumnal leaf coloration but slows its progress rate}, volume={8}, ISSN={["1466-8238"]}, DOI={10.1111/geb.13581}, abstractNote={AbstractAimInitiation of autumnal leaf senescence is crucial for plant overwintering and ecosystem dynamics. Previous studies have focused on the advanced stages of autumnal leaf senescence and reported that climatic warming delayed senescence, despite the fundamental differences among the stages of senescence. However, the timing of onset of leaf coloration (DLCO), the earliest visual sign of senescence, has rarely been studied. Here, we assessed the response of DLCO to temperature.Location30–75° N in the Northern Hemisphere.Time period2000–2018.Major taxa studiedDeciduous vegetation.MethodsWe retrieved DLCO from high‐temporal‐resolution satellite data, which were then validated by PhenoCam observations. We investigated the temporal changes in DLCO and the relationship between DLCO and temperature by using satellite and ground observations.ResultsDLCO was not significantly (p > .05) delayed between 2000 and 2018 in 94% of the area. DLCO was positively (p < .05) correlated with pre‐DLCO mean daily minimum temperature (Tmin) in only 9% of the area, whereas the end of leaf coloration (DLCE) was positively correlated with pre‐DLCE mean Tmin over a larger area (34%). Further analyses showed that warming slowed the progress of leaf coloration. Interestingly, DLCO was less responsive to pre‐DLCO mean Tmin in areas where daylength was longer across the Northern Hemisphere, particularly for woody vegetation.Main conclusionsThe rate of progress of coloration is more sensitive to temperature than its start date, resulting in an extension of the duration of leaf senescence under warming. The dependence of DLCO response to temperature on daylength indicates stronger photoperiodic control on initiation of leaf senescence in areas with longer daylength (i.e., shorter nights), possibly because plants respond to the length of uninterrupted darkness rather than daylength. This study indicates that the onset of leaf coloration was not responsive to climate warming and provides observational evidence of photoperiod control of autumnal leaf senescence at biome and continental scales.}, journal={GLOBAL ECOLOGY AND BIOGEOGRAPHY}, author={Jiang, Nan and Shen, Miaogen and Ciais, Philippe and Campioli, Matteo and Penuelas, Josep and Korner, Christian and Cao, Ruyin and Piao, Shilong and Liu, Licong and Wang, Shiping and et al.}, year={2022}, month={Aug} }
 @book{voisin_bennett_fang_nearing_nijssen_rao_2021, title={A science paradigm shift is needed for Earth and Environmental Systems Sciences (EESS) to integrate Knowledge-Guided Artificial Intelligence (KGAI) and lead new EESS-KGAI theories}, url={http://dx.doi.org/10.2172/1769651}, DOI={10.2172/1769651}, abstractNote={A science paradigm shift is needed for Earth and Environmental Systems Sciences (EESS) to integrate Knowledge-Guided Artificial Intelligence (KGAI) and lead new EESS-KGAI theories Nathalie Voisin, Andrew Bennett, Yilin Fang, Grey Nearing, Bart Nijssen, Yuhan Rao 1.Pacific Northwest National Laboratory, 2. University of Washington; 3. University of California Davis & Google Fellow; 4. North Carolina State University}, institution={Office of Scientific and Technical Information (OSTI)}, author={Voisin, Nathalie and Bennett, Andrew and Fang, Yilin and Nearing, Grey and Nijssen, Bart and Rao, Yuhan}, year={2021}, month={Apr} }
 @article{wang_rao_chen_liu_wang_2021, title={Adopting "Difference-in-Differences" Method to Monitor Crop Response to Agrometeorological Hazards with Satellite Data: A Case Study of Dry-Hot Wind}, volume={13}, ISSN={["2072-4292"]}, DOI={10.3390/rs13030482}, abstractNote={Rapid changing climate has increased the risk of natural hazards and threatened global and regional food security. Near real-time monitoring of crop response to agrometeorological hazards is fundamental to ensuring national and global food security. However, quantifying crop responses to a specific hazard in the natural environment is still quite challenging, especially over large areas, due to the lack of tools to separate the independent impact of the hazard on crops from other confounding factors. In this study, we present a general difference-in-differences (DID) framework to monitor crop response to agrometeorological hazards at near real-time using widely accessible remotely sensed vegetation indices (VIs). To demonstrate the effectiveness of the DID framework, we applied it in quantifying the dry-hot wind impact on winter wheat in northern China as a case study using the VIs calculated from the MODIS data. The monitoring results for three years with varying severity levels of dry-hot events (i.e., 2007, 2013, and 2014) demonstrated that the framework can effectively detect winter wheat growing areas affected by dry-hot wind hazards. The estimated damage shows a notable relationship (R2 = 0.903, p < 0.001) with the dry-hot wind intensity calculated from meteorological data, suggesting the effectiveness of the method when field data on a large scale is not available for direct validation. The main advantage of this method is that it can effectively isolate the impact of a specific hazard (i.e., dry-hot wind in the case study) from the mixed signals caused by other confounding factors. This general DID framework is very flexible and can be easily extended to other natural hazards and crop types with proper adjustment. Not only can this framework improve the crop yield forecast but also it can provide near real-time assessment for farmers to adapt their farming practice to mitigate impacts of agricultural hazards.}, number={3}, journal={REMOTE SENSING}, author={Wang, Shuai and Rao, Yuhan and Chen, Jin and Liu, Licong and Wang, Wenqing}, year={2021}, month={Feb} }
 @article{shen_jiang_peng_rao_huang_fu_yang_zhu_cao_chen_et al._2020, title={Can changes in autumn phenology facilitate earlier green-up date of northern vegetation?}, volume={291}, ISSN={["1873-2240"]}, DOI={10.1016/j.agrformet.2020.108077}, abstractNote={Climate warming has induced substantial advances in the onset of vegetation green-up in the northern hemisphere during recent decades. To date, however, the temporal changes in green-up date have not been adequately explained by the statistical relationships between green-up date and climatic factors, posing challenges in the attribution and prediction of phenological change. In this study, we thus turned to focus on autumn phenology, a critical biotic factor that is likely to affect the subsequent spring phenology of vegetation. Using satellite-retrieved start and end of growing season (SOS and EOS) over the period from 1982 to 2015, we examined the association between the EOS and the SOS in the following year in northern middle and high latitudes (north of 25°N). Interannual changes in SOS were significantly (P < 0.05) related to changes in EOS in the previous year in 26.4% of the total pixels, mostly in the boreal region, with a 1-day advance of EOS generally resulting in about a 0.5- to 1.0-day advance of the following SOS, suggesting that the advanced SOS may be associated with the advanced EOS. In temperate ecosystems, however, SOS showed a weak negative partial correlation with previous year's EOS (significant for 10.3% of the total pixels), suggesting that the delayed EOS may have limited contribution to the advanced SOS. Our analysis further revealed that changes in the EOS contributed little to the changes in the number of subsequent chilling days in temperate ecosystems and that the sum of forcing temperatures was weakly related with the number of the chilling days in the boreal region, suggesting that EOS may affect SOS through other mechanisms such as changes in the timing when the chilling requirement is met as well as in carbohydrate and nutrient economy. This study suggested that the timing of EOS may explain some of the temporal changes in SOS in the following year in 36.7% of the study region, but further studies are needed to identify the exact mechanisms.}, journal={AGRICULTURAL AND FOREST METEOROLOGY}, author={Shen, Miaogen and Jiang, Nan and Peng, Dailiang and Rao, Yuhan and Huang, Yan and Fu, Yongshuo H. and Yang, Wei and Zhu, Xiaolin and Cao, Ruyin and Chen, Xuehong and et al.}, year={2020}, month={Sep} }
 @article{wang_chen_rao_liu_wang_dong_2020, title={Response of winter wheat to spring frost from a remote sensing perspective: Damage estimation and influential factors}, volume={168}, ISSN={["1872-8235"]}, url={https://doi.org/10.1016/j.isprsjprs.2020.08.014}, DOI={10.1016/j.isprsjprs.2020.08.014}, abstractNote={Spring frost is one of the major weather-related threats to winter wheat. The damage to winter wheat caused by spring frost is aggravated by the increase in extreme weather events and the advance of spring phenology driven by a warming climate. Until recently, studies of frost damage were primarily based on controlled field experiments and crop model simulations, which cannot accurately represent the real frost damage suffered by winter wheat in the natural environment. In this study, a remote sensing-based spring frost damage index (SFDI) was proposed to rapidly and effectively quantify the impact of spring frost on winter wheat at the provincial scale. Compared with the existing methods, the SFDI is easy to implement with widely available remotely sensed vegetation index (VI) time-series data. It can be used to assess spring frost damage to winter wheat in near real-time to allow a rapid response. Although the SFDI was developed for winter wheat and spring frost, it has the potential to be extended to other agricultural hazards and crop types through careful adjustments to the design. We assessed the performance of SFDI using a spring frost event that occurred from April 3–7, 2018, in North China as a case study. The results showed that the severely damaged areas were mainly located at the junction of Hebei, Henan, and Shandong provinces, especially in western Shandong Province. The result showed good agreement with the proxy data retrieved from the national archives of regional newspaper reports about the event. The validity of the new index (SFDI) was also verified against the reduction in county-level crop production. Additionally, we used multivariate linear regression (MLR) and geographically weighted regression (GWR) to identify the key factors affecting the spatial variation in SFDI. The results indicated that the growth status of winter wheat before spring frost and the amount of precipitation during the frost event were the two major factors affecting the severity of frost damage to winter wheat, followed by the accumulated frost degree-days and soil moisture. This suggests that proper management of the crop growth rate after winter wheat greening and adequate soil moisture (from irrigation and precipitation) before and during the spring frost period could greatly alleviate the damage of spring frost to winter wheat.}, journal={ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING}, publisher={Elsevier BV}, author={Wang, Shuai and Chen, Jin and Rao, Yuhan and Liu, Licong and Wang, Wenqing and Dong, Qi}, year={2020}, month={Oct}, pages={221–235} }
 @article{runkle_sugg_leeper_rao_matthews_rennie_2020, title={Short-term effects of specific humidity and temperature on COVID-19 morbidity in select US cities}, volume={740}, ISSN={["1879-1026"]}, url={https://doi.org/10.1016/j.scitotenv.2020.140093}, DOI={10.1016/j.scitotenv.2020.140093}, abstractNote={Little is known about the environmental conditions that drive the spatiotemporal patterns of SARS-CoV-2. Preliminary research suggests an association with meteorological parameters. However, the relationship with temperature and humidity is not yet apparent for COVID-19 cases in US cities first impacted. The objective of this study is to evaluate the association between COVID-19 cases and meteorological parameters in select US cities. A case-crossover design with a distributed lag nonlinear model was used to evaluate the contribution of ambient temperature and specific humidity on COVID-19 cases in select US cities. The case-crossover examines each COVID case as its own control at different time periods (before and after transmission occurred). We modeled the effect of temperature and humidity on COVID-19 transmission using a lag period of 7 days. A subset of 8 cities were evaluated for the relationship with meteorological parameters and 5 cities were evaluated in detail. Short-term exposure to humidity was positively associated with COVID-19 transmission in 4 cities. The associations were small with 3 out of 4 cities exhibiting higher COVID19 transmission with specific humidity that ranged from 6 to 9 g/kg. Our results suggest that weather should be considered in infectious disease modeling efforts. Future work is needed over a longer time period and across different locations to clearly establish the weather-COVID19 relationship.}, journal={SCIENCE OF THE TOTAL ENVIRONMENT}, publisher={Elsevier BV}, author={Runkle, Jennifer D. and Sugg, Margaret M. and Leeper, Ronald D. and Rao, Yuhan and Matthews, Jessica L. and Rennie, Jared J.}, year={2020}, month={Oct} }
 @article{liu_yu_yu_wang_rao_2019, title={Enterprise LST Algorithm Development and Its Evaluation with NOAA 20 Data}, volume={11}, url={https://doi.org/10.3390/rs11172003}, DOI={10.3390/rs11172003}, abstractNote={Satellite land surface temperatures (LSTs) have been routinely produced for decades from a variety of polar-orbiting and geostationary satellites, which makes it possible to generate LST climate data globally. However, consistency of the satellite LSTs from different satellite missions is a concern for such purpose; an enterprise satellite LST algorithm is desired for the LST production through different satellite missions, or at the least, through series satellites of a satellite mission. The enterprise LST algorithm employs the split window technique and uses the emissivity explicitly in its formula. This research focuses on the enterprise LST algorithm design, development and its evaluations with the National Oceanic and Atmospheric Administration’s (NOAA) 20 (N20) Visible Infrared Imaging Radiometer Suite (VIIRS) data available since 5 January 2018. In this study, the enterprise LST algorithm was evaluated using simulation dataset consisting of over 2000 profiles from SeeBor collection and the results show a bias of 0.19 K and 0.34 K and standard deviation of 0.48 K and 0.69 K for nighttime and daytime, respectively. The in situ observations from seven NOAA Surface Radiation budget (SURFRAD) sites and two Baseline Surface Radiation Network (BSRN) sites were used for LST validation. The results indicate a bias of −0.3 K and a root mean square error (RMSE) of 2.06 K for SURFRAD stations and a bias of 0.2 K and a RMSE of ~2 K for BSRN sites. Further, the cross-satellite analysis presents a bias of 0.7 K and an RMSE of 1.9 K for comparisons with AQUA MODIS LST (MYD11_L2, Collection 6). The enterprise N20 VIIRS LST product reached the provisional maturity in February 2019 and is ready for users to use in their applications.}, number={17}, journal={Remote Sensing}, publisher={MDPI AG}, author={Liu, Yuling and Yu, Yunyue and Yu, Peng and Wang, Heshun and Rao, Yuhan}, year={2019}, month={Aug}, pages={2003} }
 @article{rao_liang_wang_yu_song_zhou_shen_xu_2019, title={Estimating daily average surface air temperature using satellite land surface temperature and top-of-atmosphere radiation products over the Tibetan Plateau}, volume={234}, url={http://dx.doi.org/10.1016/j.rse.2019.111462}, DOI={10.1016/j.rse.2019.111462}, abstractNote={The Tibetan Plateau (TP) has experienced rapid warming in recent decades. However, the meteorological stations of the TP are scarce and mostly located at the eastern and southern parts of the TP where the elevation is relatively low, which increases the uncertainty of regional and local climate studies. Recently, the remotely sensed land surface temperature (LST) has been used to estimate the surface air temperature (SAT). However, the thermal infrared based LST is prone to cloud contamination, which limits the availability of the estimated SAT. This study presents a novel all sky model based on the rule-based Cubist regression to estimate all sky daily average SAT using LST, incident solar radiation (ISR), top-of-atmosphere (TOA) albedo and outgoing longwave radiation (OLR). The model is trained using station data of the Chinese Meteorological Administration (CMA) and corresponding satellite products. The output is evaluated using independent station data with the bias of −0.07 °C and RMSE of 1.87 °C. Additionally, the 25-fold cross validation shows a stable model performance (RMSE: 1.6–2.8 °C). Moreover, the all sky Cubist model increases the availability of the estimated SAT by nearly three times. We used the all sky Cubist model to estimate the daily average SAT of the TP for 2002–2016 at 0.05° × 0.05°. We compared our all sky Cubist model estimated daily average SAT with three existing data sets (i.e., GLDAS, CLDAS, and CMFD). Our model estimation shows similar spatial and temporal dynamics with these existing data sets but outperforms them with lower bias and RMSE when benchmarked against the CMA station data. The estimated SAT data could be very useful for regional and local climate studies over the TP. Although the model is developed for the TP, the framework is generic and may be extended to other regions with proper model training using local data.}, journal={Remote Sensing of Environment}, publisher={Elsevier BV}, author={Rao, Yuhan and Liang, Shunlin and Wang, Dongdong and Yu, Yunyue and Song, Zhen and Zhou, Yuan and Shen, Miaogen and Xu, Baiqing}, year={2019}, month={Dec}, pages={111462} }
 @article{li_chen_rao_2018, title={A practical sampling method for assessing accuracy of detected land cover/land use change: Theoretical analysis and simulation experiments}, volume={144}, url={https://doi.org/10.1016/j.isprsjprs.2018.08.006}, DOI={10.1016/j.isprsjprs.2018.08.006}, abstractNote={Accuracy assessment plays a crucial role in the implementation of change detection, which is commonly used to track land surface changes and ecosystem dynamics. There are currently two major indicators for accuracy assessment of change detection: the binary change accuracy (ca) and the overall transition accuracy (ta). The overall transition accuracy has been recommended over change accuracy, because the binary change accuracy does not consider the accuracy of the types of changes of the underlying land cover classes. However, the application of overall transition accuracy has been limited by the challenge of collecting enough representative samples with a practical sampling strategy to meet the users’ requirement of precision. This study provides an iterative sampling framework to ensure that the precision of the estimated overall transition accuracy meets the users’ predefined requirement. We use a set of simulated change maps to comprehensively examine the effectiveness and robustness of the proposed sampling strategy. The simulation-based results demonstrate that the proposed framework can achieve satisfactory performance for transition accuracy assessment and it is robust against different properties of classification results and target landscapes, including the degree of fragmentation, proportions of land cover types, and temporal correlation of the classification error between individual dates. The effectiveness, robustness and practicality of the proposed sampling strategy will enable producers and users of land cover/land use change maps to get reliable and meaningful accuracy assessment for further applications.}, journal={ISPRS Journal of Photogrammetry and Remote Sensing}, publisher={Elsevier BV}, author={Li, Yang and Chen, Jin and Rao, Yuhan}, year={2018}, month={Oct}, pages={379–389} }
 @article{rao_liang_yu_2018, title={Land Surface Air Temperature Data Are Considerably Different Among BEST‐LAND, CRU‐TEM4v, NASA‐GISS, and NOAA‐NCEI}, volume={123}, url={https://doi.org/10.1029/2018JD028355}, DOI={10.1029/2018JD028355}, abstractNote={AbstractSeveral groups routinely produce gridded land surface air temperature (LSAT) data sets using station measurements to assess the status and impact of climate change. The Intergovernmental Panel on Climate Change Fifth Assessment Report suggests that estimated global and hemispheric mean LSAT trends of different data sets are consistent. However, less attention has been paid to the intercomparison at local/regional scales, which is important for local/regional studies. In this study we comprehensively compare four data sets at different spatial and temporal scales, including Berkley Earth Surface Temperature land surface air temperature data set (BEST‐LAND), Climate Research Unit Temperature Data Set version 4 (CRU‐TEM4v), National Aeronautics and Space Administration Goddard Institute for Space Studies data (NASA‐GISS), and data provided by National Oceanic and Atmospheric Administration National Center for Environmental Information (NOAA‐NCEI). The mean LSAT anomalies are remarkably different because of the data coverage differences, with the magnitude nearly 0.4°C for the global and Northern Hemisphere and 0.6°C for the Southern Hemisphere. This study additionally finds that on the regional scale, northern high latitudes, southern middle‐to‐high latitudes, and the equator show the largest differences nearly 0.8°C. These differences cause notable differences for the trend calculation at regional scales. At the local scale, four data sets show significant variations over South America, Africa, Maritime Continent, central Australia, and Antarctica, which leads to remarkable differences in the local trend analysis. For some areas, different data sets produce conflicting results of whether warming exists. Our analysis shows that the differences across scales are associated with the availability of stations and the use of infilling techniques. Our results suggest that conventional LSAT data sets using only station observations have large uncertainties across scales, especially over station‐sparse areas. In developing future LSAT data sets, the data uncertainty caused by limited and unevenly distributed station observations must be reduced.}, number={11}, journal={Journal of Geophysical Research: Atmospheres}, publisher={American Geophysical Union (AGU)}, author={Rao, Yuhan and Liang, Shunlin and Yu, Yunyue}, year={2018}, month={Jun}, pages={5881–5900} }
 @article{chen_rao_shen_wang_zhou_ma_tang_yang_2016, title={A Simple Method for Detecting Phenological Change From Time Series of Vegetation Index}, volume={54}, DOI={10.1109/tgrs.2016.2518167}, abstractNote={Remote sensing is a valuable way to retrieve spatially continuous information on vegetation phenological changes, which are widely used as an indicator of climate change. We propose a simple method called weighted cross-correlogram spectral matching-phenology (CCSM-P), which combines CCSM and a weighted correlation system, for detecting vegetation phenological changes by using multiyear vegetation index (VI) time series. In experiments with simulated enhanced VI (EVI) for various scenarios, CCSM-P exhibited high accuracy and robustness to noise and the potential to capture long-term phenological change trends. For a temperate grassland in northern China, CCSM-P retrieved more reasonable vegetation spring phenology from Moderate Resolution Imaging Spectroradiometer (MODIS) EVI images than the MODIS phenology product (MCD12Q2). When validated against field phenological observations in five of the AmeriFlux Network sites in the U.S. (four deciduous broadleaf forest sites and a closed shrublands site), and a cropland site in China, CCSM-P exhibited mean absolute differences (MADs) ranging from 2 to 10 days (median: 4.2 days), whereas MAD of non-CCSM methods showed larger variations, ranging from 5 to 58 days (median: 21.3 days). This is because CCSM-P integrates field phenological observations. Compared with non-CCSM methods, which are widely used to identify phenological events, CCSM-P is more accurate and less dependent on prior knowledge (thresholds or predefined functions), which indicates its effectiveness and applicability for detecting year-to-year variations and long-term change trends in phenology, and should facilitate more reliable assessments of phenological changes in climate change studies.}, number={6}, journal={IEEE Transactions on Geoscience and Remote Sensing}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Chen, Jin and Rao, Yuhan and Shen, Miaogen and Wang, Cong and Zhou, Yuan and Ma, Lei and Tang, Yanhong and Yang, Xi}, year={2016}, month={Jun}, pages={3436–3449} }
 @article{lu_chen_tang_rao_yang_wu_2016, title={Land cover change detection by integrating object-based data blending model of Landsat and MODIS}, volume={184}, DOI={10.1016/j.rse.2016.07.028}, abstractNote={Accurate information on land cover changes is critical for global change studies, land cover mapping and ecosystem management. Although there are numerous change detection methods, pseudo changes can occur if data are acquired from different seasons, which presents a significant challenge for land cover change detection. In this study, land cover change detection by integrating object-based data blending model of Landsat and MODIS is proposed to solve this issue. The Estimation of Scale Parameter (ESP) tool under Minimum Mapping Unit (MMU) restriction is employed to identify the optimal scale for Landsat image segmentation. The Object Based Spatial and Temporal Vegetation Index Unmixing Model (OB-STVIUM) disaggregates MODIS NDVIs to Landsat objects using the spatial analysis and the linear mixing theory. Then, the change detection method of NDVI Gradient Difference (NDVI-GD) is developed to detect change and no-change objects considering the NDVI shape and value differences simultaneously. The results of the study indicate that the approach proposed in this study can effectively detect change areas when Landsat images are acquired from different seasons. OB-STVIUM is more suitable for change detection application compared with the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and NDVI Linear Mixing Growth Model (NDVI-LMGM), because it is less sensitive to the number and acquisition time of Landsat images.}, journal={Remote Sensing of Environment}, publisher={Elsevier BV}, author={Lu, Miao and Chen, Jun and Tang, Huajun and Rao, Yuhan and Yang, Peng and Wu, Wenbin}, year={2016}, month={Oct}, pages={374–386} }
 @article{wang_rao_wu_zhao_chen_2015, title={A method for screening climate change-sensitive infectious diseases}, volume={12}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84921288704&partnerID=MN8TOARS}, DOI={10.3390/ijerph120100767}, abstractNote={Climate change is a significant and emerging threat to human health, especially where infectious diseases are involved. Because of the complex interactions between climate variables and infectious disease components (i.e., pathogen, host and transmission environment), systematically and quantitatively screening for infectious diseases that are sensitive to climate change is still a challenge. To address this challenge, we propose a new statistical indicator, Relative Sensitivity, to identify the difference between the sensitivity of the infectious disease to climate variables for two different climate statuses (i.e., historical climate and present climate) in non-exposure and exposure groups. The case study in Anhui Province, China has demonstrated the effectiveness of this Relative Sensitivity indicator. The application results indicate significant sensitivity of many epidemic infectious diseases to climate change in the form of changing climatic variables, such as temperature, precipitation and absolute humidity. As novel evidence, this research shows that absolute humidity has a critical influence on many observed infectious diseases in Anhui Province, including dysentery, hand, foot and mouth disease, hepatitis A, hemorrhagic fever, typhoid fever, malaria, meningitis, influenza and schistosomiasis. Moreover, some infectious diseases are more sensitive to climate change in rural areas than in urban areas. This insight provides guidance for future health inputs that consider spatial variability in response to climate change.}, number={1}, journal={International Journal of Environmental Research and Public Health}, author={Wang, Y. and Rao, Y. and Wu, X. and Zhao, H. and Chen, J.}, year={2015}, pages={767–783} }
 @inproceedings{wang_cao_chen_liu_rao_2015, title={A quantitative assessment of multiple scattering in plant-soil mixtures and the implications on nonlinear spectral unmixing models}, DOI={10.1109/igarss.2015.7326129}, abstractNote={Bilinear Model (BM) is one of widely used nonlinear spectral unmixing methods, which are developed to deal with nonlinearity resulted from the multiple scattering within mixed pixels such as plant-soil mixtures. In the BM, products of endmember spectra are used to represent multiple scattering effect, and this approximation needs a validation. This study applies a Monte Carlo ray-tracing (MCRT) model to test this approximation by exploring the correlations between multiple scattering reflectances and endmember products. The correlations are found linear, proving the rationality of this approximation. Besides, the correlations between multiple scattering coefficients in the BM and vegetation characters are analyzed. Scattering coefficients are found to have quadratic relationships with vegetation coverage and linear relationships with crown height. The correlations can be used as constraints when solving the BM to retrieve the abundance of each component, to relieve the collinearity problem which impacts the solution precision.}, booktitle={2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)}, publisher={IEEE}, author={Wang, Jianmin and Cao, Xin and Chen, Jin and Liu, Desheng and Rao, Yuhan}, year={2015}, month={Jul} }
 @article{rao_zhu_chen_wang_2015, title={An improved method for producing high spatial-resolution NDVI time series datasets with multi-temporal MODIS NDVI data and Landsat TM/ETM+ images}, volume={7}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84933575461&partnerID=MN8TOARS}, DOI={10.3390/rs70607865}, abstractNote={Due to technical limitations, it is impossible to have high resolution in both spatial and temporal dimensions for current NDVI datasets. Therefore, several methods are developed to produce high resolution (spatial and temporal) NDVI time-series datasets, which face some limitations including high computation loads and unreasonable assumptions. In this study, an unmixing-based method, NDVI Linear Mixing Growth Model (NDVI-LMGM), is proposed to achieve the goal of accurately and efficiently blending MODIS NDVI time-series data and multi-temporal Landsat TM/ETM+ images. This method firstly unmixes the NDVI temporal changes in MODIS time-series to different land cover types and then uses unmixed NDVI temporal changes to predict Landsat-like NDVI dataset. The test over a forest site shows high accuracy (average difference: −0.0070; average absolute difference: 0.0228; and average absolute relative difference: 4.02%) and computation efficiency of NDVI-LMGM (31 seconds using a personal computer). Experiments over more complex landscape and long-term time-series demonstrated that NDVI-LMGM performs well in each stage of vegetation growing season and is robust in regions with contrasting spatial and spatial variations. Comparisons between NDVI-LMGM and current methods (i.e., Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), Enhanced STARFM (ESTARFM) and Weighted Linear Model (WLM)) show that NDVI-LMGM is more accurate and efficient than current methods. The proposed method will benefit land surface process research, which requires a dense NDVI time-series dataset with high spatial resolution.}, number={6}, journal={Remote Sensing}, author={Rao, Y. and Zhu, X. and Chen, J. and Wang, J.}, year={2015}, pages={7865–7891} }
 @article{li_rao_sun_wu_jin_bi_chen_lei_liu_duan_et al._2015, title={Identification of climate factors related to human infection with avian influenza A H7N9 and H5N1 viruses in China}, volume={5}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84949679345&partnerID=MN8TOARS}, DOI={10.1038/srep18094}, abstractNote={AbstractHuman influenza infections display a strongly seasonal pattern. However, whether H7N9 and H5N1 infections correlate with climate factors has not been examined. Here, we analyzed 350 cases of H7N9 infection and 47 cases of H5N1 infection. The spatial characteristics of these cases revealed that H5N1 infections mainly occurred in the South, Middle and Northwest of China, while the occurrence of H7N9 was concentrated in coastal areas of East and South of China. Aside from spatial-temporal characteristics, the most adaptive meteorological conditions for the occurrence of human infections by these two viral subtypes were different. We found that H7N9 infections correlate with climate factors, especially temperature (TEM) and relative humidity (RHU), while H5N1 infections correlate with TEM and atmospheric pressure (PRS). Hence, we propose a risky window (TEM 4–14 °C and RHU 65–95%) for H7N9 infection and (TEM 2–22 °C and PRS 980-1025 kPa) for H5N1 infection. Our results represent the first step in determining the effects of climate factors on two different virus infections in China and provide warning guidelines for the future when provinces fall into the risky windows. These findings revealed integrated predictive meteorological factors rooted in statistic data that enable the establishment of preventive actions and precautionary measures against future outbreaks.}, journal={Scientific Reports}, author={Li, J. and Rao, Y. and Sun, Q. and Wu, X. and Jin, J. and Bi, Y. and Chen, J. and Lei, F. and Liu, Q. and Duan, Z. and et al.}, year={2015} }
 @article{identification of climate factors related to human infection with avian influenza a h7n9 and h5n1 viruses in china_2015, DOI={10.60692/gsd8k-x9k88}, journal={OpenAlex}, year={2015}, month={Dec} }
 @article{identification of climate factors related to human infection with avian influenza a h7n9 and h5n1 viruses in china_2015, DOI={10.60692/m5p59-nsg82}, journal={OpenAlex}, year={2015}, month={Dec} }
 @article{wang_cao_chen_rao_tang_2015, title={Temperature sensitivity of spring vegetation phenology correlates to within-spring warming speed over the Northern Hemisphere}, volume={50}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84910647999&partnerID=MN8TOARS}, DOI={10.1016/j.ecolind.2014.11.004}, abstractNote={The inter-annual shift of spring vegetation phenology relative to per unit change of preseason temperature, referred to as temperature sensitivity (days °C−1), quantifies the response of spring phenology to temperature change. Temperature sensitivity was found to differ greatly among vegetation from different environmental conditions. Understanding the large-scale spatial pattern of temperature sensitivity and its underlying determinant will greatly improve our ability to predict spring phenology. In this study, we investigated the temperature sensitivity for natural ecosystems over the North Hemisphere (north of 30°N), based on the vegetation phenological date estimated from NDVI time-series data provided by the Advanced Very High Resolution Radiometer (AVHRR) and the corresponding climate dataset. We found a notable longitudinal change pattern with considerable increases of temperature sensitivity from inlands to most coastal areas and a less obvious latitudinal pattern with larger sensitivity in low latitude area. This general spatial variation in temperature sensitivity is most strongly associated with the within-spring warming speed (WWS; r = 0.35, p < 0.01), a variable describing the increase speed of daily mean temperature during spring within a year, compared with other factors including the mean spring temperature, spring precipitation and mean winter temperature. These findings suggest that the same magnitude of warming will less affect spring vegetation phenology in regions with higher WWS, which might partially reflect plants’ adaption to local climate that prevents plants from frost risk caused by the advance of spring phenology. WWS accounts for the spatial variation in temperature sensitivity and should be taken into account in forecasting spring phenology and in assessing carbon cycle under the projected climate warming.}, journal={Ecological Indicators}, author={Wang, C. and Cao, R. and Chen, J. and Rao, Y. and Tang, Y.}, year={2015}, month={Mar}, pages={62–68} }
 @article{chen_li_chen_rao_yamaguchi_2014, title={A combination of TsHARP and thin plate spline interpolation for spatial sharpening of thermal imagery}, volume={6}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84898077645&partnerID=MN8TOARS}, DOI={10.3390/rs6042845}, abstractNote={There have been many studies and much attention paid to spatial sharpening for thermal imagery. Among them, TsHARP, based on the good correlation between vegetation index and land surface temperature (LST), is regarded as a standard technique because of its operational simplicity and effectiveness. However, as LST is affected by other factors (e.g., soil moisture) in the areas with low vegetation cover, these areas cannot be well sharpened by TsHARP. Thin plate spline (TPS) is another popular downscaling technique for surface data. It has been shown to be accurate and robust for different datasets; however, it has not yet been attempted in thermal sharpening. This paper proposes to combine the TsHARP and TPS methods to enhance the advantages of each. The spatially explicit errors of these two methods were firstly estimated in theory, and then the results of TPS and TsHARP were combined with the estimation of their errors. The experiments performed across various landscapes and data showed that the proposed combined method performs more robustly and accurately than TsHARP.}, number={4}, journal={Remote Sensing}, author={Chen, X. and Li, W. and Chen, J. and Rao, Y. and Yamaguchi, Y.}, year={2014}, pages={2845–2863} }
 @article{chen_li_chen_zhan_rao_2014, title={A simple error estimation method for linear-regression-based thermal sharpening techniques with the consideration of scale difference}, volume={17}, DOI={10.1080/10095020.2014.889546}, abstractNote={Thermal remote sensing imagery is helpful for land cover classification and related analysis. Unfortunately, the spatial resolution of thermal infrared (TIR) band is generally coarser than that of visual near-infrared band, which limits its more precise applications. Various thermal sharpening (TSP) techniques have been developed for improving the spatial resolution of the imagery of TIR band or land surface temperature (LST). However, there is no research on the theoretical estimation of TSP error till now, which implies that the error in sharpened LST imagery is unknown and the further analysis might be not reliable. In this paper, an error estimation method based on classical linear regression theory for the linear-regression-based TSP techniques was firstly introduced. However, the scale difference between the coarse resolution and fine resolution is not considered in this method. Therefore, we further developed an improved error estimation method with the consideration of the scale difference, which employs a novel term named equivalent random sample size to reflect the scale difference. A simulation study of modified TsHARP (a typical TSP technique) shows that the improved method estimated the TSP error more accurately than classical regression theory. Especially, the phenomena that TSP error increases with the increasing resolution gap between the initial and target resolutions can be successfully predicted by the proposed method.}, number={1}, journal={Geo-spatial Information Science}, publisher={Informa UK Limited}, author={Chen, Xuehong and Li, Wentao and Chen, Jin and Zhan, Wenfeng and Rao, Yuhan}, year={2014}, month={Jan}, pages={54–59} }
 @article{fan_guo_li_chen_lin_zhang_shen_rao_wang_ma_2014, title={Earlier vegetation green-up has reduced spring dust storms}, volume={4}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84947281985&partnerID=MN8TOARS}, DOI={10.1038/srep06749}, abstractNote={The observed decline of spring dust storms in Northeast Asia since the 1950s has been attributed to surface wind stilling. However, spring vegetation growth could also restrain dust storms through accumulating aboveground biomass and increasing surface roughness. To investigate the impacts of vegetation spring growth on dust storms, we examine the relationships between recorded spring dust storm outbreaks and satellite-derived vegetation green-up date in Inner Mongolia, Northern China from 1982 to 2008. We find a significant dampening effect of advanced vegetation growth on spring dust storms (r = 0.49, p = 0.01), with a one-day earlier green-up date corresponding to a decrease in annual spring dust storm outbreaks by 3%. Moreover, the higher correlation (r = 0.55, p < 0.01) between green-up date and dust storm outbreak ratio (the ratio of dust storm outbreaks to times of strong wind events) indicates that such effect is independent of changes in surface wind. Spatially, a negative correlation is detected between areas with advanced green-up dates and regional annual spring dust storms (r = −0.49, p = 0.01). This new insight is valuable for understanding dust storms dynamics under the changing climate. Our findings suggest that dust storms in Inner Mongolia will be further mitigated by the projected earlier vegetation green-up in the warming world.}, journal={Scientific Reports}, author={Fan, B. and Guo, L. and Li, N. and Chen, J. and Lin, H. and Zhang, X. and Shen, M. and Rao, Y. and Wang, C. and Ma, L.}, year={2014} }
 @inproceedings{rao_chen_chen_wang_2013, title={Quantitative assessment of the different methods addressing the endmember variability}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84894273818&partnerID=MN8TOARS}, DOI={10.1109/IGARSS.2013.6723537}, abstractNote={Spectral mixture analysis is an important technique to extract desired information from the mixed remotely sensed data. However, current spectral mixture analysis techniques suffered from the endmember variability. Quantitative assessment of SMA techniques with simulated data is critical to understand the influence of endmember variability. For that reason, this study has compared five typical spectral mixture analysis addressing endmember variability issue with simulated data. The comparison result shows that MESMA seems to be the best in unmixing accuracy. However, sensitive to noise and large computation loads also made MESMA less satisfactory, while other methods could supersede MESMA at specific situations.}, booktitle={International Geoscience and Remote Sensing Symposium (IGARSS)}, publisher={IEEE}, author={Rao, Yuhan and Chen, Jin and Chen, Xuehong and Wang, Jianmin}, year={2013}, pages={3317–3320} }
 @article{混合像元分解技术及其进展, volume={20}, url={http://www.jors.cn/jrs/ch/reader/view_abstract.aspx?file_no=r16169&flag=1}, number={5}, journal={遥感学报}, pages={1102} }