{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T06:05:52Z","timestamp":1767852352505,"version":"3.49.0"},"reference-count":37,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2020,9,23]],"date-time":"2020-09-23T00:00:00Z","timestamp":1600819200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100006224","name":"Argonne National Laboratory","doi-asserted-by":"publisher","award":["LDRD-2014-132"],"award-info":[{"award-number":["LDRD-2014-132"]}],"id":[{"id":"10.13039\/100006224","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Despite an advanced ability to forecast ecosystem functions and climate at regional and global scales, little is known about relationships between local variations in water and carbon fluxes and large-scale phenomena. To enable data collection of local-scale ecosystem functions to support such investigations, we developed the EcoSpec system, a highly equipped remote sensing system that houses a hyperspectral radiometer (350\u20132500 nm) and five optical and infrared sensors in a compact tower. Its custom software controls the sequence and timing of movement of the sensors and system components and collects measurements at 12 locations around the tower. The data collected using the system was processed to remove sun-angle effects, and spectral vegetation indices computed from the data (i.e., the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Photochemical Reflectance Index (PRI), and Moisture Stress Index (MSI)) were compared with the fraction of photochemically active radiation (fPAR) and canopy temperature. The results showed that the NDVI, NDWI, and PRI were strongly correlated with fPAR; the MSI was correlated with canopy temperature at the diurnal scale. These correlations suggest that this type of near-surface remote sensing system would complement existing observatories to validate satellite remote sensing observations and link local and large-scale phenomena to improve our ability to forecast ecosystem functions and climate. The system is also relevant for precision agriculture to study crop growth, detect disease and pests, and compare traits of cultivars.<\/jats:p>","DOI":"10.3390\/s20195463","type":"journal-article","created":{"date-parts":[[2020,9,24]],"date-time":"2020-09-24T02:56:43Z","timestamp":1600916203000},"page":"5463","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["EcoSpec: Highly Equipped Tower-Based Hyperspectral and Thermal Infrared Automatic Remote Sensing System for Investigating Plant Responses to Environmental Changes"],"prefix":"10.3390","volume":"20","author":[{"given":"Yuki","family":"Hamada","sequence":"first","affiliation":[{"name":"Argonne National Laboratory, Lemont, IL 60439, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3645-7590","authenticated-orcid":false,"given":"David","family":"Cook","sequence":"additional","affiliation":[{"name":"Argonne National Laboratory, Lemont, IL 60439, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Donald","family":"Bales","sequence":"additional","affiliation":[{"name":"Argonne National Laboratory, Lemont, IL 60439, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"598","DOI":"10.1038\/ngeo618","article-title":"The boundless carbon cycle","volume":"2","author":"Battin","year":"2009","journal-title":"Nat. Geosci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1285","DOI":"10.1073\/pnas.1515160113","article-title":"The decadal state of the terrestrial carbon cycle: Global retrievals of terrestrial carbon allocation, pools, and residence times","volume":"113","author":"Bloom","year":"2016","journal-title":"PNAS"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Dixon, R.K. (2000). The global carbon cycle and global change: Responses and feedbacks from the mycorrhizosphere. Mycorrhizal Biology, Springer.","DOI":"10.1007\/978-1-4615-4265-0_6"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2117","DOI":"10.1111\/gcb.12187","article-title":"Evaluation of terrestrial carbon cycle models for their response to climate variability and to CO2 trends","volume":"19","author":"Piao","year":"2013","journal-title":"Glob. Chang. Biol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"111383","DOI":"10.1016\/j.rse.2019.111383","article-title":"Remote sensing of the terrestrial carbon cycle: A review of advances over 50 years","volume":"233","author":"Xiao","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-018-07238-2","article-title":"Cross-ecosystem carbon flows connecting ecosystems worldwide","volume":"9","author":"Gounand","year":"2018","journal-title":"Nat. Commun."},{"key":"ref_7","first-page":"58","article-title":"Spatially-explicit modeling of multi-scale drivers of aboveground forest biomass and water yield in watersheds of the Southeastern United States","volume":"199","author":"Ahmed","year":"2017","journal-title":"J. Environ. Manag."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1922","DOI":"10.1111\/gcb.14619","article-title":"Plant phenology and global climate change: Current progresses and challenges","volume":"25","author":"Piao","year":"2019","journal-title":"Glob. Chang. Biol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1016\/j.rse.2010.08.023","article-title":"The photochemical reflectance index (PRI) and the remote sensing of leaf, canopy and ecosystem radiation use efficiencies: A review and meta-analysis","volume":"115","author":"Garbulsky","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1093\/jxb\/err294","article-title":"Leaf optical properties reflect variation in photosynthetic metabolism and its sensitivity to temperature","volume":"63","author":"Serbin","year":"2012","journal-title":"J. Exp. Bot."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1259","DOI":"10.1007\/s10811-018-1655-3","article-title":"Effect of photosynthetically active radiation and temperature on the photosynthesis of two heteromorphic life history stages of a temperate edible brown alga, Cladosiphon umezakii (Chordariaceae, Ectocarpales), from Japan","volume":"31","author":"Fukumoto","year":"2019","journal-title":"J. Appl. Phycol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1002\/fee.2031","article-title":"Enhancing global change experiments through integration of remote-sensing techniques","volume":"17","author":"Shiklomanov","year":"2019","journal-title":"Front. Ecol. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"919","DOI":"10.1111\/jvs.12150","article-title":"Foliar functional traits that predict plant biomass response to warming","volume":"25","author":"Gornish","year":"2014","journal-title":"Appl. Veg. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1088","DOI":"10.3390\/s150101088","article-title":"WhiteRef: A new tower-based hyperspectral system for continuous reflectance measurements","volume":"15","author":"Sakowska","year":"2015","journal-title":"Sensors"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"111177","DOI":"10.1016\/j.rse.2019.04.030","article-title":"Remote sensing of solar-induced chlorophyll fluorescence (SIF) in vegetation: 50 years of progress","volume":"231","author":"Mohammed","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2977","DOI":"10.1002\/2015GL063201","article-title":"Solar-induced chlorophyll fluorescence that correlates with canopy photosynthesis on diurnal and seasonal scales in a temperate deciduous forest","volume":"42","author":"Yang","year":"2015","journal-title":"Geophys. Res. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1016\/j.rse.2018.07.002","article-title":"PhotoSpec: A new instrument to measure spatially distributed red and far-red Solar-Induced Chlorophyll Fluorescence","volume":"216","author":"Grossmann","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Yang, X., Shi, H., Stovall, A., Guan, K., Miao, G., Zhang, Y., Zhang, Y., Xiao, X., Ryu, Y., and Lee, J.E. (2018). FluoSpec 2\u2014An automated field spectroscopy system to monitor canopy solar-induced fluorescence. Sensors, 18.","DOI":"10.3390\/s18072063"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Gerhards, M., Schlerf, M., Mallick, K., and Udelhoven, T. (2019). Challenges and future perspectives of multi-\/Hyperspectral thermal infrared remote sensing for crop water-stress detection: A review. Remote Sens., 11.","DOI":"10.3390\/rs11101240"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.compag.2015.12.007","article-title":"Development and evaluation of thermal infrared imaging system for high spatial and temporal resolution crop water stress monitoring of corn within a greenhouse","volume":"121","author":"Mangus","year":"2016","journal-title":"Comput. Electron. Agric."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/0034-4257(79)90013-0","article-title":"Red and photographic infrared linear combinations for monitoring vegetation","volume":"8","author":"Tucker","year":"1979","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1016\/0034-4257(87)90040-X","article-title":"Satellite remote sensing of drought conditions","volume":"23","author":"Tucker","year":"1987","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/S0034-4257(96)00067-3","article-title":"NDWI\u2014A normalized difference water index for remote sensing of vegetation liquid water from space","volume":"58","author":"Gao","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/0034-4257(92)90059-S","article-title":"A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency","volume":"41","author":"Gamon","year":"1992","journal-title":"Remote Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/0034-4257(89)90046-1","article-title":"Detection of changes in leaf water content using near-and middle-infrared reflectances","volume":"30","author":"Hunt","year":"1989","journal-title":"Remote Sens. Environ."},{"key":"ref_26","first-page":"414","article-title":"Estimation of green grass\/herb biomass from airborne hyperspectral imagery using spectral indices and partial least squares regression","volume":"9","author":"Cho","year":"2007","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"3999","DOI":"10.1080\/01431160310001654923","article-title":"Narrow band vegetation indices overcome the saturation problem in biomass estimation","volume":"25","author":"Mutanga","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"475","DOI":"10.1016\/j.rse.2003.10.021","article-title":"Vegetation water content mapping using Landsat data derived normalized difference water index for corn and soybeans","volume":"92","author":"Jackson","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1035","DOI":"10.1080\/0143116042000273998","article-title":"Use of normalized difference water index for monitoring live fuel moisture","volume":"26","author":"Dennison","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Marino, E., Yebra, M., Guill\u00e9n-Climent, M., Algeet, N., Tom\u00e9, J.L., Madrigal, J., Guijarro, M., and Hernando, C. (2020). Investigating Live Fuel Moisture Content Estimation in Fire-Prone Shrubland from Remote Sensing Using Empirical Modelling and RTM Simulations. Remote Sens., 12.","DOI":"10.3390\/rs12142251"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1016\/j.rse.2004.03.017","article-title":"Estimating live fuel moisture content from remotely sensed reflectance","volume":"92","author":"Danson","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_32","first-page":"27","article-title":"Water stress detection in potato plants using leaf temperature, emissivity, and reflectance","volume":"53","author":"Gerhards","year":"2016","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.ecoinf.2019.05.008","article-title":"Estimating leaf area index and light extinction coefficient using Random Forest regression algorithm in a tropical moist deciduous forest, India","volume":"52","author":"Srinet","year":"2019","journal-title":"Ecol. Inform."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"978","DOI":"10.1071\/FP09123","article-title":"Thermal infrared imaging of crop canopies for the remote diagnosis and quantification of plant responses to water stress in the field","volume":"36","author":"Jones","year":"2009","journal-title":"Funct. Plant. Biol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1175\/1520-0469(1986)043<0505:ASBMFU>2.0.CO;2","article-title":"A simple biosphere model (SiB) for use within general circulation models","volume":"43","author":"Sellers","year":"1986","journal-title":"J. Atmos. Sci."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1038\/363234a0","article-title":"Global climate change and terrestrial net primary production","volume":"363","author":"Melillo","year":"1993","journal-title":"Nature"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Hu, L., Fan, W., Ren, H., Liu, S., Cui, Y., and Zhao, P. (2018). Spatiotemporal dynamics in vegetation GPP over the great khingan mountains using GLASS products from 1982 to 2015. Remote Sens., 10.","DOI":"10.3390\/rs10030488"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/19\/5463\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:12:57Z","timestamp":1760177577000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/19\/5463"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,23]]},"references-count":37,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2020,10]]}},"alternative-id":["s20195463"],"URL":"https:\/\/doi.org\/10.3390\/s20195463","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9,23]]}}}