{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T07:59:52Z","timestamp":1769759992473,"version":"3.49.0"},"reference-count":76,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2021,4,7]],"date-time":"2021-04-07T00:00:00Z","timestamp":1617753600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Water management and irrigation practices are persistent challenges for many agricultural systems, exacerbated by changing seasonal and weather patterns. The wild blueberry industry is at heightened susceptibility due to its unique growing conditions and uncultivated nature. Stress detection in agricultural fields can prompt management responses to mitigate detrimental conditions, including drought and disease. We assessed airborne spectral data accompanied by ground sampled water potential over three developmental stages of wild blueberries collected throughout the 2019 summer on two adjacent fields, one irrigated and one non-irrigated. Ground sampled leaves were collected in tandem to the hyperspectral image collection with an unoccupied aerial vehicle (UAV) and then measured for leaf water potential. Using methods in machine learning and statistical analysis, we developed models to determine irrigation status and water potential. Seven models were assessed in this study, with four used to process six hyperspectral cube images for analysis. These images were classified as irrigated or non-irrigated and estimated for water potential levels, resulting in an R2 of 0.62 and verified with a validation dataset. Further investigation relating imaging spectroscopy and water potential will be beneficial in understanding the dynamics between the two for future studies.<\/jats:p>","DOI":"10.3390\/rs13081425","type":"journal-article","created":{"date-parts":[[2021,4,7]],"date-time":"2021-04-07T11:31:59Z","timestamp":1617795119000},"page":"1425","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Predicting Water Stress in Wild Blueberry Fields Using Airborne Visible and Near Infrared Imaging Spectroscopy"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7957-903X","authenticated-orcid":false,"given":"Catherine","family":"Chan","sequence":"first","affiliation":[{"name":"School of Forest Resources, University of Maine, Orono, ME 04469, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5539-4914","authenticated-orcid":false,"given":"Peter R.","family":"Nelson","sequence":"additional","affiliation":[{"name":"Schoodic Institute, 9 Atterbury Circle, P.O. Box 277, Winter Harbor, ME 04693, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3011-7934","authenticated-orcid":false,"given":"Daniel J.","family":"Hayes","sequence":"additional","affiliation":[{"name":"School of Forest Resources, University of Maine, Orono, ME 04469, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5637-3015","authenticated-orcid":false,"given":"Yong-Jiang","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Biology and Ecology, University of Maine, Orono, ME 04469, USA"}]},{"given":"Bruce","family":"Hall","sequence":"additional","affiliation":[{"name":"Jasper Wyman &amp; Son, P.O. Box 100, Milbridge, ME 04658, USA"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Stuart, M.B., Mcgonigle, A.J.S., and Willmott, J.R. (2019). Hyperspectral Imaging in Environmental Monitoring: A Review of Recent Developments and Technological Advances in Compact Field Deployable Systems. Sensors, 19.","DOI":"10.3390\/s19143071"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/S0034-4257(98)00064-9","article-title":"Imaging Spectroscopy and the Airborne Visible\/Infrared Imaging Spectrometer (AVIRIS)","volume":"65","author":"Green","year":"1998","journal-title":"Remote Sens. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Marrs, J., and Ni-Meister, W. (2019). Machine Learning Techniques for Tree Species Classification Using Co-Registered LiDAR and Hyperspectral Data. Remote Sens., 11.","DOI":"10.3390\/rs11070819"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.jfoodeng.2018.06.013","article-title":"Cross-polarized VNIR hyperspectral reflectance imaging for non-destructive quality evaluation of dried banana slices, drying process monitoring and control","volume":"238","author":"Dusabumuremyi","year":"2018","journal-title":"J. Food Eng."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1007\/s41348-017-0124-6","article-title":"Benefits of hyperspectral imaging for plant disease detection and plant protection: A technical perspective","volume":"125","author":"Thomas","year":"2017","journal-title":"J. Plant Dis. Prot."},{"key":"ref_6","first-page":"1","article-title":"Advances in remote sensing of vegetation function and traits","volume":"43","author":"Houborg","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.future.2014.10.029","article-title":"Remote sensing big data computing: Challenges and opportunities","volume":"51","author":"Ma","year":"2015","journal-title":"Futur. Gener. Comput. Syst."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1016\/S0169-5347(03)00071-5","article-title":"From space to species: Ecological applications for remote sensing","volume":"18","author":"Kerr","year":"2003","journal-title":"Trends Ecol. Evol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1016\/j.isprsjprs.2010.09.001","article-title":"Status and future of laser scanning, synthetic aperture radar and hyperspectral remote sensing data for forest biomass assessment","volume":"65","author":"Koch","year":"2010","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1016\/j.compag.2018.12.018","article-title":"A remote sensing technique for detecting laurel wilt disease in avocado in presence of other biotic and abiotic stresses","volume":"156","author":"Abdulridha","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_11","first-page":"1","article-title":"NDVI derived sugarcane area identification and crop condition assessment","volume":"1","author":"Rahman","year":"2004","journal-title":"Plan Plus"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"6039","DOI":"10.1073\/pnas.0400168101","article-title":"From The Cover: Drought stress and carbon uptake in an Amazon forest measured with spaceborne imaging spectroscopy","volume":"101","author":"Asner","year":"2004","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1080\/07388550902869792","article-title":"Understanding water deficit stress-induced changes in the basic metabolism of higher plants\u2013biotechnologically and sustainably improving agriculture and the ecoenvironment in arid regions of the globe","volume":"29","author":"Shao","year":"2009","journal-title":"Crit. Rev. Biotechnol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1016\/j.jplph.2004.01.013","article-title":"Drought-induced responses of photosynthesis and antioxidant metabolism in higher plants","volume":"161","author":"Reddy","year":"2004","journal-title":"J. Plant Physiol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1016\/j.compag.2017.07.026","article-title":"Recent advances in crop water stress detection","volume":"141","author":"Ihuoma","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Kramer, P.J., and Boyer, J.S. (1995). Water Relations of Plants and Soils, Academic Press.","DOI":"10.1016\/B978-012425060-4\/50003-6"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2145","DOI":"10.1093\/jxb\/eru496","article-title":"The divining root: Moisture-driven responses of roots at the micro- and macro-scale","volume":"66","author":"Robbins","year":"2015","journal-title":"J. Exp. Bot."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1023\/A:1026415302759","article-title":"Significance and limits in the use of predawn leaf water potential for tree irrigation","volume":"207","author":"Archer","year":"1998","journal-title":"Plant Soil"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"19","DOI":"10.21273\/HORTSCI.25.1.19","article-title":"Physiological Aspects of the Control of Water Status in Horticultural Crops","volume":"25","author":"Jones","year":"1990","journal-title":"HortScience"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Borengasser, M., Hungate, W.S., and Watkins, R. (2007). Hyperspectral Remote Sensing: Principles and Applications, CRC Press.","DOI":"10.1201\/9781420012606"},{"key":"ref_21","unstructured":"Rose, A., Drummond, F.A., Yarborough, D.E., and Asare, E. (2013). MR445: Maine Wild Blueberry Growers: A 2010 Economic and Sociological Analysis of a Traditional Downeast Crop in Transition, DigitalCommons@UMaine."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"911","DOI":"10.4141\/P03-027","article-title":"Proctor tolerance of lowbush blueberries (Vaccinium angustifolium Ait.) to drought stress. II. Soil water and yield component analysis","volume":"85","author":"Glass","year":"2005","journal-title":"Can. J. Plant Sci."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"105778","DOI":"10.1016\/j.compag.2020.105778","article-title":"Wild blueberry yield prediction using a combination of computer simulation and machine learning algorithms","volume":"178","author":"Obsie","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Tasnim, R., Drummond, F., and Zhang, Y.-J. (2021). Climate Change Patterns of Wild Blueberry Fields in Downeast, Maine over the Past 40 Years. Water, 13.","DOI":"10.3390\/w13050594"},{"key":"ref_25","unstructured":"Whittle, P. (2018, November 24). Hard Times as Maine Wild Blueberry Industry in Decline. Available online: https:\/\/www.timesrecord.com\/articles\/maine-1\/hard-times-as-maine-wild-blueberry-industry-in-decline\/."},{"key":"ref_26","unstructured":"Tasnim, R., Calderwood, L., Annis, S., Drummond, F.A., and Zhang, Y.J. (2020). The Future of Wild Blueberries: Testing Warming Impacts Using Open-Top Chambers, Spire."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1146\/annurev-marine-010318-095323","article-title":"Unoccupied aircraft systems in marine science and conservation","volume":"11","author":"Johnston","year":"2019","journal-title":"Annu. Rev. Mar. Sci."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"e12647","DOI":"10.1111\/jfpe.12647","article-title":"Visualization research of moisture content in leaf lettuce leaves based on WT-PLSR and hyperspectral imaging technology","volume":"41","author":"Zhou","year":"2018","journal-title":"J. Food Process Eng."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.isprsjprs.2015.09.003","article-title":"Combining leaf physiology, hyperspectral imaging and partial least squares-regression (PLS-R) for grapevine water status assessment","volume":"109","author":"Rapaport","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_30","unstructured":"Nelson, P.R. (2020). Github Repository, LECOSPEC. Available online: https:\/\/github.com\/nelsopet\/lecospec."},{"key":"ref_31","unstructured":"Nelson, P., and Thompson, N. (2018). Visible and Infrared Imaging Spectroscopy for High Resolution Mapping and Health Assessment of Maine\u2019s Forest and Agricultural Resources, Maine Economic Improvement Funds (MEIF) Small Campus Initiative (SCI) Research Grant."},{"key":"ref_32","unstructured":"Anthony, A., Imke, D., Scott, A., Mike, S., Russell, V., Xungang, Y., and Rocky, B. NOAA\u2019s U.S. Climate Normals (1981\u20132010), NOAA National Centers for Environmental Information."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1097\/SS.0b013e3182376ed6","article-title":"Response of wild blueberry yield to spatial variability of soil properties","volume":"177","author":"Farooque","year":"2012","journal-title":"Soil Sci."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1300\/J301v03n01_16","article-title":"Stem and Leaf Diseases and Their Effects on Yield in Maine Lowbush Blueberry Fields","volume":"3","author":"Annis","year":"2004","journal-title":"Small Fruits Rev."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1007\/s00271-018-0603-y","article-title":"Evaluation of crop water stress index and leaf water potential for deficit irrigation management of sprinkler-irrigated wheat","volume":"37","author":"Alghory","year":"2019","journal-title":"Irrig. Sci."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Forkuor, G., Hounkpatin, O.K.L., Welp, G., and Thiel, M. (2017). High Resolution Mapping of Soil Properties Using Remote Sensing Variables in South-Western Burkina Faso: A Comparison of Machine Learning and Multiple Linear Regression Models. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0170478"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/j.rse.2003.12.013","article-title":"Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture","volume":"90","author":"Haboudane","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_38","unstructured":"Schilder, A. (2018, November 20). Growth Stages. Michigan State University Extension, Available online: https:\/\/www.canr.msu.edu\/blueberries\/growing_blueberries\/growth-stages."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"245","DOI":"10.21273\/HORTSCI.26.3.245","article-title":"Natural Variability in Yield of Lowbush Blueberries","volume":"26","author":"Hepler","year":"1991","journal-title":"HortScience"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"641","DOI":"10.2134\/agronj2003.0257","article-title":"Temporal and Spatial Relationships between Within-Field Yield Variability in Cotton and High-Spatial Hyperspectral Remote Sensing Imagery","volume":"97","author":"Ustin","year":"2005","journal-title":"Agron. J."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1353691","DOI":"10.1155\/2017\/1353691","article-title":"Significant remote sensing vegetation indices: A review of developments and applications","volume":"2017","author":"Xue","year":"2017","journal-title":"J. Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"5584","DOI":"10.3390\/rs70505584","article-title":"Early detection and quantification of Verticillium wilt in olive using hyperspectral and thermal imagery over large areas","volume":"7","year":"2015","journal-title":"Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Poona, N., Van Niekerk, A., and Ismail, R. (2016). Investigating the utility of oblique tree-based ensembles for the classification of hyperspectral data. Sensors, 16.","DOI":"10.3390\/s16111918"},{"key":"ref_44","first-page":"535","article-title":"Spectral resampling based on user-defined inter-band correlation filter: C3 and C4 grass species classification","volume":"21","author":"Adjorlolo","year":"2013","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v089.i12","article-title":"Hyperspectral Data Analysis in R: The hsdar Package","volume":"89","author":"Lehnert","year":"2019","journal-title":"J. Stat. Softw."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Boonprong, S., Cao, C., Chen, W., and Bao, S. (2018). Random forest variable importance spectral indices scheme for burnt forest recovery monitoring\u2014Multilevel RF-VIMP. Remote Sens., 10.","DOI":"10.3390\/rs10060807"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v077.i01","article-title":"Ranger: A fast implementation of random forests for high dimensional data in C++ and R","volume":"77","author":"Wright","year":"2017","journal-title":"J. Stat. Softw."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.aca.2017.02.001","article-title":"Predicting the ethanol potential of wheat straw using near-infrared spectroscopy and chemometrics: The challenge of inherently intercorrelated response functions","volume":"962","author":"Rinnan","year":"2017","journal-title":"Anal. Chim. Acta"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1681","DOI":"10.3389\/fpls.2017.01681","article-title":"UAV-based thermal imaging for high-throughput field phenotyping of black poplar response to drought","volume":"8","author":"Ludovisi","year":"2017","journal-title":"Front. Plant Sci."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"432","DOI":"10.1038\/s41477-018-0189-7","article-title":"Previsual symptoms of Xylella fastidiosa infection revealed in spectral plant-trait alterations","volume":"4","author":"Camino","year":"2018","journal-title":"Nat. Plants"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Fan, L., Zhao, J., Xu, X., Liang, D., Yang, G., Feng, H., Yang, H., Wang, Y., Chen, G., and Wei, P. (2019). Hyperspectral-based estimation of leaf nitrogen content in corn using optimal selection of multiple spectral variables. Sensors, 19.","DOI":"10.3390\/s19132898"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/S0176-1617(99)80314-9","article-title":"A New Reflectance Index for Remote Sensing of Chlorophyll Content in Higher Plants: Tests using Eucalyptus Leaves","volume":"154","author":"Datt","year":"1999","journal-title":"J. Plant Physiol."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"296","DOI":"10.1016\/S0034-4257(99)00023-1","article-title":"The Chlorophyll Fluorescence Ratio F735\/F700 as an Accurate Measure of the Chlorophyll Content in Plants","volume":"69","author":"Gitelson","year":"1999","journal-title":"Remot Sens. Environ."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/0034-4257(95)00186-7","article-title":"Optimization of soil-adjusted vegetation indices","volume":"55","author":"Rondeaux","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"5403","DOI":"10.1080\/0143116042000274015","article-title":"The MERIS terrestrial chlorophyll index","volume":"25","author":"Dash","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/S0034-4257(00)00197-8","article-title":"Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density","volume":"76","author":"Broge","year":"2001","journal-title":"Remote Sens. Environ."},{"key":"ref_57","first-page":"667","article-title":"Improved Classification of Mangroves Health Status Using Hyperspectral Remote Sensing Data","volume":"XL-8","author":"Vidhya","year":"2014","journal-title":"ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/S0034-4257(99)00067-X","article-title":"Hyperspectral Vegetation Indices and Their Relationships with Agricultural Crop Characteristics","volume":"71","author":"Thenkabail","year":"2000","journal-title":"Remote Sens. Environ."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"e6926","DOI":"10.7717\/peerj.6926","article-title":"Combining UAV-based hyperspectral imagery and machine learning algorithms for soil moisture content monitoring","volume":"7","author":"Ge","year":"2019","journal-title":"PeerJ"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.rse.2015.04.032","article-title":"Estimation of crop LAI using hyperspectral vegetation indices and a hybrid inversion method","volume":"165","author":"Liang","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.compag.2010.06.009","article-title":"Early detection and classification of plant diseases with Support Vector Machines based on hyperspectral reflectance","volume":"74","author":"Rumpf","year":"2010","journal-title":"Comput. Electron. Agric."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1034\/j.1399-3054.1999.106119.x","article-title":"Non-destructive optical detection of pigment changes during leaf senescence and fruit ripening","volume":"106","author":"Merzlyak","year":"1999","journal-title":"Physiol. Plant."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1093\/jxb\/erl118","article-title":"Monitoring plant and soil water status: Established and novel methods revisited and their relevance to studies of drought tolerance","volume":"58","author":"Jones","year":"2006","journal-title":"J. Exp. Bot."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1152","DOI":"10.1002\/biot.200800242","article-title":"Multi-sensor plant imaging: Towards the development of a stress-catalogue","volume":"4","author":"Chaerle","year":"2009","journal-title":"Biotechnol. J."},{"key":"ref_65","first-page":"549","article-title":"Effect of cold and drought stress on blueberry dehydrin accumulation","volume":"76","author":"Panta","year":"2001","journal-title":"J. Hortic. Sci. Biotechnol."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1007\/s11119-019-09640-2","article-title":"Hyperspectral remote sensing of grapevine drought stress","volume":"20","author":"Zovko","year":"2019","journal-title":"Precis. Agric."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"1843","DOI":"10.1093\/jxb\/eri174","article-title":"Estimation of leaf water potential by thermal imagery and spatial analysis","volume":"56","author":"Cohen","year":"2005","journal-title":"J. Exp. Bot."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.compag.2017.02.024","article-title":"Hyperspectral data mining to identify relevant canopy spectral features for estimating durum wheat growth, nitrogen status, and grain yield","volume":"136","author":"Thorp","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Gonz\u00e1lez-Fern\u00e1ndez, A.B., Sanz-Ablanedo, E., Gabella, V.M., Garc\u00eda-Fern\u00e1ndez, M., and Rodr\u00edguez-P\u00e9rez, J.R. (2019). Field Spectroscopy: A Non-Destructive Technique for Estimating Water Status in Vineyards. Agronomy, 9.","DOI":"10.3390\/agronomy9080427"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"4626","DOI":"10.3390\/rs70404626","article-title":"Remote Estimation of Leaf and Canopy Water Content in Winter Wheat with Different Vertical Distribution of Water-Related Properties","volume":"7","author":"Liu","year":"2015","journal-title":"Remote Sens."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Osco, L.P., Ramos, A.P.M., Pinheiro, M.M.F., Moriya, \u00c9.A.S., Imai, N.N., Estrabis, N., Ianczyk, F., De Ara\u00fajo, F.F., Liesenberg, V., and Jorge, L.A.D.C. (2020). A Machine Learning Framework to Predict Nutrient Content in Valencia-Orange Leaf Hyperspectral Measurements. Remote Sens., 12.","DOI":"10.3390\/rs12060906"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12898-019-0233-0","article-title":"Estimation of vegetation water content using hyperspectral vegetation indices: A comparison of crop water indicators in response to water stress treatments for summer maize","volume":"19","author":"Zhang","year":"2019","journal-title":"BMC Ecol."},{"key":"ref_73","first-page":"177","article-title":"Hyperspectral-based predictive modelling of grapevine water status in the Portuguese Douro wine region","volume":"58","author":"Costa","year":"2017","journal-title":"Int. J. Appl. Earth Obs. Geoinform."},{"key":"ref_74","unstructured":"Scialabba, N. (2000, January 28\u201331). Factors influencing organic agriculture policies with a focus on developing countries. Proceedings of the IFOAM 2000 Scientific Conference, Basel, Switzerland."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.jaridenv.2014.09.003","article-title":"Potential to monitor plant stress using remote sensing tools","volume":"113","author":"Ramoelo","year":"2015","journal-title":"J. Arid. Environ."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1007\/s11119-005-0681-8","article-title":"Future Directions of Precision Agriculture","volume":"6","author":"McBratney","year":"2005","journal-title":"Precis. Agric."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/8\/1425\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:34:17Z","timestamp":1760362457000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/8\/1425"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,7]]},"references-count":76,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2021,4]]}},"alternative-id":["rs13081425"],"URL":"https:\/\/doi.org\/10.3390\/rs13081425","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,7]]}}}