{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T15:39:11Z","timestamp":1772120351008,"version":"3.50.1"},"reference-count":127,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2024,9,12]],"date-time":"2024-09-12T00:00:00Z","timestamp":1726099200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"INRAE-EMMAH Avignon"},{"name":"Kaust university"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>A range of remote sensing platforms provide high spatial and temporal resolution insights which are useful for monitoring vegetation growth. Very few studies have focused on fruit orchards, largely due to the inherent complexity of their structure. Fruit trees are mixed with inter-rows that can be grassed or non-grassed, and there are no standard protocols for ground measurements suitable for the range of crops. The assessment of biophysical variables (BVs) for fruit orchards from optical satellites remains a significant challenge. The objectives of this study are as follows: (1) to address the challenges of extracting and better interpreting biophysical variables from optical data by proposing new ground measurements protocols tailored to various orchards with differing inter-row management practices, (2) to quantify the impact of the inter-row at the Sentinel pixel scale, and (3) to evaluate the potential of Sentinel 2 data on BVs for orchard development monitoring and the detection of key phenological stages, such as the flowering and fruit set stages. Several orchards in two pedo-climatic zones in southeast France were monitored for three years: four apricot and nectarine orchards under different management systems and nine cherry orchards with differing tree densities and inter-row surfaces. We provide the first comparison of three established ground-based methods of assessing BVs in orchards: (1) hemispherical photographs, (2) a ceptometer, and (3) the Viticanopy smartphone app. The major phenological stages, from budburst to fruit growth, were also determined by in situ annotations on the same fields monitored using Viticanopy. In parallel, Sentinel 2 images from the two study sites were processed using a Biophysical Variable Neural Network (BVNET) model to extract the main BVs, including the leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), and fraction of green vegetation cover (FCOVER). The temporal dynamics of the normalised FAPAR were analysed, enabling the detection of the fruit set stage. A new aggregative model was applied to data from hemispherical photographs taken under trees and within inter-rows, enabling us to quantify the impact of the inter-row at the Sentinel 2 pixel scale. The resulting value compared to BVs computed from Sentinel 2 gave statistically significant correlations (0.57 for FCOVER and 0.45 for FAPAR, with respective RMSE values of 0.12 and 0.11). Viticanopy appears promising for assessing the PAI (plant area index) and FCOVER for orchards with grassed inter-rows, showing significant correlations with the Sentinel 2 LAI (R2 of 0.72, RMSE 0.41) and FCOVER (R2 0.66 and RMSE 0.08). Overall, our results suggest that Sentinel 2 imagery can support orchard monitoring via indicators of development and inter-row management, offering data that are useful to quantify production and enhance resource management.<\/jats:p>","DOI":"10.3390\/rs16183393","type":"journal-article","created":{"date-parts":[[2024,9,12]],"date-time":"2024-09-12T06:31:40Z","timestamp":1726122700000},"page":"3393","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Phenological and Biophysical Mediterranean Orchard Assessment Using Ground-Based Methods and Sentinel 2 Data"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-9473-7232","authenticated-orcid":false,"given":"Pierre","family":"Rouault","sequence":"first","affiliation":[{"name":"UMR 1114 EMMAH INRAE, Avignon University, Domaine St Paul, 84914 Avignon, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4812-127X","authenticated-orcid":false,"given":"Dominique","family":"Courault","sequence":"additional","affiliation":[{"name":"UMR 1114 EMMAH INRAE, Avignon University, Domaine St Paul, 84914 Avignon, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guillaume","family":"Pouget","sequence":"additional","affiliation":[{"name":"UMR 1114 EMMAH INRAE, Avignon University, Domaine St Paul, 84914 Avignon, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fabrice","family":"Flamain","sequence":"additional","affiliation":[{"name":"UMR 1114 EMMAH INRAE, Avignon University, Domaine St Paul, 84914 Avignon, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Papa-Khaly","family":"Diop","sequence":"additional","affiliation":[{"name":"UMR 1114 EMMAH INRAE, Avignon University, Domaine St Paul, 84914 Avignon, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"V\u00e9ronique","family":"Desfonds","sequence":"additional","affiliation":[{"name":"UMR 1114 EMMAH INRAE, Avignon University, Domaine St Paul, 84914 Avignon, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9727-8281","authenticated-orcid":false,"given":"Claude","family":"Doussan","sequence":"additional","affiliation":[{"name":"UMR 1114 EMMAH INRAE, Avignon University, Domaine St Paul, 84914 Avignon, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7676-2376","authenticated-orcid":false,"given":"Andr\u00e9","family":"Chanzy","sequence":"additional","affiliation":[{"name":"UMR 1114 EMMAH INRAE, Avignon University, Domaine St Paul, 84914 Avignon, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9711-5482","authenticated-orcid":false,"given":"Marta","family":"Debolini","sequence":"additional","affiliation":[{"name":"CMCC Foundation\u2014Euro-Mediterranean Centre on Climate Change, IAFES Division, 07100 Sassari, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1279-5272","authenticated-orcid":false,"given":"Matthew","family":"McCabe","sequence":"additional","affiliation":[{"name":"Division of Biological and Environmental Sciences and Engineering, King Abdullah University of Science and Technology, Thuwal 23955-6900, Makkah, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Raul","family":"Lopez-Lozano","sequence":"additional","affiliation":[{"name":"UMR 1114 EMMAH INRAE, Avignon University, Domaine St Paul, 84914 Avignon, France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,12]]},"reference":[{"key":"ref_1","unstructured":"Cherif, S., Doblas-Miranda, E., Lionello, P., Borrego, C., Giorgi, F., Rilov, G., Iglesias, A., Jebari, S., Mahmoudi, E., and Moriondo, M. (2020). Drivers of Change. Climate and Environmental Change in the Mediterranean Basin\u2013Current Situation and Risks for the Future, Union for the Mediterranean, Plan Bleu, UNEP\/MAP. First Mediterranean Assessment Report."},{"key":"ref_2","unstructured":"MedECC (2020). Climate and Environmental Change in the Mediterranean Basin\u2013Current Situation and Risks for the Future, Union for the Mediterranean, Plan Bleu, UNEP\/MAP. First Mediterranean Assessment Report."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1051","DOI":"10.1007\/s00484-019-01719-9","article-title":"Cultural Ecosystem Services Provided by Flowering of Cherry Trees under Climate Change: A Case Study of the Relationship between the Periods of Flowering and Festivals","volume":"63","author":"Nagai","year":"2019","journal-title":"Int. J. Biometeorol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2245","DOI":"10.1007\/s00484-018-1628-x","article-title":"Climate Change Threatens Central Tunisian Nut Orchards","volume":"62","author":"Benmoussa","year":"2018","journal-title":"Int. J. Biometeorol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1016\/j.scienta.2011.07.011","article-title":"Dormancy in Temperate Fruit Trees in a Global Warming Context: A Review","volume":"130","author":"Campoy","year":"2011","journal-title":"Sci. Hortic."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1016\/j.scienta.2014.10.041","article-title":"Global Warming Impact on Floral Phenology of Fruit Trees Species in Mediterranean Region","volume":"180","author":"Malagi","year":"2014","journal-title":"Sci. Hortic."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1943","DOI":"10.1016\/j.biocon.2009.03.016","article-title":"The Impact of Climate Change on Cherry Trees and Other Species in Japan","volume":"142","author":"Primack","year":"2009","journal-title":"Biol. Conserv."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Mihailescu, E., and Bruno Soares, M. (2020). The Influence of Climate on Agricultural Decisions for Three European Crops: A Systematic Review. Front. Sustain. Food Syst., 4.","DOI":"10.3389\/fsufs.2020.00064"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.agwat.2014.04.017","article-title":"Plant-Based Sensing to Monitor Water Stress: Applicability to Commercial Orchards","volume":"142","year":"2014","journal-title":"Agric. Water Manag."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Bujdos\u00f3, G., and Hrotko, K. (2017). Cherry Production. Cherries: Botany, Production and Uses, CABI.","DOI":"10.1079\/9781780648378.0001"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Blanco, V., Blaya-Ros, P.J., Castillo, C., Soto-Vall\u00e9s, F., Torres-S\u00e1nchez, R., and Domingo, R. (2020). Potential of UAS-Based Remote Sensing for Estimating Tree Water Status and Yield in Sweet Cherry Trees. Remote Sens., 12.","DOI":"10.3390\/rs12152359"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/j.scienta.2015.05.027","article-title":"Flower Development in Sweet Cherry Framed in the BBCH Scale","volume":"192","author":"Herrero","year":"2015","journal-title":"Sci. Hortic."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Gobin, A., Sallah, A.-H.M., Curnel, Y., Delvoye, C., Weiss, M., Wellens, J., Piccard, I., Planchon, V., Tychon, B., and Goffart, J.-P. (2023). Crop Phenology Modelling Using Proximal and Satellite Sensor Data. Remote Sens., 15.","DOI":"10.3390\/rs15082090"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Guimar\u00e3es, N., Sousa, J.J., P\u00e1dua, L., Bento, A., and Couto, P. (2024). Remote Sensing Applications in Almond Orchards: A Comprehensive Systematic Review of Current Insights, Research Gaps, and Future Prospects. Appl. Sci., 14.","DOI":"10.3390\/app14051749"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1111\/1440-1703.12371","article-title":"Review: Monitoring of Land Cover Changes and Plant Phenology by Remote-Sensing in East Asia","volume":"38","author":"Shin","year":"2023","journal-title":"Ecol. Res."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"nwac290","DOI":"10.1093\/nsr\/nwac290","article-title":"Challenges and Opportunities in Remote Sensing-Based Crop Monitoring: A Review","volume":"10","author":"Wu","year":"2023","journal-title":"Natl. Sci. Rev."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1007\/s13593-021-00697-w","article-title":"STICS Crop Model and Sentinel-2 Images for Monitoring Rice Growth and Yield in the Camargue Region","volume":"41","author":"Courault","year":"2021","journal-title":"Agron. Sustain. Dev."},{"key":"ref_18","first-page":"102569","article-title":"Disaggregated PROBA-V Data Allows Monitoring Individual Crop Phenology at a Higher Observation Frequency than Sentinel-2","volume":"104","author":"Rivas","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"120678","DOI":"10.1016\/j.jenvman.2024.120678","article-title":"Impacts of Mining on Vegetation Phenology and Sensitivity Assessment of Spectral Vegetation Indices to Mining Activities in Arid\/Semi-Arid Areas","volume":"356","author":"Sun","year":"2024","journal-title":"J. Environ. Manag."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"4643","DOI":"10.1080\/01431160802632249","article-title":"Multi-Year Monitoring of Rice Crop Phenology through Time Series Analysis of MODIS Images","volume":"30","author":"Boschetti","year":"2009","journal-title":"Int. J. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.agwat.2005.02.013","article-title":"Monitoring Wheat Phenology and Irrigation in Central Morocco: On the Use of Relationships between Evapotranspiration, Crops Coefficients, Leaf Area Index and Remotely-Sensed Vegetation Indices","volume":"97","author":"Duchemin","year":"2006","journal-title":"Agric. Water Manag."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Nasrallah, A., Baghdadi, N., El Hajj, M., Darwish, T., Belhouchette, H., Faour, G., Darwich, S., and Mhawej, M. (2019). Sentinel-1 Data for Winter Wheat Phenology Monitoring and Mapping. Remote Sens., 11.","DOI":"10.3390\/rs11192228"},{"key":"ref_23","first-page":"102172","article-title":"Characterizing Spring Phenology of Temperate Broadleaf Forests Using Landsat and Sentinel-2 Time Series","volume":"92","author":"Kowalski","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_24","unstructured":"Neale, C.M., and Maltese, A. (2021, January 13\u201317). Characterising the Spring and Autumn Land Surface Phenology of Macaronesian Species Using Sentinel-2 Data: The Case of Canary Island. Proceedings of the Remote Sensing for Agriculture, Ecosystems, and Hydrology XXIII, Online."},{"key":"ref_25","first-page":"267","article-title":"Sentinel-2 Time Series: A Promising Tool in Monitoring Temperate Species Spring Phenology","volume":"97","year":"2024","journal-title":"For. Int. J. For. Res."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Misra, G., Cawkwell, F., and Wingler, A. (2020). Status of Phenological Research Using Sentinel-2 Data: A Review. Remote Sens., 12.","DOI":"10.3390\/rs12172760"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1037","DOI":"10.5194\/isprs-archives-XLI-B8-1037-2016","article-title":"Time Series Analysis of Remote Sensing Observations for Citrus Crop Growth Stage and Evapotranspiration Estimation","volume":"XLI-B8","author":"Sawant","year":"2016","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Abubakar, M.A., Chanzy, A., Flamain, F., and Courault, D. (2023). Characterisation of Grapevine Canopy Leaf Area and Inter-Row Management Using Sentinel-2 Time Series. OENO One, 57.","DOI":"10.20870\/oeno-one.2023.57.4.7703"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Rao, P., Zhou, W., Bhattarai, N., Srivastava, A.K., Singh, B., Poonia, S., Lobell, D.B., and Jain, M. (2021). Using Sentinel-1, Sentinel-2, and Planet Imagery to Map Crop Type of Smallholder Farms. Remote Sens., 13.","DOI":"10.3390\/rs13101870"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1016\/j.rse.2019.01.031","article-title":"Chlorophyll Content Estimation in an Open-Canopy Conifer Forest with Sentinel-2A and Hyperspectral Imagery in the Context of Forest Decline","volume":"223","author":"Hornero","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"739","DOI":"10.1029\/2018RG000608","article-title":"An Overview of Global Leaf Area Index (LAI): Methods, Products, Validation, and Applications","volume":"57","author":"Fang","year":"2019","journal-title":"Rev. Geophys."},{"key":"ref_32","first-page":"429","article-title":"Principles of Environmental Physics","volume":"13","author":"Monteith","year":"1973","journal-title":"Agric. Meteorol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"111402","DOI":"10.1016\/j.rse.2019.111402","article-title":"Remote Sensing for Agricultural Applications: A Meta-Review","volume":"236","author":"Weiss","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_34","unstructured":"Weiss, M., Baret, F., and Jay, S. (2024, August 02). S2ToolBox Level 2 Products LAI, FAPAR, FCOVER 2.0. Available online: https:\/\/step.esa.int\/docs\/extra\/ATBD_S2ToolBox_V2.1.pdf."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1307","DOI":"10.1016\/j.agrformet.2009.03.001","article-title":"Optimal Geometric Configuration and Algorithms for LAI Indirect Estimates under Row Canopies: The Case of Vineyards","volume":"149","author":"Baret","year":"2009","journal-title":"Agric. For. Meteorol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/S0034-4257(99)00056-5","article-title":"Direct and Indirect Estimation of Leaf Area Index, fAPAR, and Net Primary Production of Terrestrial Ecosystems","volume":"70","author":"Gower","year":"1999","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1301","DOI":"10.1080\/01431169108929727","article-title":"Satellite Remote Sensing of Primary Production: Comparison of Results for Sahelian Grasslands 1981\u20131988","volume":"12","author":"Prince","year":"1991","journal-title":"Int. J. Remote Sens."},{"key":"ref_38","first-page":"77","article-title":"Tracking the Impact of Climate Factors on Vegetation Dynamics across the Alashan Plateau Semi Desert Ecoregion","volume":"14","author":"Shobairi","year":"2024","journal-title":"Comput. Ecol. Softw."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Belda, S., Pipia, L., and Verrelst, J. (2024). Trends in Satellite Time Series Processing for Vegetation Phenology Monitoring. Multitemporal Earth Observation Image Analysis, John Wiley & Sons, Ltd.","DOI":"10.1002\/9781394306657.ch5"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"102489","DOI":"10.1016\/j.ecoinf.2024.102489","article-title":"Multi-Sensor High Spatial Resolution Leaf Area Index Estimation by Combining Surface Reflectance with Vegetation Indices for Highly Heterogeneous Regions: A Case Study of the Chishui River Basin in Southwest China","volume":"80","author":"Han","year":"2024","journal-title":"Ecol. Inform."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Camacho, F., Mart\u00ednez-S\u00e1nchez, E., Brown, L.A., Morris, H., Morrone, R., Williams, O., Dash, J., Origo, N., S\u00e1nchez-Zapero, J., and Boccia, V. (2024). Validation and Conformity Testing of Sentinel-3 Green Instantaneous FAPAR and Canopy Chlorophyll Content Products. Remote Sens., 16.","DOI":"10.3390\/rs16152698"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.agrformet.2015.02.021","article-title":"Towards Regional Grain Yield Forecasting with 1km-Resolution EO Biophysical Products: Strengths and Limitations at Pan-European Level","volume":"206","author":"Duveiller","year":"2015","journal-title":"Agric. For. Meteorol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.rse.2013.07.018","article-title":"Assimilation of Remotely Sensed Soil Moisture and Vegetation with a Crop Simulation Model for Maize Yield Prediction","volume":"138","author":"Ines","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.isprsjprs.2023.03.020","article-title":"Review of Ground and Aerial Methods for Vegetation Cover Fraction (fCover) and Related Quantities Estimation: Definitions, Advances, Challenges, and Future Perspectives","volume":"199","author":"Li","year":"2023","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"13","DOI":"10.20870\/oeno-one.2009.43.1.806","article-title":"Modelling Soil Water Content and Grapevine Growth and Development with the Stics Crop-Soil Model under Two Different Water Management Strategies","volume":"43","author":"Celette","year":"2009","journal-title":"OENO One"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1051\/agro:2002036","article-title":"Validation d\u2019une M\u00e9thode Bas\u00e9e Sur l\u2019utilisation de R\u00e9seaux de Neurones Pour l\u2019estimation de Variables Biophysiques Des Couverts V\u00e9g\u00e9taux \u00e0 Partir de Donn\u00e9es de T\u00e9l\u00e9d\u00e9tection","volume":"22","author":"Weiss","year":"2002","journal-title":"Agronomie"},{"key":"ref_47","unstructured":"Kalaitzidis, C., Heinzel, V., and Zianis, D. (2010). A Review of Multispectral Vegetation Indices for Biomass Estimation. Imagin[e,g] Europe, IOS Press."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Odi-Lara, M., Campos, I., Neale, C.M.U., Ortega-Far\u00edas, S., Poblete-Echeverr\u00eda, C., Balbont\u00edn, C., and Calera, A. (2016). Estimating Evapotranspiration of an Apple Orchard Using a Remote Sensing-Based Soil Water Balance. Remote Sens., 8.","DOI":"10.3390\/rs8030253"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"2373","DOI":"10.3390\/rs70302373","article-title":"Estimation of Actual Crop Coefficients Using Remotely Sensed Vegetation Indices and Soil Water Balance Modelled Data","volume":"7","author":"Paredes","year":"2015","journal-title":"Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Dong, X., Zhang, Z., Yu, R., Tian, Q., and Zhu, X. (2020). Extraction of Information about Individual Trees from High-Spatial-Resolution UAV-Acquired Images of an Orchard. Remote Sens., 12.","DOI":"10.3390\/rs12010133"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"112333","DOI":"10.1016\/j.scienta.2023.112333","article-title":"Using Remote Sensing to Identify Individual Tree Species in Orchards: A Review","volume":"321","author":"Ok","year":"2023","journal-title":"Sci. Hortic."},{"key":"ref_52","unstructured":"Park, S., Nolan, A., Ryu, D., Fuentes, S., Hernandez, E., Chung, H., and O\u2019Connell, M. (December, January 29). Estimation of Crop Water Stress in a Nectarine Orchard Using High-Resolution Imagery from Unmanned Aerial Vehicle (UAV). Proceedings of the 21st International Congress on Modelling and Simulation, Gold Coast, QLD, Australia."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Houborg, R., and McCabe, M. (2015, January 21\u201324). Application of a Regularized Model Inversion System (REGFLEC) to Multi-Temporal RapidEye Imagery for Retrieving Vegetation Characteristics. Proceedings of the SPIE Remote Sensing Conference, Toulouse, France.","DOI":"10.1117\/12.2196378"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"2403","DOI":"10.1093\/jxb\/erg263","article-title":"Ground-based Measurements of Leaf Area Index: A Review of Methods, Instruments and Current Controversies","volume":"54","year":"2003","journal-title":"J. Exp. Bot."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.agrformet.2003.08.027","article-title":"Review of Methods for in Situ Leaf Area Index Determination: Part I. Theories, Sensors and Hemispherical Photography","volume":"121","author":"Jonckheere","year":"2004","journal-title":"Agric. For. Meteorol."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.agrformet.2003.08.001","article-title":"Review of Methods for in Situ Leaf Area Index (LAI) Determination: Part II. Estimation of LAI, Errors and Sampling","volume":"121","author":"Weiss","year":"2004","journal-title":"Agric. For. Meteorol."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"644","DOI":"10.1016\/j.agrformet.2007.11.015","article-title":"Estimation of Leaf Area and Clumping Indexes of Crops with Hemispherical Photographs","volume":"148","author":"Demarez","year":"2008","journal-title":"Agric. For. Meteorol."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1051\/agro:19970602","article-title":"Ground Cover and Leaf Area Index of Maize and Sugar Beet Crops","volume":"17","author":"Andrieu","year":"1997","journal-title":"Agronomie"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Jackson, J.E. (2003). The Biology of Apples and Pears, Cambridge University Press.","DOI":"10.1017\/CBO9780511542657"},{"key":"ref_60","unstructured":"Steiner, M., Magyar, L., Gueviki, M., and Hrotk\u00f3, K. (2015). Optimization of Light Interception in Intensive Sweet Cherry Orchard. Horticulture, LIX, Available online: https:\/\/horticulturejournal.usamv.ro\/pdf\/2015\/art17.pdf."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Pokovai, K., and Fodor, N. (2019). Adjusting Ceptometer Data to Improve Leaf Area Index Measurements. Agronomy, 9.","DOI":"10.3390\/agronomy9120866"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1080\/02757259009532119","article-title":"Characterizing Plant Canopies with Hemispherical Photographs","volume":"5","author":"Rich","year":"1990","journal-title":"Remote Sens. Rev."},{"key":"ref_63","unstructured":"Weiss, M., Baret, F., de Solan, B., and Demarez, V. (2008). CAN-EYE, logiciel de traitement d\u2019images pour l\u2019estimation de l\u2019indice foliaire. Cah. Tech. L\u2019inra, 159, Available online: https:\/\/hal.science\/hal-01496819\/document."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"De Bei, R., Fuentes, S., Gilliham, M., Tyerman, S., Edwards, E., Bianchini, N., Smith, J., and Collins, C. (2016). VitiCanopy: A Free Computer App to Estimate Canopy Vigor and Porosity for Grapevine. Sensors, 16.","DOI":"10.3390\/s16040585"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"8542","DOI":"10.1080\/01431161.2021.1980241","article-title":"Retrieval of Olive Tree Biophysical Properties from Sentinel-2 Time Series Based on Physical Modelling and Machine Learning Technique","volume":"42","author":"Makhloufi","year":"2021","journal-title":"Int. J. Remote Sens."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"716","DOI":"10.1111\/avsc.12181","article-title":"Estimating Leaf Area Index in Tree Species Using the PocketLAI Smart App","volume":"18","author":"Orlando","year":"2015","journal-title":"Appl. Veg. Sci."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"2860","DOI":"10.3390\/s150202860","article-title":"Digital Cover Photography for Estimating Leaf Area Index (LAI) in Apple Trees Using a Variable Light Extinction Coefficient","volume":"15","author":"Fuentes","year":"2015","journal-title":"Sensors"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.foreco.2012.12.017","article-title":"An Assessment of Ground Reference Methods for Estimating LAI of Boreal Forests","volume":"292","author":"Majasalmi","year":"2013","journal-title":"For. Ecol. Manag."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"108763","DOI":"10.1016\/j.agwat.2024.108763","article-title":"High-Resolution Satellite Imagery to Assess Orchard Characteristics Impacting Water Use","volume":"295","author":"Rouault","year":"2024","journal-title":"Agric. Water Manag."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"109011","DOI":"10.1016\/j.scienta.2019.109011","article-title":"Climate Change Impact on Phenological Stages of Sweet and Sour Cherry Trees in a Continental Climate Environment","volume":"261","author":"Paltineanu","year":"2020","journal-title":"Sci. Hortic."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"281","DOI":"10.2307\/3237150","article-title":"Plant Competition in Mediterranean-type Vegetation","volume":"10","author":"Sardans","year":"1999","journal-title":"J. Veg. Sci."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Abubakar, M.A., Chanzy, A., Flamain, F., Pouget, G., and Courault, D. (2023). Delineation of Orchard, Vineyard, and Olive Trees Based on Phenology Metrics Derived from Time Series of Sentinel-2. Remote Sens., 15.","DOI":"10.3390\/rs15092420"},{"key":"ref_73","unstructured":"Mohammed, G. (2017). Mod\u00e9lisation Biog\u00e9ochimique du Syst\u00e8me \u201cIrrigation-Sol-Plante-Nappe\u201d: Application \u00e0 la Durabilit\u00e9 du Syst\u00e8me de Culture du foin de Crau. [Doctoral Thesis, Universit\u00e9 d\u2019Avignon]."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"590","DOI":"10.1016\/j.jenvman.2016.07.002","article-title":"The PRECOS Framework: Measuring the Impacts of the Global Changes on Soils, Water, Agriculture on Territories to Better Anticipate the Future","volume":"181","author":"Trolard","year":"2016","journal-title":"J. Environ. Manag."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"691","DOI":"10.1016\/j.proenv.2013.06.078","article-title":"Modelling of Drainage and Hay Production over the Crau Aquifer for Analysing Impact of Global Change on Aquifer Recharge","volume":"19","author":"Olioso","year":"2013","journal-title":"Procedia Environ. Sci."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"416","DOI":"10.1016\/j.rse.2019.03.020","article-title":"Validation of the Sentinel Simplified Level 2 Product Prototype Processor (SL2P) for Mapping Cropland Biophysical Variables Using Sentinel-2\/MSI and Landsat-8\/OLI Data","volume":"225","author":"Djamai","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1016\/S0034-4257(99)00045-0","article-title":"Evaluation of Canopy Biophysical Variable Retrieval Performances from the Accumulation of Large Swath Satellite Data","volume":"70","author":"Weiss","year":"1999","journal-title":"Remote Sens. Environ."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1016\/j.rse.2013.07.027","article-title":"Validation of Coarse Spatial Resolution LAI and FAPAR Time Series over Cropland in Southwest France","volume":"139","author":"Claverie","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/j.rse.2018.06.037","article-title":"Retrieval of the Canopy Chlorophyll Content from Sentinel-2 Spectral Bands to Estimate Nitrogen Uptake in Intensive Winter Wheat Cropping Systems","volume":"216","author":"Delloye","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"114309","DOI":"10.1016\/j.rse.2024.114309","article-title":"Physics-constrained deep learning for biophysical parameter retrieval from Sentinel-2 images: Inversion of the PROSAIL model","volume":"312","author":"Valero","year":"2024","journal-title":"Remote Sens. Environ."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"3631","DOI":"10.1021\/ac034173t","article-title":"A Perfect Smoother","volume":"75","author":"Eilers","year":"2003","journal-title":"Anal. Chem."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"123","DOI":"10.20870\/oeno-one.2020.54.1.2481","article-title":"Using Smartphone Leaf Area Index Data Acquired in a Collaborative Context within Vineyards in Southern France","volume":"54","author":"Pichon","year":"2020","journal-title":"OENO One"},{"key":"ref_83","first-page":"165","article-title":"Assessment of Canopy Vigor Information from Kiwifruit Plants Based on a Digital Surface Model from Unmanned Aerial Vehicle Imagery","volume":"12","author":"Xue","year":"2019","journal-title":"Int. J. Agric. Biol. Eng."},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Fuentes, S., Chacon, G., Torrico, D.D., Zarate, A., and Gonzalez Viejo, C. (2019). Spatial Variability of Aroma Profiles of Cocoa Trees Obtained through Computer Vision and Machine Learning Modelling: A Cover Photography and High Spatial Remote Sensing Application. Sensors, 19.","DOI":"10.20944\/preprints201904.0316.v1"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"183","DOI":"10.17660\/ActaHortic.2019.1235.24","article-title":"Canopy Architecture Assessment of Cherry Trees by Cover Photography Based on Variable Light Extinction Coefficient Modelled Using Artificial Neural Networks","volume":"1235","author":"Tongson","year":"2019","journal-title":"Acta Hortic."},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Ilniyaz, O., Kurban, A., and Du, Q. (2022). Leaf Area Index Estimation of Pergola-Trained Vineyards in Arid Regions Based on UAV RGB and Multispectral Data Using Machine Learning Methods. Remote Sens., 14.","DOI":"10.3390\/rs14020415"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1111\/j.1744-7348.1998.tb05842.x","article-title":"Interactions between Wheat (Triticum aestivum L.) Cultivar, Row Spacing and Density and the Effect on Weed Suppression and Crop Yield","volume":"133","author":"Champion","year":"1998","journal-title":"Ann. Appl. Biol."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.fcr.2013.09.024","article-title":"Comparison of Leaf Area Index Estimates by Ceptometer and PocketLAI Smart App in Canopies with Different Structures","volume":"155","author":"Francone","year":"2014","journal-title":"Field Crops Res."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"724","DOI":"10.1139\/x04-213","article-title":"Estimating Forest Canopy Bulk Density Using Six Indirect Methods","volume":"35","author":"Keane","year":"2005","journal-title":"Can. J. For. Res."},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Lopes, D., Nunes, L., Walford, N., Aranha, J., Sette, C.J., Viana, H., and Hernandez, C. (2014). A Simplified Methodology for the Correction of Leaf Area Index (LAI) Measurements Obtained by Ceptometer with Reference to Pinus Portuguese Forests. iFor.-Biogeosci. For., 7.","DOI":"10.3832\/ifor0096-007"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"272","DOI":"10.21273\/HORTSCI.30.2.272","article-title":"Comparison of Four Methods for Estimating Total Light Interception by Apple Trees of Varying Forms","volume":"30","author":"Lakso","year":"1995","journal-title":"HortScience"},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1111\/ajgw.12005","article-title":"Comparison of Different Protocols for Indirect Measurement of Leaf Area Index with Ceptometers in Vertically Trained Vineyards","volume":"19","author":"Casterad","year":"2013","journal-title":"Aust. J. Grape Wine Res."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1017\/S0014479702003083","article-title":"Canopy characteristics of contrasting clones of cacao (Theobroma cacao)","volume":"38","author":"Daymond","year":"2002","journal-title":"Exp. Agric."},{"key":"ref_94","unstructured":"Louarn, G. (2005). Analyse et Mod\u00e9lisation de l\u2019organogen\u00e8se et de l\u2019architecture Du Rameau de Vigne (Vitis vinifera L.). [Ph.D. Thesis]. Available online: https:\/\/www.researchgate.net\/publication\/358146863_Analyse_et_modelisation_de_l%27organogenese_et_de_l%27architecture_du_rameau_de_vigne_Vitis_vinifera_L."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1146\/annurev.pp.19.060168.001235","article-title":"Transpiration and Leaf Temperature","volume":"19","author":"Gates","year":"1968","journal-title":"Annu. Rev. Plant. Physiol."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"341","DOI":"10.2307\/2402436","article-title":"Interception of Light by Model Hedgerow Orchards in Relation to Latitude, Time of Year and Hedgerow Configuration and Orientation","volume":"9","author":"Jackson","year":"1972","journal-title":"J. Appl. Ecol."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"935","DOI":"10.1080\/14620316.1995.11515369","article-title":"Light Distribution in Apple Orchard Systems in Relation to Production and Fruit Quality","volume":"70","author":"Wagenmakers","year":"1995","journal-title":"J. Hortic. Sci."},{"key":"ref_98","first-page":"141","article-title":"Ph\u00e4nologische Entwicklungsstadien Des Kernobstes (Malus Domestica Borkh. und Pyrus communis L.), Des Steinobstes (Prunus-Arten), Der Johannisbeere (Ribes-Arten) und Der Erdbeere (Fragaria \u00d7 Ananassa Duch.)","volume":"46","author":"Meier","year":"1994","journal-title":"Heft 7"},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Parker, B.L., Skinner, M., and Lewis, T. (1995). Western Flower Thrips in Peach Orchards in France. Thrips Biology and Management, Springer.","DOI":"10.1007\/978-1-4899-1409-5"},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"426","DOI":"10.2503\/hortj.OKD-052","article-title":"Chilling Requirements and Blooming Dates of Leading Peach Cultivars and a Promising Early Maturing Peach Selection, Momo Tsukuba 127","volume":"86","author":"Sawamura","year":"2017","journal-title":"Hortic. J."},{"key":"ref_101","first-page":"83","article-title":"\u00c9valuation ph\u00e9nologique et pomologique d\u2019une collection vari\u00e9tale de cerisiers en conditions de moyenne altitude au Maroc","volume":"55","author":"Oukabli","year":"2000","journal-title":"Fruits"},{"key":"ref_102","first-page":"167","article-title":"D\u00e9roulement d\u2019un Cycle V\u00e9g\u00e9tatif de Jeunes Plants de Cerisiers (Hybrides Prunus cerasus \u00d7 Prunus avium)","volume":"26","author":"Rejeb","year":"2011","journal-title":"Pr\u00e9misses D\u2019am\u00e9lior. Vigueur"},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1016\/j.agrformet.2018.11.033","article-title":"Review of Indirect Optical Measurements of Leaf Area Index: Recent Advances, Challenges, and Perspectives","volume":"265","author":"Yan","year":"2019","journal-title":"Agric. For. Meteorol."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1016\/j.scienta.2013.10.009","article-title":"Canopy Leaf Area Index for Apple Tree Using Hemispherical Photography in Arid Region","volume":"164","author":"Liu","year":"2013","journal-title":"Sci. Hortic."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"716","DOI":"10.1016\/j.rse.2008.11.014","article-title":"Albedo and LAI Estimates from FORMOSAT-2 Data for Crop Monitoring","volume":"113","author":"Bsaibes","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"113259","DOI":"10.1016\/j.rse.2022.113259","article-title":"Correcting for the Clumping Effect in Leaf Area Index Calculations Using One-Dimensional Fractal Dimension","volume":"281","author":"Lai","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"uhad018","DOI":"10.1093\/hr\/uhad018","article-title":"Characteristics of Photosynthesis and Vertical Canopy Architecture of Citrus Trees under Two Labor-Saving Cultivation Modes Using UAV-Based LiDAR Data in Citrus Orchards","volume":"10","author":"Dian","year":"2023","journal-title":"Hortic. Res."},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"108423","DOI":"10.1016\/j.agwat.2023.108423","article-title":"Evapotranspiration, Gross Primary Productivity and Water Use Efficiency over a High-Density Olive Orchard Using Ground and Satellite Based Data","volume":"287","author":"Elfarkh","year":"2023","journal-title":"Agric. Water Manag."},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/j.agwat.2018.01.011","article-title":"Water Status, Gas Exchange and Crop Performance in a Super High Density Olive Orchard under Deficit Irrigation Scheduled from Leaf Turgor Measurements","volume":"202","author":"Fernandes","year":"2018","journal-title":"Agric. Water Manag."},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"196","DOI":"10.55779\/ng42196","article-title":"Monitoring Flowering Phenology of Apple Trees Using Remote Sensing Techniques","volume":"4","year":"2024","journal-title":"Nova Geod."},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"631","DOI":"10.5194\/isprs-archives-XLII-2-W13-631-2019","article-title":"Automatic apple tree blossom estimation from uav rgb imagery","volume":"XLII-2-W13","author":"Valente","year":"2019","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_112","doi-asserted-by":"crossref","unstructured":"Horton, R., Cano, E., Bulanon, D., and Fallahi, E. (2017). Peach Flower Monitoring Using Aerial Multispectral Imaging. J. Imaging, 3.","DOI":"10.3390\/jimaging3010002"},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.isprsjprs.2019.08.006","article-title":"An Enhanced Bloom Index for Quantifying Floral Phenology Using Multi-Scale Remote Sensing Observations","volume":"156","author":"Chen","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_114","doi-asserted-by":"crossref","unstructured":"Torgbor, B.A., Rahman, M.M., Robson, A., Brinkhoff, J., and Khan, A. (2022). Assessing the Potential of Sentinel-2 Derived Vegetation Indices to Retrieve Phenological Stages of Mango in Ghana. Horticulturae, 8.","DOI":"10.3390\/horticulturae8010011"},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1016\/j.rse.2017.07.015","article-title":"Understanding the Temporal Behavior of Crops Using Sentinel-1 and Sentinel-2-like Data for Agricultural Applications","volume":"199","author":"Veloso","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"383","DOI":"10.17660\/ActaHortic.2000.537.45","article-title":"Potential Use of Infra-Red Thermometry for the Detection of Water Stress in Apple Trees","volume":"537","author":"Giuliani","year":"2000","journal-title":"Acta Hortic."},{"key":"ref_117","first-page":"255","article-title":"Some Contributions of Remote Sensing for Orchard Irrigation Scheduling Resulting from the TELERIEG Research Program in the South-West of France","volume":"1038","author":"Regnard","year":"2014","journal-title":"Acta Hortic."},{"key":"ref_118","doi-asserted-by":"crossref","unstructured":"McCabe, M., Miralles, D., Holmes, T., and Fisher, J. (2023, July 04). Advances in the Remote Sensing of Terrestrial Evaporation. Available online: https:\/\/www.mdpi.com\/2072-4292\/11\/9\/1138.","DOI":"10.3390\/rs11091138"},{"key":"ref_119","doi-asserted-by":"crossref","unstructured":"Roujean, J.-L., Bhattacharya, B., Gamet, P., Pandya, M.R., Boulet, G., Olioso, A., Singh, S.K., Shukla, M.V., Mishra, M., and Briottet, X. (2021, January 6\u201310). TRISHNA: An Indo-French Space Mission to Study the Thermography of the Earth at Fine Spatio-Temporal Resolution. Proceedings of the 2021 IEEE International India Geoscience and Remote Sensing Symposium (InGARSS), Ahmedabad, India.","DOI":"10.1109\/InGARSS51564.2021.9791925"},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"409","DOI":"10.1051\/agro:2004031","article-title":"Adaptation of the Crop Model STICS to Intercropping. Theoretical Basis and Parameterisation","volume":"24","author":"Brisson","year":"2004","journal-title":"Agronomie"},{"key":"ref_121","unstructured":"Garcia de Cortazar Atauri, I. (2006). Adaptation Du Mod\u00e8le STICS \u00e0 La Vigne (Vitis vinifera L.): Utilisation Dans Le Cadre d\u2019une \u00c9tude d\u2019impact Du Changement Climatique \u00e0 l\u2019\u00e9chelle de La France. [Doctoral Thesis, \u00c9cole nationale sup\u00e9rieure agronomique (Montpellier)]."},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.eja.2018.01.009","article-title":"Analyzing Ecosystem Services in Apple Orchards Using the STICS Model","volume":"94","author":"Demestihas","year":"2018","journal-title":"Eur. J. Agron."},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"1731","DOI":"10.5194\/hess-14-1731-2010","article-title":"Combined Use of FORMOSAT-2 Images with a Crop Model for Biomass and Water Monitoring of Permanent Grassland in Mediterranean Region","volume":"14","author":"Courault","year":"2010","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"108810","DOI":"10.1016\/j.agwat.2024.108810","article-title":"Responses of Leaf Nitrogen Status and Leaf Area Index to Water and Nitrogen Application and Their Relationship with Apple Orchard Productivity","volume":"296","author":"Sun","year":"2024","journal-title":"Agric. Water Manag."},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.agee.2017.09.030","article-title":"Management of Service Crops for the Provision of Ecosystem Services in Vineyards: A Review","volume":"251","author":"Garcia","year":"2018","journal-title":"Agric. Ecosyst. Environ."},{"key":"ref_126","first-page":"325","article-title":"Effects of orchard grass on soil fertility and apple tree nutrition","volume":"26","author":"Yang","year":"2020","journal-title":"J. Plant Nutr. Fertil."},{"key":"ref_127","doi-asserted-by":"crossref","first-page":"100191","DOI":"10.1016\/j.kjs.2024.100191","article-title":"Early Detection of Bactrocera Zonata Infestation in Peach Fruit Using Remote Sensing Technique and Application of Nematodes for Its Control","volume":"51","author":"Heikal","year":"2024","journal-title":"Kuwait J. Sci."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/18\/3393\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:54:43Z","timestamp":1760111683000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/18\/3393"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,12]]},"references-count":127,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["rs16183393"],"URL":"https:\/\/doi.org\/10.3390\/rs16183393","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,12]]}}}