{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T10:45:31Z","timestamp":1770893131737,"version":"3.50.1"},"reference-count":63,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2022,10,8]],"date-time":"2022-10-08T00:00:00Z","timestamp":1665187200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Food and Agriculture of United Nations (FAO)","award":["RYC-2019-027818-I"],"award-info":[{"award-number":["RYC-2019-027818-I"]}]},{"name":"Food and Agriculture of United Nations (FAO)","award":["CA17134"],"award-info":[{"award-number":["CA17134"]}]},{"DOI":"10.13039\/501100003329","name":"Ministerio de Ciencia e Innovaci\u00f3n, Spain","doi-asserted-by":"publisher","award":["RYC-2019-027818-I"],"award-info":[{"award-number":["RYC-2019-027818-I"]}],"id":[{"id":"10.13039\/501100003329","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003329","name":"Ministerio de Ciencia e Innovaci\u00f3n, Spain","doi-asserted-by":"publisher","award":["CA17134"],"award-info":[{"award-number":["CA17134"]}],"id":[{"id":"10.13039\/501100003329","id-type":"DOI","asserted-by":"publisher"}]},{"name":"COST Action","award":["RYC-2019-027818-I"],"award-info":[{"award-number":["RYC-2019-027818-I"]}]},{"name":"COST Action","award":["CA17134"],"award-info":[{"award-number":["CA17134"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The second United Nations Sustainable Development Goal (SDG2), zero hunger, aims to improve the productivity, food security, nutrition, and sustainability of small-scale farmers. The fall armyworm (FAW, Spodoptera frugiperda) has been devasting to smallholder farmer food security since it spread to sub-Saharan Africa in 2016, who have suffered massive crop losses, particularly maize, an important staple for basic sustenance. Since the FAW mainly devours green leaf biomass during the maize vegetative growth stage, the implementation of remote sensing technologies offers opportunities for monitoring the FAW. Here, we developed and tested a Sentinel 2 a+b satellite-based monitoring algorithm based on optimized first-derivative NDVI time series analysis using Google Earth Engine. For validation, we first employed the FAO Fall Armyworm Monitoring and Early Warning System (FAMEWS) mobile app data from Kenya, and then subsequently conducted field validation campaigns in Zimbabwe, Kenya, and Tanzania. Additionally, we directly observed loss of green biomass during maize vegetative growth stages caused by the FAW, confirming the observed signals of loss of the leaf area index (LAI) and the total green biomass (via the NDVI). Preliminary analyses suggested that satellite monitoring of small-scale farmer fields at the regional level may be possible with an NDVI first-derivative time series anomaly analysis using ESA Sentinel 2 a+b (R2 = 0.81). Commercial nanosatellite constellations, such as PlanetScope, were also explored, which may offer benefits from greater spatial resolution and return interval frequency. Due to other confounding factors, such as clouds, intercropping, weeds, abiotic stresses, or even other biotic pests (e.g., locusts), validation results were mixed. Still, maize biomass anomaly detection for monitoring the FAW using satellite data could help confirm the presence of the FAW with the help of expanded field-based monitoring through the FAO FAMEWS app.<\/jats:p>","DOI":"10.3390\/rs14195003","type":"journal-article","created":{"date-parts":[[2022,10,10]],"date-time":"2022-10-10T03:07:28Z","timestamp":1665371248000},"page":"5003","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Regional Monitoring of Fall Armyworm (FAW) Using Early Warning Systems"],"prefix":"10.3390","volume":"14","author":[{"given":"Ma. Luisa","family":"Buchaillot","sequence":"first","affiliation":[{"name":"Integrative Crop Ecophysiology Group, Plant Physiology Section, Faculty of Biology, University of Barcelona, 08028 Barcelona, Spain"},{"name":"AGROTECNIO (Center for Research in Agrotechnology), Av. Rovira Roure 191, 25198 Lleida, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2735-3485","authenticated-orcid":false,"given":"Jill","family":"Cairns","sequence":"additional","affiliation":[{"name":"CIMMYT Western Africa Regional Office, Harare P.O. Box MP163, Zimbabwe"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6929-0083","authenticated-orcid":false,"given":"Esnath","family":"Hamadziripi","sequence":"additional","affiliation":[{"name":"CIMMYT Western Africa Regional Office, Harare P.O. Box MP163, Zimbabwe"}]},{"given":"Kenneth","family":"Wilson","sequence":"additional","affiliation":[{"name":"Lancaster Environment Centre, Lancaster University, Lancaster LA1 4YQ, UK"}]},{"given":"David","family":"Hughes","sequence":"additional","affiliation":[{"name":"Department of Entomology & Biology, Penn State University, University Park, PA 16801, USA"}]},{"given":"John","family":"Chelal","sequence":"additional","affiliation":[{"name":"Department of Agriculture and Biotechnology, Moi University, Eldoret 3900-30100, Kenya"}]},{"given":"Peter","family":"McCloskey","sequence":"additional","affiliation":[{"name":"Department of Entomology & Biology, Penn State University, University Park, PA 16801, USA"}]},{"given":"Annalyse","family":"Kehs","sequence":"additional","affiliation":[{"name":"Department of Entomology & Biology, Penn State University, University Park, PA 16801, USA"}]},{"given":"Nicholas","family":"Clinton","sequence":"additional","affiliation":[{"name":"Google, 1600 Amphitheatre Pkwy, Mountain View, CA 94043, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8866-2388","authenticated-orcid":false,"given":"Jos\u00e9 Luis","family":"Araus","sequence":"additional","affiliation":[{"name":"Integrative Crop Ecophysiology Group, Plant Physiology Section, Faculty of Biology, University of Barcelona, 08028 Barcelona, Spain"},{"name":"AGROTECNIO (Center for Research in Agrotechnology), Av. Rovira Roure 191, 25198 Lleida, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1687-1965","authenticated-orcid":false,"given":"Shawn C.","family":"Kefauver","sequence":"additional","affiliation":[{"name":"Integrative Crop Ecophysiology Group, Plant Physiology Section, Faculty of Biology, University of Barcelona, 08028 Barcelona, Spain"},{"name":"AGROTECNIO (Center for Research in Agrotechnology), Av. Rovira Roure 191, 25198 Lleida, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Brown, M.E. (2021). Metrics to accelerate private sector investment in sustainable development goal 2\u2014Zero hunger. Sustainability, 13.","DOI":"10.3390\/su13115967"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"100327","DOI":"10.1016\/j.gfs.2019.100327","article-title":"Maize agro-food systems to ensure food and nutrition security in reference to the Sustainable Development Goals","volume":"25","author":"Tanumihardjo","year":"2020","journal-title":"Glob. Food Secur."},{"key":"ref_3","unstructured":"Prasanna, B., Huesing, J.E., Eddy, R., and Peschke, V.M. (2018). Fall Armyworm in Africa: A Guide for Integrated Pest Management, CIMMYT. [1st ed.]."},{"key":"ref_4","unstructured":"FAOSTAT (2016). The State of Food and Agriculture 2016 (SOFA): Climate Change, Agriculture and Food Security, FAO."},{"key":"ref_5","first-page":"1","article-title":"Fall armyworm (Spodoptera frugiperda) management by smallholders","volume":"14","author":"Hruska","year":"2019","journal-title":"CAB Rev. Perspect. Agric. Vet. Sci. Nutr. Nat. Resour."},{"key":"ref_6","unstructured":"FAO (2020). The Global Action for Fall Armyworm Control: Action Framework 2020\u20132022, FAO."},{"key":"ref_7","first-page":"920","article-title":"Yield losses in maize (Zea mays) due to fall armyworm infestation and potential IoT-based interventions for its control","volume":"7","author":"Balla","year":"2019","journal-title":"J. Entomol. Zool. Stud."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1471","DOI":"10.1093\/jee\/91.6.1471","article-title":"Factors Associated with Resistance to Fall Armyworm (Lepidoptera: Noctuidae) and Southwestern Corn Borer (Lepidoptera: Crambidae) in Corn at Different Vegetative Stages","volume":"91","author":"Williams","year":"1998","journal-title":"J. Econ. Entomol."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Sisay, B., Simiyu, J., Mendesil, E., Likhayo, P., Ayalew, G., Mohamed, S., Subramanian, S., and Tefera, T. (2019). Fall armyworm, (spodoptera frugiperda) infestations in East Africa: Assessment of damage and parasitism. Insects, 10.","DOI":"10.3390\/insects10070195"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1564\/v28_oct_02","article-title":"Fall armyworm: Impacts and implications for Africa","volume":"28","author":"Day","year":"2017","journal-title":"Outlooks Pest Manag."},{"key":"ref_11","first-page":"09","article-title":"Review on management methods of fall armyworm (Spodoptera frugiperda J.E. Smith) in Sub- Saharan Africa","volume":"5","author":"Gebreziher","year":"2020","journal-title":"Int. J. Entomol. Res."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2513","DOI":"10.2134\/agronj2018.02.0110","article-title":"Maize\u2013legume intercropping and push\u2013pull for management of fall armyworm, stemborers, and striga in Uganda","volume":"110","author":"Hailu","year":"2018","journal-title":"Agron. J."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1016\/j.jenvman.2019.05.011","article-title":"Agro-ecological options for fall armyworm (Spodoptera frugiperda J.E. Smith) management: Providing low-cost, smallholder friendly solutions to an invasive pest","volume":"243","author":"Harrison","year":"2019","journal-title":"J. Environ. Manag."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"631","DOI":"10.1146\/annurev.ento.45.1.631","article-title":"Pest Management Strategies in Traditional Agriculture: An African Perspective","volume":"45","author":"Abate","year":"2000","journal-title":"Annu. Rev. Entomol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1007\/s10668-009-9195-2","article-title":"Social and ecological facets of pest management in Honduran subsistence agriculture: Implications for IPM extension and natural resource management","volume":"12","author":"Wyckhuys","year":"2010","journal-title":"Environ. Dev. Sustain."},{"key":"ref_16","first-page":"765","article-title":"Local and indigenous knowledge of farmers management practice against fall armyworm (Spodoptera frugiperda) (J.E. Smith) (Lepidoptera: Noctuidae): A review","volume":"8","author":"Yigezu","year":"2020","journal-title":"J. Entomol. Zool. Stud."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Ayra-Pardo, C., Huang, S., Kan, Y., and Wright, D.J. (2021). Impact of invasive fall armyworm on plant and arthropod communities and implications for crop protection. Int. J. Pest Manag., 1\u201312.","DOI":"10.1080\/09670874.2021.1968534"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1153","DOI":"10.1093\/jee\/75.6.1153","article-title":"Relative Susceptibility of a Summer-Planted Dent and Tropical Flint Corn Variety to Whorl Stage Damage by the Fall Armyworm (Lepidoptera: Noctuidae)","volume":"75","author":"Gross","year":"1982","journal-title":"J. Econ. Entomol."},{"key":"ref_19","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_20","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1016\/S0168-1923(99)00018-0","article-title":"Leaf area index estimates obtained for clumped canopies using hemispherical photography","volume":"94","author":"Jackson","year":"1999","journal-title":"Agric. For. Meteorol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1193","DOI":"10.1016\/j.agrformet.2008.02.014","article-title":"Intercomparison and sensitivity analysis of Leaf Area Index retrievals from LAI-2000, AccuPAR, and digital hemispherical photography over croplands","volume":"148","author":"Garrigues","year":"2008","journal-title":"Agric. For. Meteorol."},{"key":"ref_22","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_23","doi-asserted-by":"crossref","unstructured":"Van Hoek, M., Jia, L., Zhou, J., Zheng, C., and Menenti, M. (2016). Early drought detection by spectral analysis of satellite time series of precipitation and Normalized Difference Vegetation Index (NDVI). Remote Sens., 8.","DOI":"10.3390\/rs8050422"},{"key":"ref_24","unstructured":"Rouse, J.W., Haas, R.H., Schell, J.A., and Deering, D.W. (1973, January 10\u201314). Monitoring Vegetation Systems in the Great Plains with ERTS. Proceedings of the Third ERTS Symposium, Washington, DC, USA."},{"key":"ref_25","first-page":"607","article-title":"Evaluation of Narrowband and Broadband Vegetation Indices for Determining Optimal Hyperspectral Wavebands for Agricultural Crop Characterization","volume":"68","author":"Thenkabail","year":"2002","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1111\/jipb.12117","article-title":"Conventional digital cameras as a tool for assessing leaf area index and biomass for cereal breeding","volume":"56","author":"Villegas","year":"2014","journal-title":"J. Integr. Plant Biol."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Segarra, J., Buchaillot, M.L., Araus, J.L., and Kefauver, S.C. (2020). Remote sensing for precision agriculture: Sentinel-2 improved features and applications. Agronomy, 10.","DOI":"10.3390\/agronomy10050641"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"112004","DOI":"10.1016\/j.rse.2020.112004","article-title":"Phenology of short vegetation cycles in a Kenyan rangeland from PlanetScope and Sentinel-2","volume":"248","author":"Cheng","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1016\/j.tree.2005.05.011","article-title":"Using the satellite-derived NDVI to assess ecological responses to environmental change","volume":"20","author":"Pettorelli","year":"2005","journal-title":"Trends Ecol. Evol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2335","DOI":"10.1111\/j.1365-2486.2009.01910.x","article-title":"Intercomparison, interpretation, and assessment of spring phenology in North America estimated from remote sensing for 1982\u20132006","volume":"15","author":"White","year":"2009","journal-title":"Glob. Chang. Biol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1016\/j.agee.2003.11.009","article-title":"Mapping long-term changes in savannah crop productivity in Senegal through trend analysis of time series of remote sensing data","volume":"103","author":"Tottrup","year":"2004","journal-title":"Agric. Ecosyst. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Qader, S.H., Dash, J., Alegana, V.A., Khwarahm, N.R., Tatem, A.J., and Atkinson, P.M. (2021). The role of earth observation in achieving sustainable agricultural production in arid and semi-arid regions of the world. Remote Sens., 13.","DOI":"10.3390\/rs13173382"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"4199","DOI":"10.1080\/01431160701442054","article-title":"Indicators of Northern Eurasia\u2019s land-cover change trends from SPOT-VEGETATION time-series analysis 1998\u20132005","volume":"28","author":"Herold","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.rse.2008.08.015","article-title":"Phenologically-tuned MODIS NDVI-based production anomaly estimates for Zimbabwe","volume":"113","author":"Funk","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.envsoft.2013.10.021","article-title":"Image time series processing for agriculture monitoring","volume":"53","author":"Eerens","year":"2014","journal-title":"Environ. Model. Softw."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Reinermann, S., Gessner, U., Asam, S., Kuenzer, C., and Dech, S. (2019). The effect of droughts on vegetation condition in Germany: An analysis based on two decades of satellite earth observation time series and crop yield statistics. Remote Sens., 11.","DOI":"10.3390\/rs11151783"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1704","DOI":"10.3390\/rs5041704","article-title":"Using low resolution satellite imagery for yield prediction and yield anomaly detection","volume":"5","author":"Rembold","year":"2013","journal-title":"Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2293","DOI":"10.1080\/01431160701408444","article-title":"Monitoring farmers\u2019 decisions on Mediterranean irrigated crops using satellite image time series","volume":"29","author":"Serra","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1951","DOI":"10.1109\/TGRS.2012.2212447","article-title":"Evaluation of agreement between space remote sensing SPOT-VEGETATION fAPAR Time Series","volume":"51","author":"Meroni","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Winkler, K., Gessner, U., and Hochschild, V. (2017). Identifying droughts affecting agriculture in Africa based on remote sensing time series between 2000-2016: Rainfall anomalies and vegetation condition in the context of ENSO. Remote Sens., 9.","DOI":"10.3390\/rs9080831"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3389\/fenvs.2015.00046","article-title":"Remote sensing time series analysis for crop monitoring with the SPIRITS software: New functionalities and use examples","volume":"3","author":"Rembold","year":"2015","journal-title":"Front. Environ. Sci."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"470","DOI":"10.1007\/s11119-021-09845-4","article-title":"Detecting the attack of the fall armyworm (Spodoptera frugiperda) in cotton plants with machine learning and spectral measurements","volume":"23","author":"Ramos","year":"2021","journal-title":"Precis. Agric."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Brown, M.E., Mugo, S., Petersen, S., and Klauser, D. (2022). Designing a Pest and Disease Outbreak Warning System for Farmers, Agronomists and Agricultural Input Distributors in East Africa. Insects, 13.","DOI":"10.3390\/insects13030232"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"578","DOI":"10.17516\/1999-494X-0247","article-title":"Spatial Distribution of NDVI Seeds of Cereal Crops with Different Levels of Weediness According to PlanetScope Satellite Data","volume":"13","author":"Pisman","year":"2020","journal-title":"J. Sib. Fed. Univ. Eng. Technol."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Moyroud, N., and Portet, F. (2018). Introduction to QGIS. QGIS Generic Tools, Wiley.","DOI":"10.1002\/9781119457091.ch1"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1111\/j.1744-7348.2007.00116.x","article-title":"Using vegetation indices derived from conventional digital cameras as selection criteria for wheat breeding in water-limited environments","volume":"150","author":"Casadesus","year":"2007","journal-title":"Ann. Appl. Biol."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Buchaillot, M.L., Gracia-Romero, A., Vergara-Diaz, O., Zaman-Allah, M.A., Tarekegne, A., Cairns, J.E., Prasanna, B.M., Araus, J.L., and Kefauver, S.C. (2019). Evaluating maize genotype performance under low nitrogen conditions using RGB UAV phenotyping techniques. Sensors, 19.","DOI":"10.3390\/s19081815"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.isprsjprs.2019.01.016","article-title":"Radiometric calibration assessments for UAS-borne multispectral cameras: Laboratory and field protocols","volume":"149","author":"Cao","year":"2019","journal-title":"Int. Soc. J. Photogramm. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"644","DOI":"10.1016\/j.agrformet.2007.11.015","article-title":"Dedieu 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_50","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/j.cropro.2019.01.028","article-title":"Understanding the factors influencing fall armyworm (Spodoptera frugiperda J.E. Smith) damage in African smallholder maize fields and quantifying its impact on yield. A case study in Eastern Zimbabwe","volume":"120","author":"Baudron","year":"2019","journal-title":"Crop Prot."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Wang, R., Cherkauer, K., and Bowling, L. (2016). Corn response to climate stress detected with satellite-based NDVI time series. Remote Sens., 8.","DOI":"10.3390\/rs8040269"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Li, H., Jia, M., Zhang, R., Ren, Y., and Wen, X. (2019). Incorporating the plant phenological trajectory into mangrove species mapping with dense time series Sentinel-2 imagery and the Google Earth Engine platform. Remote Sens., 11.","DOI":"10.3390\/rs11212479"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1102","DOI":"10.1080\/15481603.2020.1846948","article-title":"A large-scale change monitoring of wetlands using time series Landsat imagery on Google Earth Engine: A case study in Newfoundland","volume":"57","author":"Mahdianpari","year":"2020","journal-title":"GISci. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"112829","DOI":"10.1016\/j.rse.2021.112829","article-title":"Continuous monitoring of forest change dynamics with satellite time series","volume":"269","author":"Decuyper","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_55","first-page":"105","article-title":"Sentinel-2 Data Analysis and Comparison with UAV Multispectral Images for Precision Viticulture Study areas","volume":"1","author":"Nonni","year":"2018","journal-title":"GI Forum"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/j.rse.2019.01.010","article-title":"Assessing spring phenology of a temperate woodland: A multiscale comparison of ground, unmanned aerial vehicle and Landsat satellite observations","volume":"223","author":"Berra","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"2971","DOI":"10.3390\/rs70302971","article-title":"Intercomparison of UAV, Aircraft and Satellite Remote Sensing Platforms for Precision Viticulture","volume":"7","author":"Matese","year":"2015","journal-title":"Remote Sens."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.aquabot.2004.06.001","article-title":"Estimating leaf area index of a degraded mangrove forest using high spatial resolution satellite data","volume":"80","author":"Kovacs","year":"2004","journal-title":"Aquat. Bot."},{"key":"ref_59","first-page":"102396","article-title":"Integration of satellite imagery and in situ soil moisture data for estimating irrigation water requirements","volume":"102","author":"Ihuoma","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"584","DOI":"10.1038\/s41558-020-0835-8","article-title":"Climate change and locust outbreak in East Africa","volume":"10","author":"Salih","year":"2020","journal-title":"Nat. Clim. Chang."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"3753","DOI":"10.1111\/gcb.15137","article-title":"On the relative role of climate change and management in the current desert locust outbreak in East Africa","volume":"26","author":"Meynard","year":"2020","journal-title":"Glob. Chang. Biol."},{"key":"ref_62","unstructured":"Odhiambo, K., Lewis, J., Tefera, N., Thomas, A., Meroni, M., and Rembold, M. (2021). Impacts of COVID-19 and Desert Locusts on Smallholder Farmers Food Systems and Value Chains in Kenya, Publications Office of the EU."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"514785","DOI":"10.3389\/fsufs.2021.514785","article-title":"From village to globe: A dynamic real-time map of African fields through PlantVillage","volume":"5","author":"Kehs","year":"2019","journal-title":"Front. Sustain. Food Syst."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/19\/5003\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:48:10Z","timestamp":1760143690000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/19\/5003"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,8]]},"references-count":63,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2022,10]]}},"alternative-id":["rs14195003"],"URL":"https:\/\/doi.org\/10.3390\/rs14195003","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,8]]}}}