Articles citing this paper
Forecasting wheat yield in a Mediterranean-type environment from the NOAA satellite
RCG Smith, J Adams, DJ Stephens and PT Hick
46(1) pp.113 - 125
118 articles found in Crossref database.
Estimation of pasture growth rate in the south west of Western Australia from AVHRR NDVI and climate data
Hill Michael J.,
Donald Graham E.,
Hyder Michael W., Smith Richard C.G.
Remote Sensing of Environment. 2004 93(4). p.528
The effect of tillage, crop rotation and residue management on maize and wheat growth and development evaluated with an optical sensor
Verhulst Nele,
Govaerts Bram,
Nelissen Victoria,
Sayre Ken D.,
Crossa Jose,
Raes Dirk, Deckers Jozef
Field Crops Research. 2011 120(1). p.58
Detection of Fungal Diseases in Lettuce by VIR-NIR Spectroscopy in Aquaponics
Sirakov Ivaylo,
Velichkova Katya,
Dinev Toncho,
Slavcheva-Sirakova Desislava,
Valkova Elica,
Yorgov Dimitar,
Veleva Petya,
Atanasov Vasil, Atanassova Stefka
Microorganisms. 2023 11(9). p.2348
Analysis of forest tree dieback using UltraCam and UAV imagery
Naseri Mohammad Hassan,
Shataee Jouibary Shaban, Habashi Hashem
Scandinavian Journal of Forest Research. 2023 38(6). p.392
Dehydration-induced changes in spectral reflectance indices and chlorophyll fluorescence of Antarctic lichens with different thallus color, and intrathalline photobiont
Miloš Barták,
Josef Hájek,
Jana Morkusová,
Kateřina Skácelová, Alica Košuthová
Acta Physiologiae Plantarum. 2018 40(10).
Random Forest Development and Modeling of Gross Primary Productivity in the Hudson Bay Lowlands
Beaver Jason,
Humphreys Elyn R., King Douglas
Canadian Journal of Remote Sensing. 2024 50(1).
Identification of species of the genus Populus L. based on the data of hyperspectral crown survey for climate change monitoring
Dmitriev Pavel A.,
Kozlovsky Boris L.,
Dmitrieva Anastasiya A.,
Rajput Vishnu D.,
Minkina Tatiana M., Varduni Tatiana V.
Environmental Challenges. 2022 9 p.100619
A generalized regression-based model for forecasting winter wheat yields in Kansas and Ukraine using MODIS data
Becker-Reshef I.,
Vermote E.,
Lindeman M., Justice C.
Remote Sensing of Environment. 2010 114(6). p.1312
Identifying the spatial variability of soil constraints using multi-year remote sensing
Dang Y.P.,
Pringle M.J.,
Schmidt M.,
Dalal R.C., Apan A.
Field Crops Research. 2011 123(3). p.248
A Review of Estimation Methods for Aboveground Biomass in Grasslands Using UAV
Bazzo Clara Oliva Gonçalves,
Kamali Bahareh,
Hütt Christoph,
Bareth Georg, Gaiser Thomas
Remote Sensing. 2023 15(3). p.639
Early Detection of Wild Rocket Tracheofusariosis Using Hyperspectral Image-Based Machine Learning
Pane Catello,
Manganiello Gelsomina,
Nicastro Nicola, Carotenuto Francesco
Remote Sensing. 2021 14(1). p.84
Accurate estimation of sorghum crop water content under different water stress levels using machine learning and hyperspectral data
Tunca Emre,
Köksal Eyüp Selim,
Öztürk Elif,
Akay Hasan, Çetin Taner Sakine
Environmental Monitoring and Assessment. 2023 195(7).
Actual evapotranspiration estimation by ground and remote sensing methods: the Australian experience
Glenn Edward P.,
Doody Tanya M.,
Guerschman Juan P.,
Huete Alfredo R.,
King Edward A.,
McVicar Tim R.,
Van Dijk Albert I. J. M.,
Van Niel Thomas G.,
Yebra Marta, Zhang Yongqiang
Hydrological Processes. 2011 25(26). p.4103
Early Detection of Southern Pine Beetle Attack by UAV-Collected Multispectral Imagery
Kanaskie Caroline R.,
Routhier Michael R.,
Fraser Benjamin T.,
Congalton Russell G.,
Ayres Matthew P., Garnas Jeff R.
Remote Sensing. 2024 16(14). p.2608
A Neural Network Method for Classification of Sunlit and Shaded Components of Wheat Canopies in the Field Using High-Resolution Hyperspectral Imagery
Sadeghi-Tehran Pouria,
Virlet Nicolas, Hawkesford Malcolm J.
Remote Sensing. 2021 13(5). p.898
Relationship between MODIS Derived NDVI and Yield of Cereals for Selected European Countries
Panek Ewa, Gozdowski Dariusz
Agronomy. 2021 11(2). p.340
Hyperspectral imaging combined with machine learning for high‐throughput phenotyping in winter wheat
Kaur Sehijpreet,
Kakani Vijaya Gopal,
Carver Brett,
Jarquin Diego, Singh Aditya
The Plant Phenome Journal. 2024 7(1).
High spatial resolution spectral unmixing for mapping ash species across a complex urban environment
Pontius Jennifer,
Hanavan Ryan P.,
Hallett Richard A.,
Cook Bruce D., Corp Lawrence A.
Remote Sensing of Environment. 2017 199 p.360
The relationships between different vegetation indices and chlorophyll content index values (CCI) in strawberry leaves
2022 8th International Conference on Energy Efficiency and Agricultural Engineering (EE&AE) (2022)
Within-Field Relationships between Satellite-Derived Vegetation Indices, Grain Yield and Spike Number of Winter Wheat and Triticale
Panek Ewa,
Gozdowski Dariusz,
Stępień Michał,
Samborski Stanisław,
Ruciński Dominik, Buszke Bartosz
Agronomy. 2020 10(11). p.1842
In‐Season Prediction of Potential Grain Yield in Winter Wheat Using Canopy Reflectance
Raun William R.,
Solie John B.,
Johnson Gordon V.,
Stone Marvin L.,
Lukina Erna V.,
Thomason Wade E., Schepers James S.
Agronomy Journal. 2001 93(1). p.131
Climate and rainfed wheat yield
Faghih Homayoun,
Behmanesh Javad,
Rezaie Hossein, Khalili Keivan
Theoretical and Applied Climatology. 2021 144(1-2). p.13
Peatland leaf-area index and biomass estimation with ultra-high resolution remote sensing
Räsänen Aleksi,
Juutinen Sari,
Kalacska Margaret,
Aurela Mika,
Heikkinen Pauli,
Mäenpää Kari,
Rimali Aleksi, Virtanen Tarmo
GIScience & Remote Sensing. 2020 57(7). p.943
Method for national mapping spatial extent of southern beech forest using temporal spectral signatures
Schindler Jan,
Dymond John R.,
Wiser Susan K., Shepherd James D.
International Journal of Applied Earth Observation and Geoinformation. 2021 102 p.102408
Towards a Universal Hyperspectral Index to Assess Chlorophyll Content in Deciduous Forests
Sonobe Rei, Wang Quan
Remote Sensing. 2017 9(3). p.191
Towards an Improved High-Throughput Phenotyping Approach: Utilizing MLRA and Dimensionality Reduction Techniques for Transferring Hyperspectral Proximal-Based Model to Airborne Images
Heidarian Dehkordi Ramin,
Candiani Gabriele,
Nutini Francesco,
Carotenuto Federico,
Gioli Beniamino,
Cesaraccio Carla, Boschetti Mirco
Remote Sensing. 2024 16(3). p.492
Tracking the Seasonal Dynamics of Boreal Forest Photosynthesis Using EO-1 Hyperion Reflectance: Sensitivity to Structural and Illumination Effects
Hernandez-Clemente Rocio,
Kolari Pasi,
Porcar-Castell Albert,
Korhonen Lauri, Mottus Matti
IEEE Transactions on Geoscience and Remote Sensing. 2016 54(9). p.5105
Spectral Knowledge (SK-UTALCA): Software for Exploratory Analysis of High-Resolution Spectral Reflectance Data on Plant Breeding
Lobos Gustavo A., Poblete-Echeverría Carlos
Frontiers in Plant Science. 2017 7
Application of Reflectance Indices for Remote Sensing of Plants and Revealing Actions of Stressors
Kior Anastasiia,
Sukhov Vladimir, Sukhova Ekaterina
Photonics. 2021 8(12). p.582
The applicability of empirical vegetation indices for determining leaf chlorophyll content over different leaf and canopy structures
Croft H.,
Chen J.M., Zhang Y.
Ecological Complexity. 2014 17 p.119
An angular vegetation index for imaging spectroscopy data—Preliminary results on forest damage detection in the Bavarian National Park, Germany
Fassnacht Fabian Ewald,
Latifi Hooman, Koch Barbara
International Journal of Applied Earth Observation and Geoinformation. 2012 19 p.308
Modeling of winter wheat fAPAR by integrating Unmanned Aircraft Vehicle-based optical, structural and thermal measurement
Lv Zhengang,
Meng Ran,
Man Jianguo,
Zeng Linglin,
Wang Meiyv,
Xu Binyuan,
Gao Renjie,
Sun Rui, Zhao Feng
International Journal of Applied Earth Observation and Geoinformation. 2021 102 p.102407
Evaluation of a One-Dimensional Convolution Neural Network for Chlorophyll Content Estimation Using a Compact Spectrometer
Nofrizal Adenan Yandra,
Sonobe Rei,
Yamashita Hiroto,
Seki Haruyuki,
Mihara Harumi,
Morita Akio, Ikka Takashi
Remote Sensing. 2022 14(9). p.1997
Deriving Comprehensive County‐Level Crop Yield and Area Data for U.S. Cropland
Lokupitiya Erandathie,
Breidt F. Jay,
Lokupitiya Ravindra,
Williams Steve, Paustian Keith
Agronomy Journal. 2007 99(3). p.673
Hyperspectral indices for quantifying leaf chlorophyll concentrations performed differently with different leaf types in deciduous forests
Sonobe Rei, Wang Quan
Ecological Informatics. 2017 37 p.1
Mapping within-field leaf chlorophyll content in agricultural crops for nitrogen management using Landsat-8 imagery
Croft Holly,
Arabian Joyce,
Chen Jing M.,
Shang Jiali, Liu Jiangui
Precision Agriculture. 2020 21(4). p.856
Quantification of CO2 fluxes in paddy rice based on the characterization and simulation of CO2 assimilation approaches
Choi Jinsil,
Ko Jonghan,
Ng Chi Tim,
Jeong Seungtaek,
Tenhunen John,
Xue Wei, Cho Jaeil
Agricultural and Forest Meteorology. 2018 249 p.348
Fresh Yield Estimation of Spring Tea via Spectral Differences in UAV Hyperspectral Images from Unpicked and Picked Canopies
He Zongtai,
Wu Kaihua,
Wang Fumin,
Jin Lisong,
Zhang Rongxu,
Tian Shoupeng,
Wu Weizhi,
He Yadong,
Huang Ran,
Yuan Lin, Zhang Yao
Remote Sensing. 2023 15(4). p.1100
Using MODIS imagery, climate and soil data to estimate pasture growth rates on farms in the south-west of Western Australia
Donald G. E.,
Gherardi S. G.,
Edirisinghe A.,
Gittins S. P.,
Henry D. A., Mata G.
Animal Production Science. 2010 50(6). p.611
Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications
Xue Jinru, Su Baofeng
Journal of Sensors. 2017 2017 p.1
Spectral Estimation of In Vivo Wheat Chlorophyll a/b Ratio under Contrasting Water Availabilities
Mulero Gabriel,
Bacher Harel,
Kleiner Uri,
Peleg Zvi, Herrmann Ittai
Remote Sensing. 2022 14(11). p.2585
An explainable XGBoost model improved by SMOTE-ENN technique for maize lodging detection based on multi-source unmanned aerial vehicle images
Han Liang,
Yang Guijun,
Yang Xiaodong,
Song Xiaoyu,
Xu Bo,
Li Zhenhai,
Wu Jintao,
Yang Hao, Wu Jianwei
Computers and Electronics in Agriculture. 2022 194 p.106804
Interactive effects of water deficit and nitrogen nutrition on winter wheat. Remote sensing methods for their detection
Klem Karel,
Záhora Jaroslav,
Zemek František,
Trunda Petr,
Tůma Ivan,
Novotná Kateřina,
Hodaňová Petra,
Rapantová Barbora,
Hanuš Jan,
Vavříková Jana, Holub Petr
Agricultural Water Management. 2018 210 p.171
Near-distance imaging spectroscopy investigating chlorophyll fluorescence and photosynthetic activity of grassland in the daily course
Ač Alexander,
Malenovský Zbyněk,
Hanuš Jan,
Tomášková Ivana,
Urban Otmar, Marek Michal V.
Functional Plant Biology. 2009 36(11). p.1006
Comprehensive Remote Sensing (2018)
Machine learning modelling of crop structure within the Maize Triangle of South Africa
Naidoo Laven,
Main Russell,
Cho Moses A.,
Madonsela Sabelo, Majozi Nobuhle
International Journal of Remote Sensing. 2022 43(1). p.27
Integrating farmer knowledge, precision agriculture tools, and crop simulation modelling to evaluate management options for poor-performing patches in cropping fields
Oliver Y.M.,
Robertson M.J., Wong M.T.F.
European Journal of Agronomy. 2010 32(1). p.40
Contribution of agrometeorology to the simulation of crop production and its applications
Hoogenboom Gerrit
Agricultural and Forest Meteorology. 2000 103(1-2). p.137
The Potential of Landsat NDVI Sequences to Explain Wheat Yield Variation in Fields in Western Australia
Shen Jianxiu, Evans Fiona H.
Remote Sensing. 2021 13(11). p.2202
CropPhenology: An R package for extracting crop phenology from time series remotely sensed vegetation index imagery
Araya Sofanit,
Ostendorf Bertram,
Lyle Gregory, Lewis Megan
Ecological Informatics. 2018 46 p.45
Identification of species of the genus Acer L. using vegetation indices calculated from the hyperspectral images of leaves
Dmitriev Pavel A.,
Kozlovsky Boris L.,
Kupriushkin Denis P.,
Lysenko Vladimir S.,
Rajput Vishnu D.,
Ignatova Maria A.,
Tarik Ekaterina P.,
Kapralova Olga A.,
Tokhtar Valeriy K.,
Singh Anil Kumar,
Minkina Tatiana,
Varduni Tatiana V.,
Sharma Meenakshi,
Taloor Ajay Kumar, Thapliyal Asha
Remote Sensing Applications: Society and Environment. 2022 25 p.100679
The Effect of Leaf Stacking on Leaf Reflectance and Vegetation Indices Measured by Contact Probe during the Season
Neuwirthová Eva,
Lhotáková Zuzana, Albrechtová Jana
Sensors. 2017 17(6). p.1202
Resistance of Antarctic moss Sanionia uncinata to photoinhibition: chlorophyll fluorescence analysis of samples from the western and eastern coasts of the Antarctic Peninsula
Orekhova A.,
Barták M.,
Casanova‐Katny A.,
Hájek J., Sparks J.
Plant Biology. 2021 23(4). p.653
Testing the Temporal Ability of Landsat Imagery and Precision Agriculture Technology to Provide High Resolution Historical Estimates of Wheat Yield at the Farm Scale
Lyle Greg,
Lewis Megan, Ostendorf Bertram
Remote Sensing. 2013 5(4). p.1549
Detection of pine wood nematode infestation using hyperspectral drone images
Yu Run,
Luo Youqing, Ren Lili
Ecological Indicators. 2024 162 p.112034
Early detection of spruce vitality loss with hyperspectral data: Results of an experimental study in Bavaria, Germany
Einzmann Kathrin,
Atzberger Clement,
Pinnel Nicole,
Glas Christina,
Böck Sebastian,
Seitz Rudolf, Immitzer Markus
Remote Sensing of Environment. 2021 266 p.112676
Estimation of Leaf Area Index of Beta vulgaris L. Based on Optical Remote Sensing Data
Hoffmann C. M., Blomberg M.
Journal of Agronomy and Crop Science. 2004 190(3). p.197
Remote Sensing Derived Phenological Metrics to Assess the Spatio-Temporal Growth Variability in Cropping Fields
Araya Sofanit,
Ostendorf Bertram,
Lyle Greg, Lewis Megan
Advances in Remote Sensing. 2017 06(03). p.212
Vegetation Indices for Mapping Canopy Foliar Nitrogen in a Mixed Temperate Forest
Wang Zhihui,
Wang Tiejun,
Darvishzadeh Roshanak,
Skidmore Andrew,
Jones Simon,
Suarez Lola,
Woodgate William,
Heiden Uta,
Heurich Marco, Hearne John
Remote Sensing. 2016 8(6). p.491
Monitoring Forest Health Using Hyperspectral Imagery: Does Feature Selection Improve the Performance of Machine-Learning Techniques?
Schratz Patrick,
Muenchow Jannes,
Iturritxa Eugenia,
Cortés José,
Bischl Bernd, Brenning Alexander
Remote Sensing. 2021 13(23). p.4832
Evaluating hyperspectral vegetation indices for estimating nitrogen concentration of winter wheat at different growth stages
Li Fei,
Miao Yuxin,
Hennig Simon D.,
Gnyp Martin L.,
Chen Xinping,
Jia Liangliang, Bareth Georg
Precision Agriculture. 2010 11(4). p.335
Winter Wheat Yield Prediction Using Normalized Difference Vegetative Index and Agro‐Climatic Parameters in Oklahoma
Zhang Ning,
Zhao Chen,
Quiring Steven M., Li Jinlin
Agronomy Journal. 2017 109(6). p.2700
Agricultural crop discrimination in a heterogeneous low-mountain range region based on multi-temporal and multi-sensor satellite data
Kyere Isaac,
Astor Thomas,
Graß Rüdiger, Wachendorf Michael
Computers and Electronics in Agriculture. 2020 179 p.105864
Evaluation of drought and UV radiation impacts on above-ground biomass of mountain grassland by spectral reflectance and thermal imaging techniques
Novotná Kateřina,
Klem Karel,
Holub Petr,
Rapantová Barbora, Urban Otmar
Beskydy. 2016 9(1-2). p.21
Retrieving rice (Oryza sativa L.) net photosynthetic rate from UAV multispectral images based on machine learning methods
Wu Tianao,
Zhang Wei,
Wu Shuyu,
Cheng Minghan,
Qi Lushang,
Shao Guangcheng, Jiao Xiyun
Frontiers in Plant Science. 2023 13
Species-specific responses of spectral reflectance and the photosynthetic characteristics in two selected Antarctic mosses to thallus desiccation
Orekhova Alla,
Barták Miloš,
Hájek Josef, Morkusová Jana
Acta Physiologiae Plantarum. 2022 44(1).
Investigation of optimal vegetation indices for retrieval of leaf chlorophyll and leaf area index using enhanced learning algorithms
Verma Bhagyashree,
Prasad Rajendra,
Srivastava Prashant K.,
Yadav Suraj A.,
Singh Prachi, Singh R.K.
Computers and Electronics in Agriculture. 2022 192 p.106581
Joint Use of PROSAIL and DART for Fast LUT Building: Application to Gap Fraction and Leaf Biochemistry Estimations over Sparse Oak Stands
Miraglio Thomas,
Adeline Karine,
Huesca Margarita,
Ustin Susan, Briottet Xavier
Remote Sensing. 2020 12(18). p.2925
Early detection of pine wilt disease tree candidates using time-series of spectral signatures
Yu Run,
Huo Langning,
Huang Huaguo,
Yuan Yuan,
Gao Bingtao,
Liu Yujie,
Yu Linfeng,
Li Haonan,
Yang Liyuan,
Ren Lili, Luo Youqing
Frontiers in Plant Science. 2022 13
Combining chlorophyll meter readings and high spatial resolution remote sensing images for in-season site-specific nitrogen management of corn
Miao Yuxin,
Mulla David J.,
Randall Gyles W.,
Vetsch Jeffrey A., Vintila Roxana
Precision Agriculture. 2009 10(1). p.45
Retrieval of leaf biochemical properties by inversed PROSPECT model and hyperspectral indices: an ap-plication to Populus euphratica polymorphic leaves
MA ZhongGuo,
CHEN Xi,
WANG Quan,
LI PingHeng, Jiapaer Guli
Journal of Arid Land. 2012 4(1). p.52
Hyperspectral aerial imagery for detecting nitrogen stress in two potato cultivars
Nigon Tyler J.,
Mulla David J.,
Rosen Carl J.,
Cohen Yafit,
Alchanatis Victor,
Knight Joseph, Rud Ronit
Computers and Electronics in Agriculture. 2015 112 p.36
(2019)
Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps
Mulla David J.
Biosystems Engineering. 2013 114(4). p.358
Predicting yellow rust in wheat breeding trials by proximal phenotyping and machine learning
Koc Alexander,
Odilbekov Firuz,
Alamrani Marwan,
Henriksson Tina, Chawade Aakash
Plant Methods. 2022 18(1).
The early explanatory power of NDVI in crop yield modelling
Wall Lenny,
Larocque Denis, Léger Pierre‐Majorique
International Journal of Remote Sensing. 2008 29(8). p.2211
Remotely Sensed Rice Yield Prediction Using Multi-Temporal NDVI Data Derived from NOAA's-AVHRR
Huang Jingfeng,
Wang Xiuzhen,
Li Xinxing,
Tian Hanqin,
Pan Zhuokun, Yan Wengui
PLoS ONE. 2013 8(8). p.e70816
Application of neural networks to image recognition of wheat rust diseases
2020 Cognitive Sciences, Genomics and Bioinformatics (CSGB) (2020)
Comparison of an aerial-based system and an on the ground continuous measuring device to predict yield of winter wheat
Reyniers Mieke,
Vrindts Els, De Baerdemaeker Josse
European Journal of Agronomy. 2006 24(2). p.87
Criteria Comparison for Classifying Peatland Vegetation Types Using In Situ Hyperspectral Measurements
Erudel Thierry,
Fabre Sophie,
Houet Thomas,
Mazier Florence, Briottet Xavier
Remote Sensing. 2017 9(7). p.748
Opportunities and constraints for managing within-field spatial variability in Western Australian grain production
Robertson Michael,
Isbister Bindi,
Maling Ian,
Oliver Yvette,
Wong Mike,
Adams Matt,
Bowden Bill, Tozer Peter
Field Crops Research. 2007 104(1-3). p.60
Remote Sensing-Based Estimation of Advanced Perennial Grass Biomass Yields for Bioenergy
Hamada Yuki,
Zumpf Colleen R.,
Cacho Jules F.,
Lee DoKyoung,
Lin Cheng-Hsien,
Boe Arvid,
Heaton Emily,
Mitchell Robert, Negri Maria Cristina
Land. 2021 10(11). p.1221
Remote sensing for assessing the zone of benefit where deep drains improve productivity of land affected by shallow saline groundwater
Kobryn H.T.,
Lantzke R.,
Bell R., Admiraal R.
Journal of Environmental Management. 2015 150 p.138
Functional Hyperspectral Imaging by High-Related Vegetation Indices to Track the Wide-Spectrum Trichoderma Biocontrol Activity Against Soil-Borne Diseases of Baby-Leaf Vegetables
Manganiello Gelsomina,
Nicastro Nicola,
Caputo Michele,
Zaccardelli Massimo,
Cardi Teodoro, Pane Catello
Frontiers in Plant Science. 2021 12
Measuring wheat nitrogen status from space and ground‐based platform
Reyniers M., Vrindts E.
International Journal of Remote Sensing. 2006 27(3). p.549
Optical Measurement of Crop Cover for Yield Prediction of Wheat
Reyniers M.,
Vrindts E., Baerdemaeker J. De
Biosystems Engineering. 2004 89(4). p.383
Improved wheat yield and production forecasting with a moisture stress index, AVHRR and MODIS data
Schut A. G. T.,
Stephens D. J.,
Stovold R. G. H.,
Adams M., Craig R. L.
Crop and Pasture Science. 2009 60(1). p.60
Towards universal broad leaf chlorophyll indices using PROSPECT simulated database and hyperspectral reflectance measurements
le Maire G.,
François C., Dufrêne E.
Remote Sensing of Environment. 2004 89(1). p.1
Investigation of Remote Sensing for Potential Use in Dike Inspection
Cundill Sharon L.,
van der Meijde Mark, Hack H. Robert G. K.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2014 7(2). p.733
Analysis of relationship between cereal yield and NDVI for selected regions of Central Europe based on MODIS satellite data
Panek Ewa, Gozdowski Dariusz
Remote Sensing Applications: Society and Environment. 2020 17 p.100286
Geospatial Technologies for Crops and Soils (2021)
Assessment of Invasive and Weed Species by Hyperspectral Imagery in Agrocenoses Ecosystem
Dmitriev Pavel A.,
Kozlovsky Boris L.,
Kupriushkin Denis P.,
Dmitrieva Anastasia A.,
Rajput Vishnu D.,
Chokheli Vasily A.,
Tarik Ekaterina P.,
Kapralova Olga A.,
Tokhtar Valeriy K.,
Minkina Tatiana M., Varduni Tatiana V.
Remote Sensing. 2022 14(10). p.2442
Aroma and metabolite profiling in duckweeds: Exploring species and ecotypic variation to enable wider adoption as a food crop
Smith Kellie E.,
Schäfer Martin,
Lim Mui,
Robles-Zazueta Carlos A.,
Cowan Laura,
Fisk Ian D.,
Xu Shuqing, Murchie Erik H.
Journal of Agriculture and Food Research. 2024 18 p.101263
Evaluation of Grass Quality under Different Soil Management Scenarios Using Remote Sensing Techniques
Askari Mohammad Sadegh,
McCarthy Timothy,
Magee Aidan, Murphy Darren J.
Remote Sensing. 2019 11(15). p.1835
Using spectral reflectance to estimate leaf chlorophyll content of tea with shading treatments
Sonobe Rei,
Sano Tomohito, Horie Hideki
Biosystems Engineering. 2018 175 p.168
Revealing the Complex Relationship Among Hyperspectral Reflectance, Photosynthetic Pigments, and Growth in Norway Spruce Ecotypes
Hejtmánek Jakub,
Stejskal Jan,
Čepl Jaroslav,
Lhotáková Zuzana,
Korecký Jiří,
Krejzková Anna,
Dvořák Jakub, Gezan Salvador A.
Frontiers in Plant Science. 2022 13
Multiscaling NDVI Series Analysis of Rainfed Cereal in Central Spain
Rivas-Tabares David Andrés,
Saa-Requejo Antonio,
Martín-Sotoca Juan José, Tarquis Ana María
Remote Sensing. 2021 13(4). p.568
The current and potential operational uses of remote sensing to aid decisions on drought exceptional circumstances in Australia: a review
McVicar Tim R., Jupp David L.B.
Agricultural Systems. 1998 57(3). p.399
Monitoring LAI, Chlorophylls, and Carotenoids Content of a Woodland Savanna Using Hyperspectral Imagery and 3D Radiative Transfer Modeling
Miraglio Thomas,
Adeline Karine,
Huesca Margarita,
Ustin Susan, Briottet Xavier
Remote Sensing. 2019 12(1). p.28
Indicator-to-impact links to help improve agricultural drought preparedness in Thailand
Tanguy Maliko,
Eastman Michael,
Magee Eugene,
Barker Lucy J.,
Chitson Thomas,
Ekkawatpanit Chaiwat,
Goodwin Daniel,
Hannaford Jamie,
Holman Ian,
Pardthaisong Liwa,
Parry Simon,
Rey Vicario Dolores, Visessri Supattra
Natural Hazards and Earth System Sciences. 2023 23(7). p.2419
Estimating Aboveground Net Primary Production (ANPP) Using Landsat 8-Based Indices: A Case Study From Hir-Neur Rangelands, Iran
Ghorbani Ardavan,
Dadjou Farid,
Moameri Mehdi, Biswas Asim
Rangeland Ecology & Management. 2020 73(5). p.649
Fusion of UAV Hyperspectral Imaging and LiDAR for the Early Detection of EAB Stress in Ash and a New EAB Detection Index—NDVI(776,678)
Zhou Quan,
Yu Linfeng,
Zhang Xudong,
Liu Yujie,
Zhan Zhongyi,
Ren Lili, Luo Youqing
Remote Sensing. 2022 14(10). p.2428
Computer-Aided Crop Yield Forecasting Techniques - Systematic Review Highlighting the Application of AI
Pushpalatha Raji,
Roshni Thendiyath,
Gangadharan Byju, Kutty Govindan
Environmental Modeling & Assessment. 2024
Advances in precision agriculture in south-eastern Australia. I. A regression methodology to simulate spatial variation in cereal yields using farmers' historical paddock yields and normalised difference vegetation index
Fisher P. D.,
Abuzar M.,
Rab M. A.,
Best F., Chandra S.
Crop and Pasture Science. 2009 60(9). p.844
Ash decline assessment in emerald ash borer-infested regions: A test of tree-level, hyperspectral technologies
Pontius Jennifer,
Martin Mary,
Plourde Lucie, Hallett Richard
Remote Sensing of Environment. 2008 112(5). p.2665
Field-based remote sensing models predict radiation use efficiency in wheat
Robles-Zazueta Carlos A,
Molero Gemma,
Pinto Francisco,
Foulkes M John,
Reynolds Matthew P, Murchie Erik H
Journal of Experimental Botany. 2021 72(10). p.3756
Regional estimation of savanna grass nitrogen using the red-edge band of the spaceborne RapidEye sensor
Ramoelo A.,
Skidmore A.K.,
Cho M.A.,
Schlerf M.,
Mathieu R., Heitkönig I.M.A.
International Journal of Applied Earth Observation and Geoinformation. 2012 19 p.151
Potential to monitor plant stress using remote sensing tools
Ramoelo Abel,
Dzikiti Sebinasi,
van Deventer Heidi,
Maherry Ashton,
Cho Moses Azong, Gush Mark
Journal of Arid Environments. 2015 113 p.134
Estimation of maize above-ground biomass based on stem-leaf separation strategy integrated with LiDAR and optical remote sensing data
Zhu Yaohui,
Zhao Chunjiang,
Yang Hao,
Yang Guijun,
Han Liang,
Li Zhenhai,
Feng Haikuan,
Xu Bo,
Wu Jintao, Lei Lei
PeerJ. 2019 7 p.e7593
Identifying the Spectral Signatures of Invasive and Native Plant Species in Two Protected Areas of Pakistan through Field Spectroscopy
Iqbal Iram M.,
Balzter Heiko,
Firdaus-e-Bareen , Shabbir Asad
Remote Sensing. 2021 13(19). p.4009
Modelling seasonal changes in the distribution of Common Quail Coturnix coturnix in farmland landscapes using remote sensing
Sardà‐Palomera Francesc,
Puigcerver Manel,
Brotons Lluís,
Rodríguez‐Teijeiro José Domingo, Butler Simon
Ibis. 2012 154(4). p.703
A high resolution broad scale spatial indicator of grain growing profitability for natural resource planning
Lyle G., Ostendorf B.
Ecological Indicators. 2011 11(1). p.209
Improving the timeliness of winter wheat production forecast in the United States of America, Ukraine and China using MODIS data and NCAR Growing Degree Day information
Franch B.,
Vermote E.F.,
Becker-Reshef I.,
Claverie M.,
Huang J.,
Zhang J.,
Justice C., Sobrino J.A.
Remote Sensing of Environment. 2015 161 p.131
A 10-Year Assessment of Hemlock Decline in the Catskill Mountain Region of New York State Using Hyperspectral Remote Sensing Techniques
Hanavan R. P.,
Pontius J., Hallett R.
Journal of Economic Entomology. 2015 108(1). p.339
Using only the red-edge bands is sufficient to detect tree stress: A case study on the early detection of PWD using hyperspectral drone images
Li Niwen,
Huo Langning, Zhang Xiaoli
Computers and Electronics in Agriculture. 2024 217 p.108665
Maple species identification based on leaf hyperspectral imaging data
Dmitriev Pavel A.,
Kozlovsky Boris L.,
Dmitrieva Anastasiya A., Varduni Tatiana V.
Remote Sensing Applications: Society and Environment. 2023 30 p.100964
An investigation into robust spectral indices for leaf chlorophyll estimation
Main Russell,
Cho Moses Azong,
Mathieu Renaud,
O’Kennedy Martha M.,
Ramoelo Abel, Koch Susan
ISPRS Journal of Photogrammetry and Remote Sensing. 2011 66(6). p.751
Evaluating the potential of airborne hyperspectral LiDAR for assessing forest insects and diseases with 3D Radiative Transfer Modeling
Zhao Xun,
Qi Jianbo,
Xu Haifeng,
Yu Zhexiu,
Yuan Lijuan,
Chen Yiwen, Huang Huaguo
Remote Sensing of Environment. 2023 297 p.113759
