Systematic Mapping Study on Remote Sensing in Agriculture

The area of remote sensing techniques in agriculture has reached a significant degree of development and maturity, with numerous journals, conferences, and organizations specialized in it. Moreover, many review papers are available in the literature. The present work describes a literature review that adopts the form of a systematic mapping study, following a formal methodology. Eight mapping questions were defined, analyzing the main types of research, techniques, platforms, topics, and spectral information. A predefined search string was applied in the Scopus database, obtaining 1590 candidate papers. Afterwards, the most relevant 106 papers were selected, considering those with more than six citations per year. These are analyzed in more detail, answering the mapping questions for each paper. In this way, the current trends and new opportunities are discovered. As a result, increasing interest in the area has been observed since 2000; the most frequently addressed problems are those related to parameter estimation, growth vigor, and water usage, using classification techniques, that are mostly applied on RGB and hyperspectral images, captured from drones and satellites. A general recommendation that emerges from this study is to build on existing resources, such as agricultural image datasets, public satellite imagery, and deep learning toolkits.

[1]  S. Miklavcic,et al.  Estimation of vegetation indices for high-throughput phenotyping of wheat using aerial imaging , 2018, Plant Methods.

[2]  Chenghai Yang,et al.  Original paper: Evaluating high resolution SPOT 5 satellite imagery for crop identification , 2011 .

[3]  Chu Zhang,et al.  Rice Seed Cultivar Identification Using Near-Infrared Hyperspectral Imaging and Multivariate Data Analysis , 2013, Sensors.

[4]  Rick L. Lawrence,et al.  Mapping invasive plants using hyperspectral imagery and Breiman Cutler classifications (RandomForest) , 2006 .

[5]  Gui-Song Xia,et al.  Land-Cover Classification with High-Resolution Remote Sensing Images Using Transferable Deep Models , 2018 .

[6]  Jose Armando Fernandez-Gallego,et al.  UAV and Ground Image-Based Phenotyping: A Proof of Concept with Durum Wheat , 2019, Remote. Sens..

[7]  Krystyna A. Stankiewicz,et al.  The efficiency of crop recognition on ENVISAT ASAR images in two growing seasons , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[8]  Raul Morais,et al.  Hyperspectral Imaging: A Review on UAV-Based Sensors, Data Processing and Applications for Agriculture and Forestry , 2017, Remote. Sens..

[9]  Mukesh Tripathi,et al.  A role of computer vision in fruits and vegetables among various horticulture products of agriculture fields: A survey , 2020 .

[10]  Giorgos Mallinis,et al.  A Hidden Markov Models Approach for Crop Classification: Linking Crop Phenology to Time Series of Multi-Sensor Remote Sensing Data , 2015, Remote. Sens..

[11]  Pasquale Daponte,et al.  A height measurement uncertainty model for archaeological surveys by aerial photogrammetry , 2017 .

[12]  Kendall C. DeJonge,et al.  Evaluation of thermal remote sensing indices to estimate crop evapotranspiration coefficients , 2017 .

[13]  Jizhong Deng,et al.  A fully convolutional network for weed mapping of unmanned aerial vehicle (UAV) imagery , 2018, PloS one.

[14]  Yanjie Wang,et al.  Estimation of Winter Wheat Above-Ground Biomass Using Unmanned Aerial Vehicle-Based Snapshot Hyperspectral Sensor and Crop Height Improved Models , 2017, Remote. Sens..

[15]  R. Lucas,et al.  Rule-based classification of multi-temporal satellite imagery for habitat and agricultural land cover mapping , 2007 .

[16]  Gabriele Nolè,et al.  Agricultural plastic waste spatial estimation by Landsat 8 satellite images , 2017, Comput. Electron. Agric..

[17]  Vanni Nardino,et al.  Estimation of canopy attributes in beech forests using true colour digital images from a small fixed-wing UAV , 2016, Int. J. Appl. Earth Obs. Geoinformation.

[18]  S. Minu,et al.  Review of preprocessing techniques used in soil property prediction from hyperspectral data , 2016 .

[19]  Achim Walter,et al.  Assessment of Multi-Image Unmanned Aerial Vehicle Based High-Throughput Field Phenotyping of Canopy Temperature , 2020, Frontiers in Plant Science.

[20]  Wang Futang,et al.  Monitoring winter wheat growth in North China by combining a crop model and remote sensing data , 2008 .

[21]  P. Nagler,et al.  Vegetation index‐based crop coefficients to estimate evapotranspiration by remote sensing in agricultural and natural ecosystems , 2011 .

[22]  D. King,et al.  Comparison of pixel- and object-based classification in land cover change mapping , 2011 .

[23]  J. M. Molina-Martínez,et al.  Remote Image Capture System to Improve Aerial Supervision for Precision Irrigation in Agriculture , 2019, Water.

[24]  Yanxia Zhao,et al.  Assimilating remote sensing information with crop model using Ensemble Kalman Filter for improving LAI monitoring and yield estimation , 2013 .

[25]  Xavier Blaes,et al.  Efficiency of crop identification based on optical and SAR image time series , 2005 .

[26]  Paul Scheunders,et al.  Close-range hyperspectral image analysis for the early detection of stress responses in individual plants in a high-throughput phenotyping platform , 2018 .

[27]  B. Brisco,et al.  Precision Agriculture and the Role of Remote Sensing: A Review , 1998 .

[28]  Aiman Soliman,et al.  Remote Sensing of Soil Moisture in Vineyards Using Airborne and Ground-Based Thermal Inertia Data , 2013, Remote. Sens..

[29]  A. Huete,et al.  Evaluation of optical remote sensing to estimate actual evapotranspiration and canopy conductance , 2013 .

[30]  L. Quebrajo,et al.  Linking thermal imaging and soil remote sensing to enhance irrigation management of sugar beet , 2018 .

[31]  Miao Zhang,et al.  Mapping Winter Wheat Biomass and Yield Using Time Series Data Blended from PROBA-V 100- and 300-m S1 Products , 2016, Remote. Sens..

[32]  Ferhat Kurtulmuş,et al.  Discriminating rapeseed varieties using computer vision and machine learning , 2015, Expert Syst. Appl..

[33]  Mac McKee,et al.  Estimating chlorophyll with thermal and broadband multispectral high resolution imagery from an unmanned aerial system using relevance vector machines for precision agriculture , 2015, Int. J. Appl. Earth Obs. Geoinformation.

[34]  Fabian Löw,et al.  Defining the Spatial Resolution Requirements for Crop Identification Using Optical Remote Sensing , 2014, Remote. Sens..

[35]  Michael Pflanz,et al.  Monitoring Agronomic Parameters of Winter Wheat Crops with Low-Cost UAV Imagery , 2016, Remote. Sens..

[36]  Michael Buchwitz,et al.  A remote sensing technique for global monitoring of power plant CO 2 emissions from space and related applications , 2010 .

[37]  Fei Liu,et al.  Detecting macronutrients content and distribution in oilseed rape leaves based on hyperspectral imaging , 2013 .

[38]  F. Baret,et al.  Estimates of plant density of wheat crops at emergence from very low altitude UAV imagery. , 2017 .

[39]  P. Pyšek,et al.  Timing Is Important: Unmanned Aircraft vs. Satellite Imagery in Plant Invasion Monitoring , 2017, Front. Plant Sci..

[40]  Yong He,et al.  Detection of early blight and late blight diseases on tomato leaves using hyperspectral imaging , 2015, Scientific Reports.

[41]  G. Carbone,et al.  Monitoring agricultural drought for arid and humid regions using multi-sensor remote sensing data , 2010 .

[42]  Massimo Franceschet,et al.  The role of conference publications in CS , 2010, Commun. ACM.

[43]  Izabela Karsznia,et al.  Study of fauna population changes on Penguin Island and Turret Point Oasis (King George Island, Antarctica) using an unmanned aerial vehicle , 2018, Polar Biology.

[44]  Yanbo Huang,et al.  Agricultural remote sensing big data: Management and applications , 2018, Journal of Integrative Agriculture.

[45]  F. Baret,et al.  Quantification of plant stress using remote sensing observations and crop models: the case of nitrogen management. , 2006, Journal of experimental botany.

[46]  Juha Suomalainen,et al.  Estimating Plant Traits of Grasslands from UAV-Acquired Hyperspectral Images: A Comparison of Statistical Approaches , 2015, ISPRS Int. J. Geo Inf..

[47]  Jorge Torres-Sánchez,et al.  3-D Characterization of Vineyards Using a Novel UAV Imagery-Based OBIA Procedure for Precision Viticulture Applications , 2018, Remote. Sens..

[48]  Diofantos G. Hadjimitsis,et al.  Spectral sensitivity of ALOS, ASTER, IKONOS, LANDSAT and SPOT satellite imagery intended for the detection of archaeological crop marks , 2014, Int. J. Digit. Earth.

[49]  J. M. González-Esquiva,et al.  Optimal color space selection method for plant/soil segmentation in agriculture , 2016, Comput. Electron. Agric..

[50]  Dhiman Mondal,et al.  Gradation of yellow mosaic virus disease of okra and bitter gourd based on entropy based binning and Naive Bayes classifier after identification of leaves , 2017 .

[51]  Anne-Katrin Mahlein,et al.  Benefits of hyperspectral imaging for plant disease detection and plant protection: a technical perspective , 2018 .

[52]  Yuping Lei,et al.  Estimation of water consumption and crop water productivity of winter wheat in North China Plain using remote sensing technology , 2008 .

[53]  Ramesh P. Singh,et al.  Crop yield estimation model for Iowa using remote sensing and surface parameters , 2006 .

[54]  V. Chelladurai,et al.  Detection of infestation by Callosobruchus maculatus in mung bean using near-infrared hyperspectral imaging , 2013 .

[55]  Stephen Parkes,et al.  Intra-Season Crop Height Variability at Commercial Farm Scales Using a Fixed-Wing UAV , 2018, Remote. Sens..

[56]  R. Lamparelli,et al.  Mapping and discrimination of soya bean and corn crops using spectro-temporal profiles of vegetation indices , 2015 .

[57]  M. Weiss,et al.  Remote sensing for agricultural applications: A meta-review , 2020 .

[58]  S. Ollinger,et al.  DIRECT ESTIMATION OF ABOVEGROUND FOREST PRODUCTIVITY THROUGH HYPERSPECTRAL REMOTE SENSING OF CANOPY NITROGEN , 2002 .

[59]  Jessica Lindblom,et al.  Promoting sustainable intensification in precision agriculture: review of decision support systems development and strategies , 2016, Precision Agriculture.

[60]  Yun Shi,et al.  3D Convolutional Neural Networks for Crop Classification with Multi-Temporal Remote Sensing Images , 2018, Remote. Sens..

[61]  Z. Niu,et al.  Remote estimation of canopy height and aboveground biomass of maize using high-resolution stereo images from a low-cost unmanned aerial vehicle system , 2016 .

[62]  Austin M. Jensen,et al.  Assessment of Surface Soil Moisture Using High-Resolution Multi-Spectral Imagery and Artificial Neural Networks , 2015, Remote. Sens..

[63]  Yadong Liu,et al.  Computer vision technology in agricultural automation —A review , 2020 .

[64]  Matthew Bardeen,et al.  Detection and Segmentation of Vine Canopy in Ultra-High Spatial Resolution RGB Imagery Obtained from Unmanned Aerial Vehicle (UAV): A Case Study in a Commercial Vineyard , 2017, Remote. Sens..

[65]  Nataliia Kussul,et al.  Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data , 2017, IEEE Geoscience and Remote Sensing Letters.

[66]  A. Ferrer,et al.  Pixel classification methods for identifying and quantifying leaf surface injury from digital images , 2014 .

[67]  Junho Yeom,et al.  Unmanned aerial system assisted framework for the selection of high yielding cotton genotypes , 2018, Comput. Electron. Agric..

[68]  Uwe Rascher,et al.  Meta-analysis assessing potential of steady-state chlorophyll fluorescence for remote sensing detection of plant water, temperature and nitrogen stress , 2015 .

[69]  Jana Müllerová,et al.  Remote sensing as a tool for monitoring plant invasions: Testing the effects of data resolution and image classification approach on the detection of a model plant species Heracleum mantegazzianum (giant hogweed) , 2013, Int. J. Appl. Earth Obs. Geoinformation.

[70]  Pablo J. Zarco-Tejada,et al.  Assessing Canopy PRI for Water Stress detection with Diurnal Airborne Imagery , 2008 .

[71]  Margaret Kalacska,et al.  Estimation of foliar chlorophyll and nitrogen content in an ombrotrophic bog from hyperspectral data: Scaling from leaf to image , 2015 .

[72]  Wei Li,et al.  A Comparative Assessment of Different Modeling Algorithms for Estimating Leaf Nitrogen Content in Winter Wheat Using Multispectral Images from an Unmanned Aerial Vehicle , 2018, Remote. Sens..

[73]  Martha C. Anderson,et al.  A comparison of operational remote sensing-based models for estimating crop evapotranspiration , 2009 .

[74]  Panagiotis G. Sarigiannidis,et al.  A Review on UAV-Based Applications for Precision Agriculture , 2019, Inf..

[75]  Alexei Novikov,et al.  Exploring Google Earth Engine Platform for Big Data Processing: Classification of Multi-Temporal Satellite Imagery for Crop Mapping , 2017, Front. Earth Sci..

[76]  Patrizia Busato,et al.  Machine Learning in Agriculture: A Review , 2018, Sensors.

[77]  Um Rao Mogili,et al.  Review on Application of Drone Systems in Precision Agriculture , 2018 .

[78]  Antoine Messéan,et al.  Toward a Reduced Reliance on Conventional Pesticides in European Agriculture. , 2016, Plant disease.

[79]  Pierre Defourny,et al.  A conceptual framework to define the spatial resolution requirements for agricultural monitoring using remote sensing , 2010 .

[80]  Gérard Dedieu,et al.  On the Potentiality of UAV Multispectral Imagery to Detect Flavescence dorée and Grapevine Trunk Diseases , 2018, Remote. Sens..

[81]  Pablo J. Zarco-Tejada,et al.  Estimating leaf carotenoid content in vineyards using high resolution hyperspectral imagery acquired from an unmanned aerial vehicle (UAV) , 2013 .

[82]  H. Ewe,et al.  Agricultural crop-type classification of multi-polarization SAR images using a hybrid entropy decomposition and support vector machine technique , 2011 .

[83]  Guijun Yang,et al.  Clustering Field-Based Maize Phenotyping of Plant-Height Growth and Canopy Spectral Dynamics Using a UAV Remote-Sensing Approach , 2018, Front. Plant Sci..

[84]  Eleni Vrochidou,et al.  Machine Vision Systems in Precision Agriculture for Crop Farming , 2019, J. Imaging.

[85]  Jorge Torres-Sánchez,et al.  Mapping the 3D structure of almond trees using UAV acquired photogrammetric point clouds and object-based image analysis , 2018, Biosystems Engineering.

[86]  Nicolas Baghdadi,et al.  Potential of SAR sensors TerraSAR-X, ASAR/ENVISAT and PALSAR/ALOS for monitoring sugarcane crops on Reunion Island , 2009 .

[87]  Hoam Chung,et al.  Adaptive Estimation of Crop Water Stress in Nectarine and Peach Orchards Using High-Resolution Imagery from an Unmanned Aerial Vehicle (UAV) , 2017, Remote. Sens..

[88]  De-Cheng Li,et al.  Retrieval and Mapping of Soil Texture Based on Land Surface Diurnal Temperature Range Data from MODIS , 2015, PloS one.

[89]  M. S. Moran,et al.  Remote Sensing for Crop Management , 2003 .

[90]  Simon Bennertz,et al.  Estimating Biomass of Barley Using Crop Surface Models (CSMs) Derived from UAV-Based RGB Imaging , 2014, Remote. Sens..

[91]  Xavier Maldague,et al.  Bayesian classification and unsupervised learning for isolating weeds in row crops , 2014, Pattern Analysis and Applications.

[92]  Clive H. Bock,et al.  Plant Disease Severity Estimated Visually, by Digital Photography and Image Analysis, and by Hyperspectral Imaging , 2010 .

[93]  Roberto Oberti,et al.  Plant disease detection based on data fusion of hyper-spectral and multi-spectral fluorescence imaging using Kohonen maps , 2005, Real Time Imaging.

[94]  Jorge Torres-Sánchez,et al.  An Automatic Random Forest-OBIA Algorithm for Early Weed Mapping between and within Crop Rows Using UAV Imagery , 2018, Remote. Sens..

[95]  R. P. Rocha,et al.  Weed mapping using techniques of precision agriculture , 2015 .

[96]  Clement Atzberger,et al.  Using Low Resolution Satellite Imagery for Yield Prediction and Yield Anomaly Detection , 2013, Remote. Sens..

[97]  Ginés García-Mateos,et al.  A fast and accurate expert system for weed identification in potato crops using metaheuristic algorithms , 2018, Comput. Ind..

[98]  Sergio M. Vicente-Serrano,et al.  Drought impacts on vegetation activity in the Mediterranean region: An assessment using remote sensing data and multi-scale drought indicators , 2017 .

[99]  John P. Fulton,et al.  An overview of current and potential applications of thermal remote sensing in precision agriculture , 2017, Comput. Electron. Agric..

[100]  Huazhong Ren,et al.  A new narrow band vegetation index for characterizing the degree of vegetation stress due to copper: the copper stress vegetation index (CSVI) , 2017 .

[101]  Heping Zhu,et al.  Robust Crop and Weed Segmentation under Uncontrolled Outdoor Illumination , 2011, Sensors.

[102]  Nicolas Barbier,et al.  Remote sensing detection of droughts in Amazonian forest canopies. , 2010, The New phytologist.

[103]  F. Baret,et al.  Influence of landscape spatial heterogeneity on the non-linear estimation of leaf area index from moderate spatial resolution remote sensing data , 2006 .

[104]  M. Meron,et al.  Evaluation of different approaches for estimating and mapping crop water status in cotton with thermal imaging , 2010, Precision Agriculture.

[105]  Eyal Ben-Dor,et al.  Current Practices in UAS-based Environmental Monitoring , 2020, Remote. Sens..

[106]  Mark Sanderson,et al.  Conferences versus journals in computer science , 2015, J. Assoc. Inf. Sci. Technol..

[107]  Stephan J. Maas,et al.  Index of Soil Moisture Using Raw Landsat Image Digital Count Data in Texas High Plains , 2015, Remote. Sens..

[108]  Chun Chang,et al.  Develop an unmanned aerial vehicle based automatic aerial spraying system , 2016, Comput. Electron. Agric..

[109]  Qingguo Zhou,et al.  Assimilating remote sensing information into a coupled hydrology-crop growth model to estimate regional maize yield in arid regions , 2014 .

[110]  Cyrill Stachniss,et al.  WeedMap: A large-scale semantic weed mapping framework using aerial multispectral imaging and deep neural network for precision farming , 2018, Remote. Sens..

[111]  Hemerson Pistori,et al.  Weed detection in soybean crops using ConvNets , 2017, Comput. Electron. Agric..

[112]  Juliane Bendig,et al.  UAV-based Imaging for Multi-Temporal, very high Resolution Crop Surface Models to monitor Crop Growth Variability , 2013 .

[113]  Eija Honkavaara,et al.  Estimating Biomass and Nitrogen Amount of Barley and Grass Using UAV and Aircraft Based Spectral and Photogrammetric 3D Features , 2018, Remote. Sens..

[114]  Huanhuan Yuan,et al.  A Comparison of Crop Parameters Estimation Using Images from UAV-Mounted Snapshot Hyperspectral Sensor and High-Definition Digital Camera , 2018, Remote. Sens..

[115]  Sim Heng Ong,et al.  A Small UAV Based Multi-Temporal Image Registration for Dynamic Agricultural Terrace Monitoring , 2017, Remote. Sens..

[116]  H. Jones,et al.  Thermal infrared imaging of crop canopies for the remote diagnosis and quantification of plant responses to water stress in the field. , 2009, Functional plant biology : FPB.

[117]  Luc Rolland,et al.  Development of Small UAS Beyond-Visual-Line-of-Sight (BVLOS) Flight Operations: System Requirements and Procedures , 2018 .

[118]  Y. R. Chen,et al.  Detection of Defects on Selected Apple Cultivars Using Hyperspectral and Multispectral Image Analysis , 2002 .

[119]  G. Gerdts,et al.  Identification of microplastic in effluents of waste water treatment plants using focal plane array-based micro-Fourier-transform infrared imaging. , 2017, Water research.

[120]  H. Jones,et al.  Monitoring and screening plant populations with combined thermal and chlorophyll fluorescence imaging. , 2007, Journal of experimental botany.

[121]  Jorge Torres-Sánchez,et al.  An automatic object-based method for optimal thresholding in UAV images: Application for vegetation detection in herbaceous crops , 2015, Comput. Electron. Agric..

[122]  Adel Hafiane,et al.  Deep Learning with Unsupervised Data Labeling for Weed Detection in Line Crops in UAV Images , 2018, Remote. Sens..

[123]  Michael Wachendorf,et al.  Estimation of Vegetable Crop Parameter by Multi-temporal UAV-Borne Images , 2018, Remote. Sens..

[124]  Xin Du,et al.  Remote sensing-based global crop monitoring: experiences with China's CropWatch system , 2014, Int. J. Digit. Earth.

[125]  Anna Zmarz.  Introduction to the special issue UAS for mapping and monitoring , 2019 .

[126]  Ola Hall,et al.  Remote Sensing of Yields: Application of UAV Imagery-Derived NDVI for Estimating Maize Vigor and Yields in Complex Farming Systems in Sub-Saharan Africa , 2018, Drones.

[127]  M. Schlerf,et al.  Inversion of a forest reflectance model to estimate structural canopy variables from hyperspectral remote sensing data , 2006 .

[128]  W. Maes,et al.  Perspectives for Remote Sensing with Unmanned Aerial Vehicles in Precision Agriculture. , 2019, Trends in plant science.

[129]  Young K. Chang,et al.  Current and future applications of statistical machine learning algorithms for agricultural machine vision systems , 2019, Comput. Electron. Agric..

[130]  J. L. Hernández-Hernández,et al.  Estimation of the Constituent Properties of Red Delicious Apples Using a Hybrid of Artificial Neural Networks and Artificial Bee Colony Algorithm , 2020, Agronomy.

[131]  Arturo Alvino,et al.  Detection of homogeneous wheat areas using multi-temporal UAS images and ground truth data analyzed by cluster analysis , 2018 .

[132]  Andreas Kamilaris,et al.  Deep learning in agriculture: A survey , 2018, Comput. Electron. Agric..

[133]  Yanchen Bo,et al.  Object-Based Crop Species Classification Based on the Combination of Airborne Hyperspectral Images and LiDAR Data , 2015, Remote. Sens..

[134]  Kai Petersen,et al.  Guidelines for conducting systematic mapping studies in software engineering: An update , 2015, Inf. Softw. Technol..

[135]  Adel Hafiane,et al.  Deep leaning approach with colorimetric spaces and vegetation indices for vine diseases detection in UAV images , 2018, Comput. Electron. Agric..

[136]  Ali Idri,et al.  Predicting Software Product Quality: A Systematic Mapping Study , 2015, Computación y Sistemas.

[137]  Christoph Hütt,et al.  Best Accuracy Land Use/Land Cover (LULC) Classification to Derive Crop Types Using Multitemporal, Multisensor, and Multi-Polarization SAR Satellite Images , 2016, Remote. Sens..

[138]  Y. Cohen,et al.  Use of thermal and visible imagery for estimating crop water status of irrigated grapevine. , 2006, Journal of experimental botany.

[139]  Ayse Kilic,et al.  Estimating Crop Coefficients Using Remote Sensing-Based Vegetation Index , 2013, Remote. Sens..

[140]  A. Pandey,et al.  Identification of critical erosion prone areas in the small agricultural watershed using USLE, GIS and remote sensing , 2007 .

[141]  R. Dubayah,et al.  Above-ground biomass estimation in closed canopy Neotropical forests using lidar remote sensing: factors affecting the generality of relationships , 2003 .

[142]  L. G. Santesteban,et al.  High-resolution UAV-based thermal imaging to estimate the instantaneous and seasonal variability of plant water status within a vineyard , 2017 .

[143]  David B. Lobell,et al.  Remote sensing of regional crop production in the Yaqui Valley, Mexico: estimates and uncertainties , 2003 .

[144]  Flavio Esposito,et al.  UAV-Based High Resolution Thermal Imaging for Vegetation Monitoring, and Plant Phenotyping Using ICI 8640 P, FLIR Vue Pro R 640, and thermoMap Cameras , 2019, Remote. Sens..

[145]  Zhongxin Chen,et al.  Research advances of SAR remote sensing for agriculture applications: A review , 2019, Journal of Integrative Agriculture.

[146]  Andrew P French,et al.  Hyperspectral image analysis techniques for the detection and classification of the early onset of plant disease and stress , 2017, Plant Methods.

[147]  Matthew Bardeen,et al.  Artificial Neural Network to Predict Vine Water Status Spatial Variability Using Multispectral Information Obtained from an Unmanned Aerial Vehicle (UAV) , 2017, Sensors.

[148]  Ajay Sharda,et al.  Development and evaluation of thermal infrared imaging system for high spatial and temporal resolution crop water stress monitoring of corn within a greenhouse , 2016, Comput. Electron. Agric..

[149]  Benoît Duchemin,et al.  Combining FAO-56 model and ground-based remote sensing to estimate water consumptions of wheat crops in a semi-arid region , 2007 .

[150]  Andreas Dengel,et al.  EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[151]  Yuyu Zhou,et al.  Terrestrial lidar remote sensing of forests: Maximum likelihood estimates of canopy profile, leaf area index, and leaf angle distribution , 2015 .

[152]  Yong He,et al.  Hyperspectral imaging for classification of healthy and gray mold diseased tomato leaves with different infection severities , 2017, Comput. Electron. Agric..

[153]  Michael Marshall,et al.  Developing in situ Non-Destructive Estimates of Crop Biomass to Address Issues of Scale in Remote Sensing , 2015, Remote. Sens..

[154]  J. Carreiras,et al.  Estimation of tree canopy cover in evergreen oak woodlands using remote sensing , 2006 .

[155]  George C. Zalidis,et al.  Remote Sensing Techniques for Soil Organic Carbon Estimation: A Review , 2019, Remote. Sens..

[156]  Michael E. Schaepman,et al.  Estimating canopy water content using hyperspectral remote sensing data , 2010, Int. J. Appl. Earth Obs. Geoinformation.

[157]  R. Lasaponara,et al.  Detection of archaeological crop marks by using satellite QuickBird multispectral imagery , 2007 .

[158]  Olga Sykioti,et al.  Monitoring canopy biophysical and biochemical parameters in ecosystem scale using satellite hyperspectral imagery: An application on a Phlomis fruticosa Mediterranean ecosystem using multiangular CHRIS/PROBA observations , 2010 .

[159]  H. Jones,et al.  Combining thermal and visible imagery for estimating canopy temperature and identifying plant stress. , 2004, Journal of experimental botany.

[160]  Y. Cohen,et al.  Estimation of leaf water potential by thermal imagery and spatial analysis. , 2005, Journal of experimental botany.

[161]  Martha C. Anderson,et al.  Utility of an image-based canopy reflectance modeling tool for remote estimation of LAI and leaf chlorophyll content at the field scale , 2009 .

[162]  Kemal Gökkaya,et al.  Subsurface tile drained area detection using GIS and remote sensing in an agricultural watershed , 2017 .

[163]  D. Moher,et al.  Preferred reporting items for systematic reviews and meta-analyses: the PRISMA Statement , 2009, BMJ : British Medical Journal.

[164]  Rita Der Sarkissian,et al.  The use of geospatial information as support for Disaster Risk Reduction; contextualization to Baalbek-Hermel Governorate/Lebanon , 2019, Applied Geography.

[165]  Clement Atzberger,et al.  Advances in Remote Sensing of Agriculture: Context Description, Existing Operational Monitoring Systems and Major Information Needs , 2013, Remote. Sens..

[166]  Alfonso Calera,et al.  Remote Sensing for Crop Water Management: From ET Modelling to Services for the End Users , 2017, Sensors.

[167]  Digvir S. Jayas,et al.  Hyperspectral imaging to classify and monitor quality of agricultural materials , 2015 .

[168]  L. Plümer,et al.  Detection of early plant stress responses in hyperspectral images , 2014 .

[169]  Adèle Paul-Hus,et al.  The journal coverage of Web of Science and Scopus: a comparative analysis , 2015, Scientometrics.

[170]  Claire Marais-Sicre,et al.  Improved Early Crop Type Identification By Joint Use of High Temporal Resolution SAR And Optical Image Time Series , 2016, Remote. Sens..

[171]  W. Maes,et al.  Estimating evapotranspiration and drought stress with ground-based thermal remote sensing in agriculture: a review. , 2012, Journal of experimental botany.

[172]  Ikuyo Kaneko,et al.  A Geographic Information System (GIS)-based approach to adaptation to regional climate change: a case study of Okutama-machi, Tokyo, Japan , 2014, Mitigation and Adaptation Strategies for Global Change.

[173]  Pablo J. Zarco-Tejada,et al.  Airborne Thermal Imagery to Detect the Seasonal Evolution of Crop Water Status in Peach, Nectarine and Saturn Peach Orchards , 2016, Remote. Sens..

[174]  Guido Lemoine,et al.  Parcel-Based Crop Classification in Ukraine Using Landsat-8 Data and Sentinel-1A Data , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[175]  Nataliia Kussul,et al.  Efficiency Assessment of Multitemporal C-Band Radarsat-2 Intensity and Landsat-8 Surface Reflectance Satellite Imagery for Crop Classification in Ukraine , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[176]  Ali Idri,et al.  Requirements engineering education: a systematic mapping study , 2013, Requirements Engineering.

[177]  M. S. Moran,et al.  Opportunities and limitations for image-based remote sensing in precision crop management , 1997 .

[178]  Vítězslav Moudrý,et al.  Comparison of leaf-off and leaf-on combined UAV imagery and airborne LiDAR for assessment of a post-mining site terrain and vegetation structure: Prospects for monitoring hazards and restoration success , 2019, Applied Geography.

[179]  Pablo J. Zarco-Tejada,et al.  Early Detection and Quantification of Almond Red Leaf Blotch Using High-Resolution Hyperspectral and Thermal Imagery , 2016, Remote. Sens..