Systematic Mapping Study on Remote Sensing in Agriculture

被引:35
作者
Alberto Garcia-Berna, Jose [1 ]
Ouhbi, Sofia [2 ]
Benmouna, Brahim [1 ]
Garcia-Mateos, Gines [1 ]
Luis Fernandez-Aleman, Jose [1 ]
Miguel Molina-Martinez, Jose [3 ]
机构
[1] Univ Murcia, Dept Comp Sci & Syst, Murcia 30100, Spain
[2] United Arab Emirates Univ, CIT, Dept Comp Sci & Software Engn, Al Ain 15551, U Arab Emirates
[3] Tech Univ Cartagena, Food Engn & Agr Equipment Dept, Cartagena 30203, Spain
来源
APPLIED SCIENCES-BASEL | 2020年 / 10卷 / 10期
关键词
remote images; systematic mapping study; agriculture; applications; SPATIAL-RESOLUTION REQUIREMENTS; LEAF-AREA INDEX; LAND-COVER; VEGETATION INDEX; SOIL-MOISTURE; TOMATO LEAVES; WATER-STRESS; HYPERSPECTRAL IMAGERY; CROP CLASSIFICATION; ABOVEGROUND BIOMASS;
D O I
10.3390/app10103456
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
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.
引用
收藏
页数:29
相关论文
共 209 条
[1]   Meta-analysis assessing potential of steady-state chlorophyll fluorescence for remote sensing detection of plant water, temperature and nitrogen stress [J].
Ac, Alexander ;
Malenovsky, Zbynek ;
Olejnickova, Julie ;
Galle, Alexander ;
Rascher, Uwe ;
Mohammed, Gina .
REMOTE SENSING OF ENVIRONMENT, 2015, 168 :420-436
[2]   Hyperspectral Imaging: A Review on UAV-Based Sensors, Data Processing and Applications for Agriculture and Forestry [J].
Adao, Telmo ;
Hruska, Jonas ;
Padua, Luis ;
Bessa, Jose ;
Peres, Emanuel ;
Morais, Raul ;
Sousa, Joaquim Joao .
REMOTE SENSING, 2017, 9 (11)
[3]   Spectral sensitivity of ALOS, ASTER, IKONOS, LANDSAT and SPOT satellite imagery intended for the detection of archaeological crop marks [J].
Agapiou, Athos ;
Alexakis, Dimitrios D. ;
Hadjimitsis, Diofantos G. .
INTERNATIONAL JOURNAL OF DIGITAL EARTH, 2014, 7 (05) :351-372
[4]   On the Potentiality of UAV Multispectral Imagery to Detect Flavescence doree and Grapevine Trunk Diseases [J].
Albetis, Johanna ;
Jacquin, Anne ;
Goulard, Michel ;
Poilve, Herve ;
Rousseau, Jacques ;
Clenet, Harold ;
Dedieu, Gerard ;
Duthoit, Sylvie .
REMOTE SENSING, 2019, 11 (01)
[5]   Evaluation of different approaches for estimating and mapping crop water status in cotton with thermal imaging [J].
Alchanatis, V. ;
Cohen, Y. ;
Cohen, S. ;
Moller, M. ;
Sprinstin, M. ;
Meron, M. ;
Tsipris, J. ;
Saranga, Y. ;
Sela, E. .
PRECISION AGRICULTURE, 2010, 11 (01) :27-41
[6]   Remote sensing detection of droughts in Amazonian forest canopies [J].
Anderson, Liana O. ;
Malhi, Yadvinder ;
Aragao, Luiz E. O. C. ;
Ladle, Richard ;
Arai, Egidio ;
Barbier, Nicolas ;
Phillips, Oliver .
NEW PHYTOLOGIST, 2010, 187 (03) :733-750
[7]   Remote Sensing Techniques for Soil Organic Carbon Estimation: A Review [J].
Angelopoulou, Theodora ;
Tziolas, Nikolaos ;
Balafoutis, Athanasios ;
Zalidis, George ;
Bochtis, Dionysis .
REMOTE SENSING, 2019, 11 (06)
[8]  
[Anonymous], 2017, P IEEE C COMPUTER VI
[9]  
[Anonymous], 2016, P INT C AGR ENG CIGR
[10]  
[Anonymous], 2019, IEEE J STARS, DOI DOI 10.1109/JSTARS.2019.2918242