Remote Sensing in Agriculture-Accomplishments, Limitations, and Opportunities

被引:263
作者
Khanal, Sami [1 ]
Kushal, K. C. [1 ]
Fulton, John P. [1 ]
Shearer, Scott [1 ]
Ozkan, Erdal [1 ]
机构
[1] Ohio State Univ, Dept Food Agr & Biol Engn, Columbus, OH 43210 USA
关键词
remote sensing; satellite; UAS; precision agriculture; MACHINE-LEARNING TECHNIQUES; ELEVATION DATA SOURCES; RTK GPS SURVEY; SOIL-MOISTURE; NITROGEN MANAGEMENT; WINTER-WHEAT; VEGETATION INDEXES; PROTEIN-CONTENT; AERIAL IMAGES; WATER STATUS;
D O I
10.3390/rs12223783
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Remote sensing (RS) technologies provide a diagnostic tool that can serve as an early warning system, allowing the agricultural community to intervene early on to counter potential problems before they spread widely and negatively impact crop productivity. With the recent advancements in sensor technologies, data management and data analytics, currently, several RS options are available to the agricultural community. However, the agricultural sector is yet to implement RS technologies fully due to knowledge gaps on their sufficiency, appropriateness and techno-economic feasibilities. This study reviewed the literature between 2000 to 2019 that focused on the application of RS technologies in production agriculture, ranging from field preparation, planting, and in-season applications to harvesting, with the objective of contributing to the scientific understanding on the potential for RS technologies to support decision-making within different production stages. We found an increasing trend in the use of RS technologies in agricultural production over the past 20 years, with a sharp increase in applications of unmanned aerial systems (UASs) after 2015. The largest number of scientific papers related to UASs originated from Europe (34%), followed by the United States (20%) and China (11%). Most of the prior RS studies have focused on soil moisture and in-season crop health monitoring, and less in areas such as soil compaction, subsurface drainage, and crop grain quality monitoring. In summary, the literature highlighted that RS technologies can be used to support site-specific management decisions at various stages of crop production, helping to optimize crop production while addressing environmental quality, profitability, and sustainability.
引用
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页码:1 / 29
页数:29
相关论文
共 163 条
[1]  
Abdel-Hady M., 1970, Proceedings of the Oklahoma Acadamy of Science, V50, P10
[2]   Estimation of soil moisture at different soil levels using machine learning techniques and unmanned aerial vehicle (UAV) multispectral imagery [J].
Aboutalebi, Mahyar ;
Allen, L. Niel ;
Torres-Rua, Alfonso F. ;
McKee, Mac ;
Coopmans, Calvin .
AUTONOMOUS AIR AND GROUND SENSING SYSTEMS FOR AGRICULTURAL OPTIMIZATION AND PHENOTYPING IV, 2019, 11008
[3]  
Alaoui A, 2018, CURR OPIN ENV SCI HL, V5, P60, DOI 10.1016/j.coesh.2018.05.003
[4]   Review of Machine Learning Approaches for Biomass and Soil Moisture Retrievals from Remote Sensing Data [J].
Ali, Iftikhar ;
Greifeneder, Felix ;
Stamenkovic, Jelena ;
Neumann, Maxim ;
Notarnicola, Claudia .
REMOTE SENSING, 2015, 7 (12) :16398-16421
[5]   Leaf nitrogen determination using non-destructive techniques-A review [J].
Ali, M. M. ;
Al-Ani, Ahmed ;
Eamus, Derek ;
Tan, Daniel K. Y. .
JOURNAL OF PLANT NUTRITION, 2017, 40 (07) :928-953
[6]   Overall results and key findings on the use of UAV visible-color, multispectral, and thermal infrared imagery to map agricultural drainage pipes [J].
Allred, Barry ;
Martinez, Luis ;
Fessehazion, Melake K. ;
Rouse, Greg ;
Williamson, Tanja N. ;
Wishart, DeBonne ;
Koganti, Triven ;
Freeland, Robert ;
Eash, Neal ;
Batschelet, Adam ;
Featheringill, Robert .
AGRICULTURAL WATER MANAGEMENT, 2020, 232
[7]   Effective and efficient agricultural drainage pipe mapping with UAS thermal infrared imagery: A case study [J].
Allred, Barry ;
Eash, Neal ;
Freeland, Robert ;
Martinez, Luis ;
Wishart, DeBonne .
AGRICULTURAL WATER MANAGEMENT, 2018, 197 :132-137
[8]   Two new soil moisture indices based on the NIR-red triangle space of Landsat-8 data [J].
Amani, Meisam ;
Parsian, Saeid ;
MirMazloumi, S. Mohammad ;
Aieneh, Omid .
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2016, 50 :176-186
[9]   USING SATELLITE DATA TO MAP FALSE BROOMWEED (ERICAMERIA-AUSTROTEXANA) INFESTATIONS ON SOUTH TEXAS RANGELANDS [J].
ANDERSON, GL ;
EVERITT, JH ;
RICHARDSON, AJ ;
ESCOBAR, DE .
WEED TECHNOLOGY, 1993, 7 (04) :865-871
[10]  
[Anonymous], 1973, P S SIGN RES ERTS 1