Remote Sensing Applications for Agriculture and Crop Modelling
Remote Sensing Applications for Agriculture and Crop Modelling

Author: Piero Toscano

Publisher: MDPI

Published: 2020-02-13

Total Pages: 308

ISBN-13: 3039282263

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Crop models and remote sensing techniques have been combined and applied in agriculture and crop estimation on local and regional scales, or worldwide, based on the simultaneous development of crop models and remote sensing. The literature shows that many new remote sensing sensors and valuable methods have been developed for the retrieval of canopy state variables and soil properties from remote sensing data for assimilating the retrieved variables into crop models. At the same time, remote sensing has been used in a staggering number of applications for agriculture. This book sets the context for remote sensing and modelling for agricultural systems as a mean to minimize the environmental impact, while increasing production and productivity. The eighteen papers published in this Special Issue, although not representative of all the work carried out in the field of Remote Sensing for agriculture and crop modeling, provide insight into the diversity and the complexity of developments of RS applications in agriculture. Five thematic focuses have emerged from the published papers: yield estimation, land cover mapping, soil nutrient balance, time-specific management zone delineation and the use of UAV as agricultural aerial sprayers. All contributions exploited the use of remote sensing data from different platforms (UAV, Sentinel, Landsat, QuickBird, CBERS, MODIS, WorldView), their assimilation into crop models (DSSAT, AQUACROP, EPIC, DELPHI) or on the synergy of Remote Sensing and modeling, applied to cardamom, wheat, tomato, sorghum, rice, sugarcane and olive. The intended audience is researchers and postgraduate students, as well as those outside academia in policy and practice.

Remote Sensing Applications for Agriculture and Crop Modelling
Remote Sensing Applications for Agriculture and Crop Modelling

Author: Piero Toscano

Publisher:

Published: 2020

Total Pages: 308

ISBN-13: 9783039282272

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Crop models and remote sensing techniques have been combined and applied in agriculture and crop estimation on local and regional scales, or worldwide, based on the simultaneous development of crop models and remote sensing. The literature shows that many new remote sensing sensors and valuable methods have been developed for the retrieval of canopy state variables and soil properties from remote sensing data for assimilating the retrieved variables into crop models. At the same time, remote sensing has been used in a staggering number of applications for agriculture. This book sets the context for remote sensing and modelling for agricultural systems as a mean to minimize the environmental impact, while increasing production and productivity. The eighteen papers published in this Special Issue, although not representative of all the work carried out in the field of Remote Sensing for agriculture and crop modeling.

Applications of Remote Sensing in Agriculture
Applications of Remote Sensing in Agriculture

Author: M. D. Steven

Publisher: Elsevier

Published: 2013-10-22

Total Pages: 444

ISBN-13: 1483161781

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Applications of Remote Sensing in Agriculture contains the proceedings of the 48th Easter School in Agricultural Science, held at the University of Nottingham on April 3-7, 1989. The meeting invites 146 delegates from over 22 countries and contributions to this book come from nine countries. This book generally presents a review of the achievements of remote sensing in agriculture, establishes the state of the art, and gives pointers to developments. This text is organized into seven parts, wherein Parts I-III cover the principles of remote sensing, climate, soil, land classification, and crop inventories. Productivity; stress; techniques for agricultural applications; and opportunities, progress, and prospects in the field of remote sensing in agriculture are also discussed.

Remote Sensing in Precision Agriculture
Remote Sensing in Precision Agriculture

Author: Salim Lamine

Publisher: Elsevier

Published: 2023-10-20

Total Pages: 555

ISBN-13: 0323914640

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Remote Sensing in Precision Agriculture: Transforming Scientific Advancement into Innovation compiles the latest applications of remote sensing in agriculture using spaceborne, airborne and drones’ geospatial data. The book presents case studies, new algorithms and the latest methods surrounding crop sown area estimation, determining crop health status, assessment of vegetation dynamics, crop diseases identification, crop yield estimation, soil properties, drone image analysis for crop damage assessment, and other issues in precision agriculture. This book is ideal for those seeking to explore and implement remote sensing in an effective and efficient manner with its compendium of scientifically and technologically sound information. Presents a well-integrated collection of chapters, with quality, consistency and continuity Provides the latest RS techniques in Precision Agriculture that are addressed by leading experts Includes detailed, yet geographically global case studies that can be easily understood, reproduced or implemented Covers geospatial data, with codes available through shared links

UAS-Remote Sensing Methods for Mapping, Monitoring and Modeling Crops
UAS-Remote Sensing Methods for Mapping, Monitoring and Modeling Crops

Author: Francisco Javier Mesas Carrascosa

Publisher: MDPI

Published: 2021-04-22

Total Pages: 174

ISBN-13: 3036505261

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The advances in unmanned aerial vehicle (UAV) platforms and onboard sensors in the past few years have greatly increased our ability to monitor and map crops. The ability to register images at ultrahigh spatial resolution at any moment has made remote sensing techniques increasingly useful in crop management. These technologies have revolutionized the way in which remote sensing is applied in precision agriculture, allowing for decision-making in a matter of days instead of weeks. However, it is still necessary to continue research to improve and maximize the potential of UAV remote sensing in agriculture. This Special Issue of Remote Sensing includes different applications of UAV remote sensing for crop management, covering RGB, multispectral, hyperspectral and light detection and ranging (LiDAR) sensor applications aboard UAVs. The papers reveal innovative techniques involving image analysis and cloud points. However, it should be emphasized that this Special Issue is a small sample of UAV applications in agriculture and that there is much more to investigate.

Land Surface Remote Sensing in Agriculture and Forest
Land Surface Remote Sensing in Agriculture and Forest

Author: Nicolas Baghdadi

Publisher: Elsevier

Published: 2016-09-15

Total Pages: 498

ISBN-13: 0081011830

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The environmental and economic importance of monitoring forests and agricultural resources has allowed remote sensing to be increasingly in the development of products and services responding to user needs.This volume presents the main applications in remote sensing for agriculture and forestry, including the primary soil properties, the estimation of the vegetation’s biophysical variables, methods for mapping land cover, the contribution of remote sensing for crop and water monitoring, and the estimation of the forest cover properties (cover dynamic, height, biomass).This book, part of a set of six volumes, has been produced by scientists who are internationally renowned in their fields. It is addressed to students (engineers, Masters, PhD), engineers and scientists, specialists in remote sensing applied to agriculture and forestry.Through this pedagogical work, the authors contribute to breaking down the barriers that hinder the use of radar imaging techniques. Provides clear and concise descriptions of modern remote sensing methods Explores the most current remote sensing techniques with physical aspects of the measurement (theory) and their applications Provides chapters on physical principles, measurement, and data processing for each technique described Describes optical remote sensing technology, including a description of acquisition systems and measurement corrections to be made

Review of the available remote sensing tools, products, methodologies and data to improve crop production forecasts
Review of the available remote sensing tools, products, methodologies and data to improve crop production forecasts

Author: Food and Agriculture Organization of the United Nations

Publisher: Food & Agriculture Org.

Published: 2018-05-31

Total Pages: 94

ISBN-13: 9251098409

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Timely and reliable agricultural production forecasts are critical to make informed food policy decisions and enable rapid responses to emerging food shortfalls. Sub-Saharan Africa is subject to highly variable yield, production and consumption, occasioned by high climate variability, rapidly increasing populations, and limited financial capacity. This review examines the current status of the remote sensing (RS) tools, products, methodologies and data that can help to improve agricultural crop production forecasting systems.

Remote Sensing Application II
Remote Sensing Application II

Author: Tofael Ahamed

Publisher: Springer

Published: 2024-05-22

Total Pages: 0

ISBN-13: 9789819711871

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This book focuses solely on the issues of agricultural productivity analysis with advanced modeling approaches bringing solutions to food-insecure regions of the world, especially in south and southeast Asia and in Africa. Advanced modeling tools and their use in regional planning provide an outstanding opportunity to help face the challenges of climate change. The sudden effect of flash floods, drought, salinity, and sea water rises causing saltwater intrusions and its impact on agricultural production are some of the disastrous results of climate change. In this edited volume, information on climate-induced impacts for flooding, flash floods, and drought impact on agricultural crops is provided to address possible solutions for food security in south Asia, southeast Asia, and some regions of Africa. Leading-edge research methodology is presented as it relates to remote sensing applications for regional science and allied fields. In regional policy planning, agriculture and forestry play key roles in food security along with environmental conservation and depend on geo-spatial variability. Satellite remote sensing and geographical information systems have an immense potential to encompass all these factors and to catalogue the regional variability of climate change and climate economics. In the satellite remote sensing domain, advanced modeling tools, deep learning applications, and cloud-based earth engines significantly increase the flexibility of decision making and its application for regional perspectives. The result can increase agricultural and forest productivity and ensure its resilience and sustainability. The book’s chapters introduce modeling techniques such as machine learning and fuzzy expert system using satellite remote sensing datasets based on cloud application. These methods assist regional planners to increase crop production, land use, and detection of changes in land cover in order to better understand their vulnerability to climate-related disaster. Furthermore, remote sensing and in-depth GIS analysis are integrated with machine learning to address natural uncertainties such as flash floods, droughts, and cyclones so that emergency responses for agricultural production management can be adopted more effectively.

Applications of Crop Growth Models in Precision Agriculture Through a GIS Linkage and Remote Sensing
Applications of Crop Growth Models in Precision Agriculture Through a GIS Linkage and Remote Sensing

Author: Matthew Stephen Seidl

Publisher:

Published: 2000

Total Pages: 98

ISBN-13:

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Crop growth models are finding new uses in the area of precision farming. Two crop growth models, CERES-Maize and CROPGRO-Soybean, have recently been used to explain corn and soybean yield variability in a field in Iowa. A visual interface would facilitate management and analysis of the vast amount of data required for use of crop growth models to analyze spatial yield variability. The first objective of this thesis is to describe the design and application of a new system which links these two crop growth models to the ArcView 3.1 Geographic Information System (GIS). This program, called Crop Models Analyst, allows the user to: 1)create maps of any of the 200 variables predicted by the models on each day of simulation; 2) interactively run the model from the GIS to test hypotheses and to make comparisons with measured data; and 3) evaluate prescriptions over multiple years of simulation. The second objective of this thesis is to describe the use of imagery as an input data layer to the CROPGRO-Soybean model. The driving force behind remote sensing is the desire to cut the costs normally required for data collection and analysis. Incorporation of imagery into crop growth models is a natural fit, as the crop model is currently the only tool which can integrate the complex systems that cause yield variability into a single predictive package. It is demonstrated that the addition of imagery provides valuable information about the spatial distribution of soybean biomass across a field.

Remote Sensing Application for Precision Agriculture
Remote Sensing Application for Precision Agriculture

Author: Matthew McCabe

Publisher: Frontiers Media SA

Published: 2023-08-11

Total Pages: 372

ISBN-13: 2832531822

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Precision agriculture is used to improve site-specific agricultural decision-making based on data collection and analysis, formulation of site-specific management recommendations, and implementation of management practices to correct for factors that can limit crop growth, yield, and quality. Various approaches for the remote sensing of soil fertility, water stress, diseases and infestations, and crop growth and condition have been developed and applied for precision agricultural purposes. With developments in remote sensing technologies, the spatial and spectral resolution and return frequencies available from both satellite and other remote collection platforms have improved to the point that the promise of precision agriculture can increasingly be realized. Unmanned aerial vehicles (UAV) in particular are providing newer and deeper insights, leveraging their high resolution, sensor-carrying flexibility and dynamic acquisition schedule. This range of remote sensing platforms has been used to estimate comprehensive information related to crop health and dynamics, providing rapid retrievals of leaf area index, canopy cover, chlorophyll, nitrogen, canopy/leaf water content, canopy/leaf temperature, biomass, and yield, amongst many other variables of interest. In combination, they allow for the expansion from local to regional scales and beyond. There has never been a greater opportunity for remote sensing data to enable precision agricultural insights that can be used to better monitor, manage and respond to in-field changes that might impact crop growth, health and yield.