A Remote Sensing Driven Geospatial Approach to Regional Crop Growth and Yield Modeling
A Remote Sensing Driven Geospatial Approach to Regional Crop Growth and Yield Modeling

Author: Sadia Alam Shammi

Publisher:

Published: 2021

Total Pages: 0

ISBN-13:

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Agriculture and food security are interlinked. New technologies and instruments are making the agricultural system easy to operate and increasing the food production. Remote sensing technology is widely used as a non-destructive method for crop growth monitoring, climate analysis, and forecasting crop yield. The objectives of this study are to (1) monitor crop growth remotely, (2) identify climate impacts on crop yield, and (3) forecasting crop yield. This study proposed methods to improve crop growth monitoring and yield predictions by using remote sensing technology. In this study, we developed crop vegetative growth metrics (VGM) from the MODIS (Moderate Resolution Imaging Spectroradiometer) 250m NDVI (Normalized Difference Vegetation Index) and EVI (Enhanced Vegetation Index) data. We developed 19 NDVI and EVI based VGM metrics for soybean crop from a time series of 2000 to 2018, but the methods are applicable to other crops as well. We found VGMmax, VGM70, VGM85, VGM98T are about 95% crop yield predictable. However, these metrics are independent of climatic events. We modelled the climatic impacts on soybean crop from the time series data from1980-2019 collected from NOAA's National Climatic Data Center (NCDC). Therefore, we estimated the impacts of increase and decrease of temperature (maximum, mean, and minimum) and precipitation (average) pattern on crop yields which will be helpful to monitor climate change impacts on crop production. Lastly, we made crop yield forecasting statistical model across different climatic regions in USA using Google Earth Engine. We used remotely sensed MODIS Terra surface reflectance 8-day global 250m data to calculate VGM metrics (e.g. VGM70, VGM85, VGM98T, VGM120, VGMmean, and VGMmax), MODIS Terra land surface temperature and Emissivity 8-Day data for average day-time and night-time temperature and CHIRPS (Climate Hazards Group Infra-red Precipitation with station data) data for precipitation, from a time series data of 2000-2019. Our predicted models showed a NMPE (Normalized Mean Prediction error) with in a range of -0.002 to 0.007. These models will be helpful to get an overall estimate of crop production and aid in national agricultural strategic planning. Overall, this study will benefit farmers, researchers, and management system of U.S. Department of Agriculture (USDA).

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.

Biometeorology in Integrated Pest Management
Biometeorology in Integrated Pest Management

Author: Jerry Hatfield

Publisher: Elsevier

Published: 2012-12-02

Total Pages: 502

ISBN-13: 0323147968

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Biometeorology in Integrated Pest Management is a resulting book from a conference with the same title held at the University of California in 1980. This book presents integrated pest management (IPM) in different viewpoints and perspectives. It serves as a helpful exchange of ideas to strengthen the research in integrated pest management. From a biometeorological viewpoint, the microclimate of agricultural systems is introduced in this book to describe the environment in which pests live. The first few chapters in this book discuss IPM in the perspective of biometeorology. Some of the topics include crop canopies (general heat exchange and wind movement), microclimate (instrumentation, techniques, and simulation), and microclimatic stress (remote sensing). The following section of the book focuses on plant pathology. The subject areas covered in this section include radiation quality and plant diseases; management of plant pathogens; and plant canopy modification and impact on plant disease. The last section focuses on weed science. The interaction of weeds to other pests, effects of light and temperature on weed growth, and weed seed germination are some of the topics discussed in this part. This book is a good source of reference to both students and professionals in the field of biometeorology, entomology, and agriculture. Other interested parties in the research of integrated pest management will also find this book helpful in their endeavors.

Remote Sensing of Agriculture and Land Cover/Land Use Changes in South and Southeast Asian Countries
Remote Sensing of Agriculture and Land Cover/Land Use Changes in South and Southeast Asian Countries

Author: Krishna Prasad Vadrevu

Publisher: Springer Nature

Published: 2022-03-28

Total Pages: 618

ISBN-13: 3030923657

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This book sheds new light on the remote sensing of agriculture in South/Southeast Asian (S/SEA) countries. S/SEA countries are growing rapidly in terms of population, industrialization, and urbanization. One of the critical challenges in the region is food security. In S/SEA, although total food production and productivity have increased in previous decades, in recent years, the growth rate of food production has slowed down, mostly due to land use change, market forces and policy interventions. Further, the weather and climate systems in the region driven primarily by monsoon variability are resulting in droughts or flooding, impacting agricultural production. Therefore, monitoring crops, including agricultural land cover changes at regular intervals, is essential to predict and prepare for disruptions in the food supply in the S/SEA countries. The current book captures the latest research on the remote sensing of agricultural land cover/ land use changes, including mapping and monitoring crops, crop yields, biophysical parameter retrievals, multi-source data fusion for agricultural applications, and chapters on decision making and early warning systems for food security. The authors of this book are international experts in the field, and their contributions highlight the use of remote sensing and geospatial technologies for agricultural research and applications in South/Southeast Asia.

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
Remote Sensing Application

Author: Tofael Ahamed

Publisher: Springer Nature

Published: 2022-05-06

Total Pages: 373

ISBN-13: 9811902135

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This book focuses solely on the issues of agriculture and forest productivity analysis with advanced modeling approaches to bring solutions to food-insecure regions of South and Southeast Asia. Advanced modeling tools and their use in regional planning provide an outstanding opportunity to contribute toward food production and environments. In this book, leading-edge research methodologies related to remote sensing and geospatial variability of soil, water, and regional agricultural production indicators and their applications are introduced together—a unique feature of the book is the domain of regional policy perspectives and allied fields. In regional policy planning, agriculture and forestry have a key role in food security and environmental conservation that depends on the geo-spatial variability of these factors. Over the years, nature and climate have determined the variability of soil type, soil quality, geographical deviation for habitat, water quality, water sources, urban influences, population growth, carbon stock levels, and water resources with rain-fed or irrigated land use practices. In addition, human nutritional values and dietary habits have brought cultural adaptation of either mono- or multi-cropping patterns in the region. To encompass all these above mentioned factors and classify regional variability for policy planning, satellite remote sensing and geographical information systems have the immense potential to increase agricultural and forest productivity to ensure the resilience of its sustainability. Therefore, the 13 chapters presented in this book introduce modeling techniques using the signatures of vegetation and water indices, land use and land change dynamics, climatic, and socioeconomic criteria through spatial, temporal, and statistical analysis. As well, remote sensing and in-depth GIS analysis are integrated with machine and deep learning algorithms to address natural uncertainties such as flash floods, droughts, and cyclones in agricultural production management.

Geospatial Modeling for Environmental Management
Geospatial Modeling for Environmental Management

Author: Shruti Kanga

Publisher: CRC Press

Published: 2022-02-16

Total Pages: 390

ISBN-13: 1000539202

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This is a comprehensive resource that integrates the application of innovative remote sensing techniques and geospatial tools in modeling Earth systems for environmental management beyond customary digitization and mapping practices. It identifies the most suitable approaches for a specific environmental problem, emphasizes the importance of physically based modeling, their uncertainty analysis, advantages, and disadvantages. The case studies on the Himalayas with a complex topography call for innovation in geospatial techniques to find solutions for various environmental problems. Features: Presents innovative geospatial methods in environmental modeling of Earth systems. Includes case studies from South Asia and discusses different processes and outcomes using spatially explicit models. Explains contemporary environmental problems through the analysis of various information layers. Provides good practices for developing countries to help manage environmental issues using low-cost geospatial approaches. Integrates geospatial modeling with policy and analysis its direct implication in decision making. Using a systems’ approach analysis, Geospatial Modeling for Environmental Management: Case Studies from South Asia shall serve environmental managers, students, researchers, and policymakers.

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.

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.