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Using Low Resolution Satellite Imagery for Yield Prediction and Yield Anomaly Detection
2013
Remote Sensing
Preserved Fulltext
Low resolution satellite imagery has been extensively used for crop monitoring and yield forecasting for over 30 years and plays an important role in a growing number of operational systems. ...
Several qualitative and quantitative approaches can be clearly distinguished, going from the use of low resolution satellite imagery as the main predictor of final crop yield to complex crop growth models ...
Abstract: Low resolution satellite imagery has been extensively used for crop monitoring and yield forecasting for over 30 years and plays an important role in a growing number of operational systems. ...
doi:10.3390/rs5041704
fatcat:oq23wwe56vbzfeobfaaadfw2jq
Correction: Rembold, F.; Atzberger, C.; Savin, I.; Rojas, O. Using Low Resolution Satellite Imagery for Yield Prediction and Yield Anomaly Detection. Remote Sens. 2013, 5, 1704-1733
2013
Remote Sensing
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Due to an oversight by the authors, in the upper graph in Figure 4 [1] only the determination coefficients for Morocco are correct. Those for the other three countries are wrong. ...
NDVI/yield linear regressions for cereals in North Africa (from Maselli and Rembold [46] ; modified). ...
Each dot corresponds to the annual yield for agricultural areas at national level and to the monthly NDVI best correlated to yield. The authors apologize for the inconvenience. ...
doi:10.3390/rs5115572
fatcat:2jlkn7xitrhordod2jr44t2qoq
Real-Time Prediction Of Crop Yields From Modis Relative Vegetation Health: A Continent-Wide Analysis Of Africa
2018
Zenodo
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Here I develop satellite analysis methods and software tools to predict crop yields two to four months before the harvest. ...
Correlations were computed between corn, soybean, and sorghum yields and monthly vegetation health anomalies for every county and year. ...
Conclusions
295 In this research, I developed a method to use three measures of crop health computed from daily MODIS 296 satellite imagery as a predictive tool for crop yields 2-4 months before the ...
doi:10.5281/zenodo.1436016
fatcat:q3akykqringzvhq6hhss6riwvq
Real-Time Prediction of Crop Yields From MODIS Relative Vegetation Health: A Continent-Wide Analysis of Africa
2018
Remote Sensing
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Here, I develop satellite analysis methods and software tools to predict crop yields two to four months before the harvest. ...
Correlations were computed between corn, soybean, and sorghum yields and monthly vegetation health anomalies for every county and year. ...
These two years are used as examples to show corn yield and satellite anomalies at the county level ( Figure 6 ). Next, the relationships were examined at a higher resolution. ...
doi:10.3390/rs10111726
fatcat:2424oacmzfdkflv6cob6tsmbma
Prospects of Improving Agricultural and Water Productivity through Unmanned Aerial Vehicles
2020
Agriculture
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UAVs mounted with multispectral and thermal cameras facilitate the monitoring of crops throughout the crop growing cycle, allowing for timely detection and intervention in case of any anomalies. ...
Coupled with accurate meteorological data, the technology allows for precise estimations of crop water requirements and crop evapotranspiration at high spatial resolution. ...
The authors would also like to acknowledge the International Water Management Institute (IWMI) for the use of its drone.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/agriculture10070256
fatcat:5rjl4hrrabdu3gewzxt2mi5ij4
Optimal Image Smoothing and Its Applications in Anomaly Detection in Remote Sensing
[article]
2020
arXiv
pre-print
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It is also very fast and can be used for detecting the anomalies in the image. A real anomaly detecting problem is considered for the Qom region in Iran. ...
Discretizing the Laplace operator and using the method of Euler-Lagrange result in a weighted average scheme for the optimal smoother. Satellite imagery can be smoothed by this optimal smoother. ...
Since the images used for agricultural purposes (especially crop yield) are typically low-resolution, a distinct method of yield anomaly detection is presented to deal with this problem. ...
arXiv:2003.08210v1
fatcat:d2elrmbqbbgrdktgzehndkzm2i
Remote Sensing Applications in Sugarcane Cultivation: A Review
2021
Remote Sensing
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More than one hundred scientific studies were assessed regarding sugarcane mapping (52 papers), crop growth anomaly detection (11 papers), health monitoring (14 papers), and yield estimation (30 papers ...
For very small areas, and in particular for up-scaling and calibration purposes, unmanned aerial vehicles (UAV) are also useful. ...
Acknowledgments: We are grateful for the comments and suggestions offered by anonymous reviewers to improve our review article. ...
doi:10.3390/rs13204040
fatcat:dfpsctv7yzgjzogww7u56hejt4
Enhancing Environmental Enforcement with Near Real-Time Monitoring: Likelihood-Based Detection of Structural Expansion of Intensive Livestock Farms
[article]
2021
arXiv
pre-print
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2021 accepted arXiv:2105.14159v2 fatcat:dvutf6eutrfvlda64oo3upc3xu
We demonstrate a process for rapid identification of significant structural expansion using satellite imagery and focusing on Concentrated Animal Feeding Operations (CAFOs) as a test case. ...
A major advantage of this approach is that it is able to work with high-cadence (daily to weekly), but lower resolution (3m/pixel), satellite imagery. ...
This prediction image is then compared with the true test image for anomaly detection. ...
arXiv:2105.14159v1
fatcat:vbv2oucc4bfsxj6a52hwhwbnly
Remote Sensing in Agriculture—Accomplishments, Limitations, and Opportunities
2020
Remote Sensing
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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. ...
However, due to the low-resolution satellite imagery, RS approaches to weed detection were not preferred until recent years, when high-resolution imagery became more accessible through UAS technology. ...
doi:10.3390/rs12223783
fatcat:2pexmjjynvcvjp6o3iofpwxgj4
Computer Vision, IoT and Data Fusion for Crop Disease Detection Using Machine Learning: A Survey and Ongoing Research
2021
Remote Sensing
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A growing body of literature recognizes the importance of using data from different types of sensors and machine learning approaches to build models for detection, prediction, analysis, assessment, etc ...
This paper reviews state-of-the-art machine learning methods that use different data sources, applied to plant disease detection. ...
Yield prediction
[63]
Rice
Multi-sensors
Yield and
meteorology data
BBI
Yield classification
[138]
Soyben
Satellite/UAV
Satellite/UAV
ELR
Vegetation feature
prediction
[141]
Wheat ...
doi:10.3390/rs13132486
fatcat:f6u2vvmgvjggrhoqsph6odas3i
Using a Diagnostic Soil-Plant-Atmosphere Model for Monitoring Drought at Field to Continental Scales
2013
Procedia Environmental Sciences
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Integrating LST information from geostationary and polar orbiting systems through data fusion, the ESI has unique potential for sensing moisture stress at field scale, with potential benefits to yield ...
satellites. ...
Data fusion Thermal satellite systems are typically characterized by either high spatial resolution (i.e., <100 m) and low temporal resolution (> 16 day revisit e.g., Landsat) or low spatial resolution ...
doi:10.1016/j.proenv.2013.06.006
fatcat:r3jxcpuu45fqrb3uqirporuij4
Remote Sensing Applications in Tobacco Yield Estimation and the Recommended Research in Zimbabwe
2013
ISRN Agronomy
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Varietal yield response to fertiliser and planting dates as well as suitable temporal windows for spectral data collection should be identified. ...
The challenges of the different tobacco land sizes have to be overcome by identifying suitable satellite platform, with sufficient spectral resolution to separate the tobacco crop from the adjacent competing ...
Acknowledgment The authors are grateful to the Tobacco Research Board/Kutsaga Research Station for funding this series of experiments on "Developing flue cured tobacco crop area and yield forecasting models ...
doi:10.1155/2013/941873
fatcat:drestx2revfkpganawv2g2u3fy
Early warning and drought risk assessment for the Bolivian Altiplano agriculture using high resolution satellite imagery data
2018
NHESSD
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Due to data-scarcity in many regions, high resolution satellite imagery data are becoming widely used. ...
Focusing on ENSO warm and cold phases, we employ a risk-based approach for drought assessment in the Bolivian Altiplano using satellite imagery data and application of an early warning system. ...
for Boliva but with different satellite imagery products. ...
doi:10.5194/nhess-2018-133
fatcat:2ryt4l64cfao3p2smgniidav7a
Drought risk in the Bolivian Altiplano associated with El Niño Southern Oscillation using satellite imagery data
2019
NHESSD
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The results show that droughts can be better predicted using a combination of satellite imagery and ground-based available data. ...
Due to these limitations, similar to many other regions in the world, we tested the performance of satellite imagery data for providing precipitation and temperature data. ...
The authors would also like to thank Ramiro Pillco Zola and Angel Aliaga for the coordination of the research project with the Universidad Mayor de San Andres of Bolivia. ...
doi:10.5194/nhess-2018-403
fatcat:viw6ihzvjvahtcq3phrawujkga
The Role of Earth Observation in Achieving Sustainable Agricultural Production in Arid and Semi-Arid Regions of the World
2021
Remote Sensing
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We demonstrate that agricultural activities can be monitored effectively and both crop area and crop yield can be predicted in advance using RS data. ...
Thirdly, it illustrates the challenges of employing freely available RS data for mapping and monitoring crop area, crop status and forecasting crop yield in these regions. ...
This was achieved by using several specific search terms within Scopus; for example, "crop yield prediction" or "yield estimation" or "yield prediction" and "remote sensing" or "Earth observation" and ...
doi:10.3390/rs13173382
fatcat:gva463nkdjbark4odzswvqg7l4
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