High Granularity Remote Sensing and Crop Production over Space and Time: NDVI over the Growing Season and Prediction of Cotton Yields at the Farm Field Level in Texas

Little, Bertis B.; Schucking, M.; Gartrell, B.; Chen, Bing; Ross, K. W.; McKellip, Rodney

doi:10.1109/icdmw.2008.91

Cited by 5 publications

(4 citation statements)

References 43 publications

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“…and the dependent variable is a thematic class such as forest, urban, water, and agriculture. Examples of spatiotemporal regression include yearly crop yield prediction [129], and daily temperature prediction at different locations.…”

Section: What Is Spatiotemporal Prediction?mentioning

confidence: 99%

Spatiotemporal Data Mining: A Computational Perspective

Shekhar

Jiang

Ali

et al. 2015

IJGI

174

113

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Explosive growth in geospatial and temporal data as well as the emergence of new technologies emphasize the need for automated discovery of spatiotemporal knowledge. Spatiotemporal data mining studies the process of discovering interesting and previously unknown, but potentially useful patterns from large spatiotemporal databases. It has broad application domains including ecology and environmental management, public safety, transportation, earth science, epidemiology, and climatology. The complexity of spatiotemporal data and intrinsic relationships limits the usefulness of conventional data science techniques for extracting spatiotemporal patterns. In this survey, we review recent computational techniques and tools in spatiotemporal data mining, focusing on several major pattern families: spatiotemporal outlier, spatiotemporal coupling and tele-coupling, spatiotemporal prediction, spatiotemporal partitioning and summarization, spatiotemporal hotspots, and change detection. Compared with other surveys in the literature, this paper emphasizes the statistical foundations of spatiotemporal data mining and provides comprehensive coverage of computational approaches for various pattern families.ISPRS Int. J. Geo-Inf. 2015, 4 2307We also list popular software tools for spatiotemporal data analysis. The survey concludes with a look at future research needs.

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Section: What Is Spatiotemporal Prediction?mentioning

confidence: 99%

Spatiotemporal Data Mining: A Computational Perspective

Shekhar

Jiang

Ali

et al. 2015

IJGI

174

113

View full text Add to dashboard Cite

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“…The type of output attribute determines the supervised learning task; two such tasks are: Classification: Here, the input vectors x i are assigned to a few discrete numbers of classes, for example, image classification73 y i . Regression: In regression, also known as function approximation or prediction, the input–output pairs are generated from an unknown function of the form y = f ( x ), where y is continuous. Typically, regression is used in regression and estimation, for example, crop yield prediction,74 daily temperature prediction, and market share estimation for a particular product. Regression can also be used in inverse estimation, that is, given that we have an observed value of y , we want to determine the corresponding x value. …”

Section: Spatial Data Mining Tasksmentioning

confidence: 99%

Identifying patterns in spatial information: A survey of methods

Shekhar

Evans

Kang

et al. 2011

WIREs Data Min & Knowl

141

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Explosive growth in geospatial data and the emergence of new spatial technologies emphasize the need for automated discovery of spatial knowledge. Spatial data mining is the process of discovering interesting and previously unknown, but potentially useful patterns from large spatial databases. The complexity of spatial data and implicit spatial relationships limits the usefulness of conventional data mining techniques for extracting spatial patterns. In this paper, we explore the emerging field of spatial data mining, focusing on different methods to extract patterns from spatial information. We conclude with a look at future research needs.

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“…Imaging Spectroradiometer (MODIS) data with the higher temporal resolution are usually used to assess early cotton production information and predict the cotton yields at national and regional levels [6,7]. Landsat Thematic Mapper (Landsat TM), Indian Remote Sensing (IRS), China-Brazil Earth Resource Satellite (CBERS) and Huanjin (HJ-1 A/B) satellite with the higher spatial resolution are often used to estimate cotton yields at regional and field levels [2,3,8,[11][12][13].…”

Section: Introductionmentioning

confidence: 99%

“…Remote sensing imagery can offer a repeated unbiased view of large areas, and has thus been widely used to estimate crop yields. Lots of studies have shown that cotton area and yield can be efficiently estimated by remote sensing [2][3][4][5][6][7][8][9][10][11][12][13]. National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution…”

Section: Introductionmentioning

confidence: 99%

Cotton Area and Yield Estimation at Zhanhua County of China Using HJ-1 EVI Time Series

Liu

et al. 2016

ITM Web Conf.

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Abstract. Cotton is a significant cash crop of China. Timely and accurate cotton area and yield estimation is useful for management decisions related to the cotton procurement and sales. This study is a first research on cotton area and yield estimation based on remote sensing at Zhanhua County which is one of the high-quality cotton production demonstration bases of China. After normalization of Enhanced Vegetation Index (EVI) time series derived from Huanjin 1 A/B satellite (HJ-1 A/B), decision tree classifier was used to identify the cotton, and then K-Means classifier was applied to estimate cotton yield. The results indicated an overall accuracy of 95% for the cotton area estimation and 91% for the cotton yield classification. With further validation, it suggests that this method can be used to timely achieve the cotton area and growth information of this region.

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High Granularity Remote Sensing and Crop Production over Space and Time: NDVI over the Growing Season and Prediction of Cotton Yields at the Farm Field Level in Texas

Cited by 5 publications

References 43 publications

Spatiotemporal Data Mining: A Computational Perspective

Spatiotemporal Data Mining: A Computational Perspective

Identifying patterns in spatial information: A survey of methods

Cotton Area and Yield Estimation at Zhanhua County of China Using HJ-1 EVI Time Series

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