National-scale soybean mapping and area estimation in the United States using medium resolution satellite imagery and field survey

Song, Xiao-Peng; Potapov, Peter; Krylov, Alexander; King, LeeAnn; Bella, Di; Hudson, A.; Khan, Anwar; Adusei, Bernard; Stehman, Stephen V.; Hansen, Matthew C.

doi:10.1016/j.rse.2017.01.008

Cited by 198 publications

(102 citation statements)

References 57 publications

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“…This percentile-based compositing method can capture phenology information without any explicit assumption and prior knowledge of the timing of phenology [46]. Land cover classification with good accuracy has been achieved using the percentile based composite method [45][46][47]. In this study, we included percentile values at 0%, 10%, 25%, 50%, 75%, 90% and 100% by following Hansen's method [45].…”

Section: Reference Samples Collectionmentioning

confidence: 99%

Green Spaces as an Indicator of Urban Health: Evaluating Its Changes in 28 Mega-Cities

Huang

Yang

et al. 2017

Remote Sensing

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Urban green spaces can yield considerable health benefits to urban residents. Assessing these health benefits is a key step for managing urban green spaces for human health and wellbeing in cities. In this study, we assessed the change of health benefits generated by urban green spaces in 28 megacities worldwide between 2005 and 2015 by using availability and accessibility as proxy indicators. We first mapped land covers of 28 megacities using 10,823 scenes of Landsat images and a random forest classifier running on Google Earth Engine. We then calculated the availability and accessibility of urban green spaces using the land cover maps and gridded population data. The results showed that the mean availability of urban green spaces in these megacities increased from 27.63% in 2005 to 31.74% in 2015. The mean accessibility of urban green spaces increased from 65.76% in 2005 to 72.86% in 2015. The increased availability and accessibility of urban green spaces in megacities have brought more health benefits to their residents.

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Section: Reference Samples Collectionmentioning

confidence: 99%

Green Spaces as an Indicator of Urban Health: Evaluating Its Changes in 28 Mega-Cities

Huang

Yang

et al. 2017

Remote Sensing

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“…Information on crop frequency derived from historical maps can be effectively used for stratification purposes in crop area estimation (Boryan, Yang, Di, & Hunt, 2014;Gallego et al, 2012). Knowing geographical distribution of given crops can help optimize available resources, when performing large scale ground observations (Song et al, 2017). For instance, early season crop masks are required to provide crop yield prediction and, consequently, crop production forecasting in the operational context which is important for food security (Becker-Reshef, Vermote, Lindeman, & Justice, 2010;Franch et al, 2015;Johnson, 2016;Kogan et al, 2013;López-Lozano et al, 2015;Shao, Campbell, Taff, & Zheng, 2015).…”

Section: Introductionmentioning

confidence: 99%

Crop inventory at regional scale in Ukraine: developing in season and end of season crop maps with multi-temporal optical and SAR satellite imagery

Kussul

Lavreniuk

Shelestov

et al. 2018

European Journal of Remote Sensing

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“…However, high accuracy and early identifications of crop distribution across an entire growing period is challenging [7,8]. Since traditional agricultural statistics on crop acreages are usually provided by the end of the season or later, in-season agricultural production managers lack necessary information about the current year's crops [9,10]. Alternatively, remote sensing satellites, owing to their synoptic and repetitive nature, have proven to be an effective means for mapping and monitoring crop extent [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

In-Season Crop Mapping with GF-1/WFV Data by Combining Object-Based Image Analysis and Random Forest

Song

Zhou

et al. 2017

Remote Sensing

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Producing accurate crop maps during the current growing season is essential for effective agricultural monitoring. Substantial efforts have been made to study regional crop distribution from year to year, but less attention is paid to the dynamics of composition and spatial extent of crops within a season. Understanding how crops are distributed at the early developing stages allows for the timely adjustment of crop planting structure as well as agricultural decision making and management. To address this knowledge gap, this study presents an approach integrating object-based image analysis with random forest (RF) for mapping in-season crop types based on multi-temporal GaoFen satellite data with a spatial resolution of 16 meters. A multiresolution local variance strategy was used to create crop objects, and then object-based spectral/textural features and vegetation indices were extracted from those objects. The RF classifier was employed to identify different crop types at four crop growth seasons by integrating available features. The crop classification performance of different seasons was assessed by calculating F-score values. Results show that crop maps derived using seasonal features achieved an overall accuracy of more than 87%. Compared to the use of spectral features, a feature combination of in-season textures and multi-temporal spectral and vegetation indices performs best when classifying crop types. Spectral and temporal information is more important than texture features for crop mapping. However, texture can be essential information when there is insufficient spectral and temporal information (e.g., crop identification in the early spring). These results indicate that an object-based image analysis combined with random forest has considerable potential for in-season crop mapping using high spatial resolution imagery.

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National-scale soybean mapping and area estimation in the United States using medium resolution satellite imagery and field survey

Cited by 198 publications

References 57 publications

Green Spaces as an Indicator of Urban Health: Evaluating Its Changes in 28 Mega-Cities

Green Spaces as an Indicator of Urban Health: Evaluating Its Changes in 28 Mega-Cities

Crop inventory at regional scale in Ukraine: developing in season and end of season crop maps with multi-temporal optical and SAR satellite imagery

In-Season Crop Mapping with GF-1/WFV Data by Combining Object-Based Image Analysis and Random Forest

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