Journal ArticleDOI
A new method for crop classification combining time series of radar images and crop phenology information.
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TLDR
In this paper, a new multitemporal data based classification approach was developed that incorporates knowledge about the phenological changes on crop lands and identifies phenological sequence patterns (PSP) of the crop types based on a dense stack of Sentinel-1 data.About:
This article is published in Remote Sensing of Environment.The article was published on 2017-09-01. It has received 189 citations till now. The article focuses on the topics: Land cover & Vegetation.read more
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Journal ArticleDOI
Mapping cropping intensity in China using time series Landsat and Sentinel-2 images and Google Earth Engine
TL;DR: Wang et al. as mentioned in this paper developed a straightforward and efficient pixel-and phenology-based algorithm to generate annual cropping intensity maps over large spatial domains at high spatial resolution by integrating Landsat-8 and Sentinel-2 time series image data for 2016-2018 using the Google Earth Engine (GEE) platform.
Journal ArticleDOI
Dual polarimetric radar vegetation index for crop growth monitoring using sentinel-1 SAR data
Dipankar Mandal,Vineet Kumar,Vineet Kumar,Debanshu Ratha,Subhadip Dey,Avik Bhattacharya,Juan M. Lopez-Sanchez,Heather McNairn,Y. S. Rao +8 more
TL;DR: In this paper, a new vegetation index was derived from dual-pol (DpRVI) SAR data for canola, soybean, and wheat, over a test site in Canada.
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Evaluating Combinations of Temporally Aggregated Sentinel-1, Sentinel-2 and Landsat 8 for Land Cover Mapping with Google Earth Engine
TL;DR: This study demonstrates that temporal aggregation is a promising tool for integrating large amounts of data in an efficient way and that it can compensate for the lower quality of automatic image selection and cloud masking and shows that combining data from different sensors can improve classification accuracy.
Journal ArticleDOI
Examining earliest identifiable timing of crops using all available Sentinel 1/2 imagery and Google Earth Engine
Nanshan You,Jinwei Dong +1 more
TL;DR: Wang et al. as discussed by the authors examined earliest identifiable timing (EIT) of major crops (rice, soybean, and corn) and generated early season crop maps independent of within-year field surveys in the Heilongjiang province, one most important province of grain production in China.
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Mapping sugarcane plantation dynamics in Guangxi, China, by time series Sentinel-1, Sentinel-2 and Landsat images
TL;DR: Wang et al. as mentioned in this paper developed a pixel-and phenology-based mapping tool to produce an annual map of sugarcane at 10m spatial resolution by analyzing time-series Landsat-7/8, Sentinel-2 and Sentinel-1 images (LC/S2/S1).
References
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Journal ArticleDOI
Random Forests
TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
Classification and Regression by randomForest
Andy Liaw,Matthew C. Wiener +1 more
TL;DR: random forests are proposed, which add an additional layer of randomness to bagging and are robust against overfitting, and the randomForest package provides an R interface to the Fortran programs by Breiman and Cutler.
Journal ArticleDOI
Global Consequences of Land Use
Jonathan A. Foley,Ruth DeFries,Gregory P. Asner,Carol C. Barford,Gordon B. Bonan,Stephen R. Carpenter,F. Stuart Chapin,Michael T. Coe,Michael T. Coe,Gretchen C. Daily,Holly K. Gibbs,Joseph H. Helkowski,Tracey Holloway,Erica A. Howard,Christopher J. Kucharik,Chad Monfreda,Jonathan A. Patz,I. Colin Prentice,Navin Ramankutty,Peter K. Snyder +19 more
TL;DR: Global croplands, pastures, plantations, and urban areas have expanded in recent decades, accompanied by large increases in energy, water, and fertilizer consumption, along with considerable losses of biodiversity.
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Agricultural Intensification and Ecosystem Properties
TL;DR: The use of ecologically based management strategies can increase the sustainability of agricultural production while reducing off-site consequences and have serious local, regional, and global environmental consequences.