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An Object-Based Paddy Rice Classification Using Multi-Spectral Data and Crop Phenology in Assam, Northeast India
2016
Remote Sensing
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Phenology was extracted from MODIS NDVI time series, and the distribution of rice was mapped from China's Environmental Satellite (HJ-1A/B) data. ...
This study investigates the effect of phenology for rice mapping using an object-based image analysis (OBIA) approach. ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/rs8060479
fatcat:5wdnmmftwnev7fn2ycdet4ywj4
Object-Based Paddy Rice Mapping Using HJ-1A/B Data and Temporal Features Extracted from Time Series MODIS NDVI Data
2016
Sensors
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The coarse resolution vegetation index data is first fused with the fine resolution data to generate the time series fine resolution data. ...
The results indicate that the use of temporal features improved the overall classification accuracy of a single-date multi-spectral image by 18.75% from 65.62% to 84.37%. ...
Many studies utilized fused time series vegetation index datasets for cropland mapping and phenology studies [34, 35] . ...
doi:10.3390/s17010010
pmid:28025525
pmcid:PMC5298583
fatcat:o2j3baeggbhrxouuewsletep4u
Rice Fields Mapping in Fragmented Area Using Multi-Temporal HJ-1A/B CCD Images
2015
Remote Sensing
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In this study, the applicability of the Chinese HJ-1A/B satellites and a two-band enhanced vegetation index (EVI2) was investigated. ...
A stepwise classification strategy utilizing the EVI2 signatures during key phenology stages, i.e., the transplanting and the vegetative to reproductive transition phases, of the SCR was proposed, and ...
Jingfeng Huang supervised the process of field campaign and data analysis. Kangyu Zhang provided partial source codes for image analysis. Xinxing Li was responsible for image data collection. ...
doi:10.3390/rs70403467
fatcat:ioydpg3uwjbq5lchxbol5anydy
ChinaCropPhen1km: a high-resolution crop phenological dataset for three staple crops in China during 2000–2015 based on leaf area index (LAI) products
2020
Earth System Science Data
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the retrieved phenological date being less than 10 d, and represented the spatiotemporal patterns of the observed phenological dynamics at the site scale fairly well. ...
In this study, we produced a 1 km grid crop phenological dataset for three main crops from 2000 to 2015 based on Global Land Surface Satellite (GLASS) leaf area index (LAI) products, called ChinaCropPhen1km ...
We would like to thank the highperformance computing support from the Center for Geodata and Analysis, Faculty of Geographical Science, Beijing Normal University (https://gda.bnu.edu.cn/, last access: ...
doi:10.5194/essd-12-197-2020
fatcat:dxk3avlgsjcgxelv5fppwmqscq
Object-Oriented Classification of Sugarcane Using Time-Series Middle-Resolution Remote Sensing Data Based on AdaBoost
2015
PLoS ONE
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In addition, time-series Chinese HJ-1 CCD images were obtained during the sugarcane growing period. ...
The prediction model was applied to the HJ-1 CCD time-series image objects, and then a map of the sugarcane planting area was produced. ...
Acknowledgments We thank the China Center for Resources Satellite Data and Application for providing the free HJ-1 CCD data and the USGS for providing the free Landsat-8 OLI data. ...
doi:10.1371/journal.pone.0142069
pmid:26528811
pmcid:PMC4631514
fatcat:645fg46325bczl7a2tqtrgxfvu
Evaluating Heavy Metal Stress Levels in Rice Based on Remote Sensing Phenology
2018
Sensors
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In this study, we used an integrated Normalized Difference Vegetation Index (NDVI) time-series image set to extract remote sensing phenology. ...
This finding provides scientific evidence for combining rice phenology and physiological characteristics in time and space, and the method is useful to monitor heavy metal stress in rice. ...
of time series images. ...
doi:10.3390/s18030860
pmid:29538350
pmcid:PMC5877332
fatcat:6l7g2xgrfbcptchvhjkcfxv7cu
Tracking the dynamics of paddy rice planting area in 1986–2010 through time series Landsat images and phenology-based algorithms
2015
Remote Sensing of Environment
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In this study, we developed an automated, Landsat-based paddy rice mapping (Landsat-RICE) system that uses time series Landsat images and a phenology-based algorithm based on the unique spectral characteristics ...
Air temperature was used to define phenology timing and crop calendar, which were then used to select Landsat images in the phenology-based algorithms for paddy rice and masks. ...
The high resolution images were provided by NASA for use in the NASA projects with the terms of the National Geospatial-Intelligence Agency's (NGA) Nextview License Agreement. ...
doi:10.1016/j.rse.2015.01.004
fatcat:73g6nbuw5bh4tdvjjjqwl674r4
Crops Planting Information Retrieval at Farmland Plot Scale Using Multi-Sources Satellite Data
2017
Journal of Advanced Agricultural Technologies
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effective data which served as the source of properties for plot objects is obtained; thirdly, the specific NDVI time-series and phenological parameters for each farmland plot are further derived from ...
This experiment illustrated the effectiveness and usefulness of the proposed method, and was referential to finely planting information extraction for other crops. Index Terms-remote sensing, crop identification ...
Project of Guangxi Province (14125008-1-6). ...
doi:10.18178/joaat.4.2.96-103
fatcat:b6dgdjbcfbgexfk2kknbzyiam4
Automated cropping intensity extraction from isolines of wavelet spectra
2016
Computers and Electronics in Agriculture
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To test its efficiency, the CIIWS method is applied to China's Henan province using 250 m 8 days composite Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) time series ...
The mapping result is also evaluated with 30 m Chinese Environmental Disaster Reduction Satellite (HJ-1)-derived data and an overall accuracy of 86.7% is obtained. ...
This work is supported by the National Natural Science Foundation of China (Grant Nos. 41471362 and 41071267). ...
doi:10.1016/j.compag.2016.04.015
fatcat:eb2dkytmhveubdphidtsg7boou
WHEAT AREA MAPPING AND PHENOLOGY DETECTION USING SYNTHETIC APERTURE RADAR AND MULTI MULTI-SPECTRAL REMOTE SENSING OBSERVATIONS
2019
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Further, we propose a method to estimate the crop phenology parameter viz. sowing date using the early time series of Normalized Difference Vegetation Index (NDVI). ...
However, accuracy drops to 87.19 % and 79.16 % while using NDVI and VV-VH respectively. Further, to estimate the sowing date we have considered the NDVI time-series during Oct. ...
The Sentinel 1&2 remote sensing observations were obtained from ESA Copernicus Open Access Hub and GEE platform. ...
doi:10.5194/isprs-archives-xlii-3-w6-123-2019
fatcat:s6g65r4zrbb3noovww3z6ztmyq
Forecasting Transplanted Rice Yield at the Farm Scale Using Moderate-Resolution Satellite Imagery and the AquaCrop Model: A Case Study of a Rice Seed Production Community in Thailand
2018
ISPRS International Journal of Geo-Information
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The rice yield simulated using the AquaCrop model and assimilated with the results of HJ-1A/B, produced a satisfactory outcome when implemented into the validated rice plots, with RMSE = 0.18 t ha −1 and ...
In addition, the optimal rice constant value for conversion of CC was investigated. HJ-1A/B satellite images were used to calculate the CC value, which was then used to simulate yield. ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/ijgi7020073
fatcat:pmgrhcqjxvgdvcn6wsgvwgjxwq
Early Season Mapping of Sugarcane by Applying Machine Learning Algorithms to Sentinel-1A/2 Time Series Data: A Case Study in Zhanjiang City, China
2019
Remote Sensing
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First, we proposed a framework consisting of two procedures: initial sugarcane mapping using the S1A SAR imagery time series, followed by non-vegetation removal using Sentinel-2 optical imagery. ...
More than 90% of the sugar production in China comes from sugarcane, which is widely grown in South China. Optical image time series have proven to be efficient for sugarcane mapping. ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/rs11070861
fatcat:qxdkbu5xnjdxlgjgp23bijseoq
Rapid Extraction of Regional-Scale Agricultural Disasters by the Standardized Monitoring Model Based on Google Earth Engine
2020
Sustainability
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Three types of disaster monitoring and scope extraction models are proposed: the normalized difference vegetation index (NDVI) median time standardization model (RNDVI_TM(i)), the NDVI median phenology ...
series. ...
MODIS NDVI time series can be used to analyze the spatiotemporal evolution of droughts and ENSO events in order to estimate the associated yield loss [29] [30] [31] . ...
doi:10.3390/su12166497
fatcat:wreorxyz4jcafbrwzgafk5q4qm
Land Cover Classification Based on Fused Data from GF-1 and MODIS NDVI Time Series
2016
Remote Sensing
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We developed four classification scenarios based on different combinations of GF-1 spectral features, the fused NDVI time-series, and the phenological parameters. ...
We extracted seven phenological parameters (including the start, end, and length of the growing season, base value, mid-season date, maximum NDVI, seasonal NDVI amplitude) from a fused NDVI time-series ...
We thank CRESDA for providing the GF-1 dataset, and the National Aeronautic and Space Administration for providing the MODIS dataset. ...
doi:10.3390/rs8090741
fatcat:pfe3ohypqvagjazp55i437zu5q
Changes in rice cropping systems in the Poyang Lake Region, China during 2004–2010
2012
Journal of Geographical Sciences
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The results revealed that: (1) from 2004 to 2010, the decrement of paddy rice field was 46.76 km 2 due to the land use change. (2) The temporal dynamics of NDVI derived from Landsat historical images could ...
temporal windows, the spatial variation of rice cropping systems was very obvious, with an increased multiple cropping index of rice about 20.2% from 2004 to 2010. ...
The time-series MODIS vegetation index data holds great potential for describing the growth dynamics of rice fields over large area with relative simple cropping systems. ...
doi:10.1007/s11442-012-0954-x
fatcat:v4sidkdm3vaczckmzmi6sofdxa
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