Mapping cropping intensity in China using time series Landsat and Sentinel-2 images and Google Earth Engine

Luo, Longfu; Xiao, Xiangming; Qin, Yuanwei; Wang, Jie; Xu, Xinliang; Hu, Yueming; Qiao, Zhi

doi:10.1016/j.rse.2019.111624

Cited by 239 publications

(139 citation statements)

References 57 publications

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“…The authors therefore proposed using red-edge bands from Sentinel-2 and the wet season radar backscatter from Sentinel-1 since they have the potential to distinguish crops. Studies [94,95] have reported the applicability of vegetation indices, such as enhanced vegetation index (EVI), NDVI and land surface water index (LSWI), from the high spatial resolution Sentinel-2 time series data in identifying rice cropped areas with high accuracy. Inclusion of phenological metrics, such as the SOS and EOS dates, in the time series curves from a combined Landsat-7 and 8 and Sentinel-2 dataset further increased the mapping accuracy of rice crop from 78% to 93% [95].…”

Section: Mapping Of Crops Using Time Series Datamentioning

confidence: 99%

Status of Phenological Research Using Sentinel-2 Data: A Review

2020

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Remote sensing of plant phenology as an indicator of climate change and for mapping land cover has received significant scientific interest in the past two decades. The advancing of spring events, the lengthening of the growing season, the shifting of tree lines, the decreasing sensitivity to warming and the uniformity of spring across elevations are a few of the important indicators of trends in phenology. The Sentinel-2 satellite sensors launched in June 2015 (A) and March 2017 (B), with their high temporal frequency and spatial resolution for improved land mapping missions, have contributed significantly to knowledge on vegetation over the last three years. However, despite the additional red-edge and short wave infra-red (SWIR) bands available on the Sentinel-2 multispectral instruments, with improved vegetation species detection capabilities, there has been very little research on their efficacy to track vegetation cover and its phenology. For example, out of approximately every four papers that analyse normalised difference vegetation index (NDVI) or enhanced vegetation index (EVI) derived from Sentinel-2 imagery, only one mentions either SWIR or the red-edge bands. Despite the short duration that the Sentinel-2 platforms have been operational, they have proved their potential in a wide range of phenological studies of crops, forests, natural grasslands, and other vegetated areas, and in particular through fusion of the data with those from other sensors, e.g., Sentinel-1, Landsat and MODIS. This review paper discusses the current state of vegetation phenology studies based on the first five years of Sentinel-2, their advantages, limitations, and the scope for future developments.

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Section: Mapping Of Crops Using Time Series Datamentioning

confidence: 99%

Status of Phenological Research Using Sentinel-2 Data: A Review

2020

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“…Previous studies have proved the reliability of TOA reflectance on image classification because the relative spectral differences are the essential aspect 20 . Lots of recent efforts have used S2 TOA images to observe crops, such as the paddy rice mapping 21 , maize area and yield mapping 22 , sugarcane identification 23 , and cropping intensity monitoring 24 . The cloudy observations of the S2 TOA data were removed based on the adjusted cloud score algorithm 25 .…”

Section: Methodsmentioning

confidence: 99%

“…The adjusted cloud score algorithm could detect clouds more accurately than the QA60 quality assessment band 11 . www.nature.com/scientificdata www.nature.com/scientificdata/ We further processed the time series data in the three steps: (1) 10-day composites were generated with the median values of the valid S2 observations; (2) data gaps were filled by the linear interpolation to achieve full coverages throughout the temporal domain 10 , and (3) 10-day time series data were smoothed by using the Savitzky-Golay (SG) filter 24 . In this study, we used the window size of 70 days (7 observations) and the 3rd order polynomial.…”

Section: Methodsmentioning

confidence: 99%

The 10-m crop type maps in Northeast China during 2017–2019

et al. 2021

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Northeast China is the leading grain production region in China where one-fifth of the national grain is produced; however, consistent and reliable crop maps are still unavailable, impeding crop management decisions for regional and national food security. Here, we produced annual 10-m crop maps of the major crops (maize, soybean, and rice) in Northeast China from 2017 to 2019, by using (1) a hierarchical mapping strategy (cropland mapping followed by crop classification), (2) agro-climate zone-specific random forest classifiers, (3) interpolated and smoothed 10-day Sentinel-2 time series data, and (4) optimized features from spectral, temporal, and texture characteristics of the land surface. The resultant maps have high overall accuracies (OA) spanning from 0.81 to 0.86 based on abundant ground truth data. The satellite estimates agreed well with the statistical data for most of the municipalities (R2 ≥ 0.83, p < 0.01). This is the first effort on regional annual crop mapping in China at the 10-m resolution, which permits assessing the performance of the soybean rejuvenation plan and crop rotation practice in China.

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“…An alternative to improve the data availability is to incorporate Landsat 8 images. Efforts have been made to successfully map crops or crop intensity by integrating Sentinel-2 and Landsat images [31,35]. However, due to the different spectral bands and spatial resolution of these sensors, the methods to integrate Sentinel-2 and Landsat images and the applications of the integrated data to mapping rice systems still need further investigations.…”

Section: Advantages Of the Dense Time Stacks Of Sentinel-2 Imagesmentioning

confidence: 99%

Mapping Ratoon Rice Planting Area in Central China Using Sentinel-2 Time Stacks and the Phenology-Based Algorithm

Liu

Chen

et al. 2020

Remote Sensing

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Mapping rice cropping systems is important for grain yield prediction and food security assessments. Both single- and double-season rice are the dominant rice systems in central China. However, because of increasing labor shortages and high costs, there has been a gradual decline in double-season rice. Ratoon rice (RR) has been proposed as an alternative system that balances the productivity, cost, and labor requirements of rice cultivation. RR has been expanding in central China, encouraged by the improved cultivars, machinery, and favorable policies. However, to our knowledge, the distribution of RR has not been mapped with remote sensing techniques. This study developed a phenology-based algorithm to map RR at a 10 m resolution in Hubei Province, Central China, using dense time stacks of Sentinel-2 images (cloud cover <80%) in 2018. The key in differentiating RR from the other rice cropping systems is through the timing of maturity. We proposed to use two contrast vegetation indices to identify RR fields. The newly-developed yellowness index (YI) calculated with the reflectance of blue, green, and red bands was used to detect the ripening phase, and the enhanced vegetation index (EVI) was used to detect the green-up of the second-season crop to eliminate the misclassification caused by stubbles left in the field. The RR map demonstrated that RR was mainly distributed in the low alluvial plains of central and southern Hubei Province. The total planting area of RR in 2018 was 2225.4 km2, accounting for 10.03% of the total area of paddy rice fields. The overall accuracy of RR, non-RR rice fields, and non-rice land cover types was 0.76. The adjusted overall accuracy for RR and non-RR was 0.91, indicating that the proposed YI and the phenology-based algorithm could accurately identify RR fields from the paddy rice fields.

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Mapping cropping intensity in China using time series Landsat and Sentinel-2 images and Google Earth Engine

Cited by 239 publications

References 57 publications

Status of Phenological Research Using Sentinel-2 Data: A Review

Status of Phenological Research Using Sentinel-2 Data: A Review

The 10-m crop type maps in Northeast China during 2017–2019

Mapping Ratoon Rice Planting Area in Central China Using Sentinel-2 Time Stacks and the Phenology-Based Algorithm

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