Mapping Paddy Rice in China in 2002, 2005, 2010 and 2014 with MODIS Time Series

Clauss, Kersten; Yan, Haiming; Kuenzer, Claudia

doi:10.3390/rs8050434

Cited by 83 publications

(43 citation statements)

References 62 publications

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“…Image segmentation can lead to mixed segments, when a segment not only contains our target class rice but also other land cover. This effect is similar to the mixed pixel effects regularly discussed in rice mapping efforts based on medium to low spatial resolution time-series (Xiao et al 2006;Clauss, Yan, and Kuenzer 2016) and is a source of spatial error in our classification. This issue can be seen in the example given in figure 6 and is mostly apparent at field boundaries.…”

Section: Discussionsupporting

confidence: 78%

“…We used a phenology based decision tree and the extracted time-series to classify each object as either rice or no rice. Decision trees are commonly used for classification of rice areas from remotely sensed time-series of multispectral (Xiao et al 2006;Wang et al 2015;Shi and Huang 2015;Clauss, Yan, and Kuenzer 2016) and SAR data (Liew et al 1998b;Choudhury and Chakraborty 2006;Torbick et al 2011a;Nguyen et al 2015;Nelson et al 2014) or a combination of both (Torbick et al 2011b(Torbick et al , 2017. We chose this classification method due to its proven performance and replicability and wanted to test its regional transferability and performance using Sentinel-1 time-series.…”

Section: Rice Classification With Sentinel-1 Time-seriesmentioning

confidence: 99%

“…The temporal σ 0 signal of rice fields has very distinct minima when the paddy fields are flooded and maxima at the heading stage of the rice plants. From previous studies we know that these first two decision nodes are valid to differentiate seasonal vegetation, such as crops and deciduous forests, from land cover with a constantly low backscatter, such as water bodies, or high backscatter, such as urban areas (Zhu et al 2012;Shimada et al 2014;Nguyen et al 2015;Clauss, Yan, and Kuenzer 2016). If the annual minimum of the σ 0 time-series was larger than -20 dB or the annual maximum was smaller than -17 dB the object was classified as no rice.…”

Section: Rice Classification With Sentinel-1 Time-seriesmentioning

confidence: 99%

“…For all remaining points we selected valid Landsat-8 observations in 2015 at pixel level, calculated the indexes LSWI (Land Surface Water Index) and EVI (Enhanced Vegetation Index) and plotted the resulting time-series. Those time-series plots were then manually inspected for visible inversions followed by a rapid EVI increase , which indicates rice growth Xiao et al 2002Xiao et al , 2006Clauss, Yan, and Kuenzer 2016). If a point was located within a field and fulfilled this inversion criterion it was labelled as rice.…”

Section: Accuracy Assessmentmentioning

confidence: 99%

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Mapping rice areas with Sentinel-1 time series and superpixel segmentation

Clauss

Ottinger

Kuenzer

2017

International Journal of Remote Sensing

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Rice is the single most important crop for food security in Asia. Knowledge about the distribution of rice fields is also relevant in the context of greenhouse relevant methane emissions, disease transmission and water resource management. Copernicus Sentinel-1 provides the first openly available archive of C-band SAR (Synthetic Aperture Radar) data at high spatial and temporal resolution. We developed one of the first methods that shows the potential of this data for accurate and timely mapping of rice growing areas. We used superpixel segmentation to create spatially averaged backscatter time-series, which are robust to speckle and reduce the amount of data to process. This method has been applied to six study sites in different rice growing regions of the world and achieved an average overall accuracy of 0.83.

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Section: Discussionsupporting

confidence: 78%

Section: Rice Classification With Sentinel-1 Time-seriesmentioning

confidence: 99%

Section: Rice Classification With Sentinel-1 Time-seriesmentioning

confidence: 99%

Section: Accuracy Assessmentmentioning

confidence: 99%

See 2 more Smart Citations

Mapping rice areas with Sentinel-1 time series and superpixel segmentation

Clauss

Ottinger

Kuenzer

2017

International Journal of Remote Sensing

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“…Their study was based on the unique spectral reflectance of the flooded soil-vegetation mix compared to other croplands. Additionally, Clauss et al [13] mapped rice areas in China using MODIS time series products and the support vector machine. The overall accuracy achieved was 0.90 with a kappa coefficient of 0.77.…”

Section: Introductionmentioning

confidence: 99%

Mapping Paddy Rice Using Sentinel-1 SAR Time Series in Camargue, France

et al. 2019

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This study proposes an effective method to map rice crops using the Sentinel-1 SAR (Synthetic Aperture Radar) time series over the Camargue region, Southern France. First, the temporal behavior of the SAR backscattering coefficient over 832 plots containing different crop types was analyzed. Through this analysis, the rice cultivation was identified using metrics derived from the Gaussian profile of the VV/VH time series (3 metrics), the variance of the VV/VH time series (one metric), and the slope of the linear regression of the VH time series (one metric). Using the derived metrics, rice plots were mapped through two different approaches: decision tree and Random Forest (RF). To validate the accuracy of each approach, the classified rice map was compared to the available national data. Similar high overall accuracy was obtained using both approaches. The overall accuracy obtained using a simple decision tree reached 96.3%, whereas an overall accuracy of 96.6% was obtained using the RF classifier. The approach, therefore, provides a simple yet precise and powerful tool to map paddy rice areas.

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Impacts of climate change on hulless barley security in plateau region: A case study of Lhasa River basin in Tibet, China

Zhang

Tao

2022

Food and Energy Security

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Accurate understanding of the impacts of climate change on hulless barley and river in the Lhasa River Basin is of great significance to food security and water resources management in the plateau region. It is important to explore the relationship between hulless barley and river under the background of climate change for the comprehensive and coordinated development of agriculture and water conservancy. Based on the Google Earth Engine (GEE) cloud computing platform, the Random Forest algorithm was used to obtain the spatio‐temporal variation of hulless barley and river in the Lhasa River Basin from 2010 to 2020. The overall accuracy and Kappa coefficient of classification results were 89.54% and 85.96%, respectively. The average area and Normalized Difference Vegetation Index (NDVI) of hulless barley from 2010 to 2020 were 178.46 km2 and 0.69, respectively. The increase of accumulated precipitation, number of precipitation days, average dew point temperature (ADPT) and average wind speed (AWS) promoted the growth of hulless barley, with NDVI significant increasing rate of 0.0173 (R2 = 0.764, p < 0.001). The combined effects of human activities (construction of water conservancy facilities and mining activities), ADPT and AWS resulted in a significant decrease (decreasing rate: 10.8682 km2/year, p < 0.01) in river area during 2010–2020. There was a significant negative correlation (R2 = 0.722, p < 0.01) between hulless barley NDVI and river area driven by climate factors. The changes in hulless barley gravity center and river gravity center were consistent, and both shifted in the northeast direction. These results provide a scientific understanding of the impacts of plateau climate change on agriculture and water resource. The land cover maps of the Lhasa River Basin with long time series and high spatial resolution were drawn. Our study verified the relevance between hulless barley gravity center and river gravity center as well as the internal relationship between hulless barley growth and river area. Furthermore, we explored the interrelation relationships among hulless barley, the Lhasa River area and climate factors.

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Mapping Paddy Rice in China in 2002, 2005, 2010 and 2014 with MODIS Time Series

Cited by 83 publications

References 62 publications

Mapping rice areas with Sentinel-1 time series and superpixel segmentation

Mapping rice areas with Sentinel-1 time series and superpixel segmentation

Mapping Paddy Rice Using Sentinel-1 SAR Time Series in Camargue, France

Impacts of climate change on hulless barley security in plateau region: A case study of Lhasa River basin in Tibet, China

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