Scientia Agricultura Sinica ›› 2022, Vol. 55 ›› Issue (16): 3093-3109.doi: 10.3864/j.issn.0578-1752.2022.16.003

• TILLAGE & CULTIVATION·PHYSIOLOGY & BIOCHEMISTRY·AGRICULTURE INFORMATION TECHNOLOGY • Previous Articles     Next Articles

Consistency Analysis of Classification Results for Single and Double Cropping Rice in Southern China Based on Sentinel-1/2 Imagery

YANG JingYa1(),HU Qiong2,WEI HaoDong1,CAI ZhiWen1,ZHANG XinYu1,SONG Qian3(),XU BaoDong1()   

  1. 1College of Resources and Environment/Macro Agriculture Research Institute, Huazhong Agricultural University, Wuhan 430070
    2College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079
    3Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences/Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture and Rural Affairs, Beijing 100081
  • Received:2021-10-28 Accepted:2022-01-28 Online:2022-08-16 Published:2022-08-11
  • Contact: Qian SONG,BaoDong XU E-mail:jingya.yang@webmail.hzau.edu.cn;songqian01@caas.cn;xubaodong @mail.hzau.edu.cn

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Abstract:

【Objective】Due to the abilities of all-time and all-weather data acquisition, the microwave remote sensing holds great potentials to identify rice in regions dominated by cloudy and rainy weather. The aim of this study was to analyze the consistency of classification results for single and double cropping rice by using optical and SAR remote sensing data, and then to explore the optimal SAR imagery features for rice classification. 【Method】In this study, using the object-based random forest classifier on the Google Earth Engine platform, Sentinel-1 and Sentinel-2 images were adopted to extract the single and double cropping rice from four typical rice growing areas in the Dongting Lake Plain. To analyze the optimal SAR features for the single and double cropping rice identification and the consistency of classification results based on Sentinel-1 and Sentinel-2 images, nine scenarios were established by the combination of different sensors and features and compared the performances of different scenarios. Furthermore, the R2 and DTW distance between the NDVI time series and the SAR backscatter coefficient time series (VH, VH/VV) were calculated, respectively. 【Result】 The overall accuracy of single and double rice cropping identification by using VH, VV and VH/VV time series was 90.42%, 82.08% and 88.33%, respectively. Moreover, the combination of VH and VH/VV time series could achieve a better performance (91.67%) for mapping single and double cropping rice. The derived R2 and DTW distance between VH (VH/VV, VV) time series and NDVI time series were 0.870 (0.915, 0.986) and 4.715 (1.896, 5.506) for single cropping rice, as well as 0.597 (0.783, 0.673) and 2.396 (1.839, 3.441) for double cropping rice, respectively. Higher R2 and lower DTW distance suggested that VH/VV time series, like NDVI, was more sensitive to the rice growth cycle. Furthermore, the flooding signals in rice transplanting phase could be well captured by VH time series. Additionally, the overall accuracy of single and double cropping rice classification based on optical and SAR features (S-2: NDVI, EVI, LSWI; S-1: VH, VH/VV) in six time windows was 91.25% and 90.00%, respectively, and their consistency was high, with the area correlation of 95.70%.【Conclusion】There was high consistency of classification results for single and double cropping rice based on optical and SAR imagery. Thus, Sentinel-1 imagery held great potentials to identify rice area in cloudy and rainy regions. Specifically, VH and VH/VV backscatter coefficient were optimal features for mapping rice. This study provided vital technical supports for feature optimization by using SAR imagery in cloudy and rainy regions to identify single and double cropping rice accurately.

Key words: single and double cropping rice identification, feature time series, Sentinel-1/2, consistency analysis, Google Earth Engine

Fig. 1

Overview of the study area and crop field samples (a) The geographical location of Dongting Lake plain in Hunan province is shown in yellow. (b) The land cover type map extracted by GlobeLand30 dataset in the study area. A, B, C and D indicate four study areas. (c, d, e, f) Sentinel-2 imagery and crop field samples for four typical regions representing single cropping rice, double cropping rice and non-rice planting areas"

Fig. 2

The growth period of single and double cropping rice in Hunan province E, M, and L in each month indicate the early, middle, and late ten-day periods of the month, respectively"

Fig. 3

Pixel-level counts of Sentinel-1 GRD images and good-quality Sentinel-2 images in 2019"

Fig. 4

The workflow of single and double cropping rice classification using Sentinel-1 and Sentinel-2 imagery Each grid in the ‘time window’ step represents a 20-day time window from April 11, 2019 to November 17, 2019, and the green grid represents the time window where the features used in the classification"

Table 1

The established scenarios with different combinations of SAR features for rice mapping"

场景 Scenarios 特征组合 Combinations of features 时间窗口 Time window
场景1 Scenario 1 S-1 VH 04-11-11-17 20 d中值合成时间序列
Time series of 20-d median composition from 04-11 to 11-17
场景2 Scenario 2 S-1 VV
场景3 Scenario 3 S-1 VH/VV
场景4 Scenario 4 S-1 VH, VV
场景5 Scenario 5 S-1 VH, VH/VV
场景6 Scenario 6 S-1 VV, VH/VV
场景7 Scenario 7 S-1 VV, VH, VH/VV

Table 2

The established scenarios with different combinations of sensors and features for rice mapping"

场景 Scenarios 特征组合 Combinations of features 时间窗口 Time window
场景8 Scenario 8 S-2 NDVI, EVI, LSWI 03-22-05-21、05-21-06-10、07-20-08-09、08-09-08-29、09-18-10-08、10-28-11-17时间窗口中值合成 Median composition in each time window
场景9 Scenario 9 S-1 VH, VH/VV 04-11-05-01、05-21-06-10、07-20-08-09、08-09-08-29、09-18-10-08、10-28-11-17时间窗口中值合成 median composition in each time window

Fig. 5

Spectral-temporal feature curves profiles over different crop types Dotted line stands for the mean value of vegetation indices over training samples, range filled color represents error bars with one positive/negative standard deviation"

Table 3

R2 and DTW distance between VH time series and NDVI time series"

水稻类别
Rice types		 VH&NDVI VV&NDVI VH/VV&NDVI
R² DTW R² DTW R² DTW
单季稻 Single rice 0.870 4.715 0.986 5.506 0.915 1.896
双季稻 Double rice 0.597 2.396 0.673 3.441 0.783 1.839

Fig. 6

The image segmentation result based on the SNIC algorithm over an area of about 1 200 m×1 200 m S-2 image (Red: Band 8, Green: Band 4, Blue: Band 3) is composited in the time window from July 25th, 2019 to July 30th, 2019, while the yellow polygons are the segmented objects"

Table 4

Classification accuracies of seven scenarios with different combinations of SAR features"

场景
Scenarios		 单季稻 Single rice 双季稻 Double rice 其他作物 Other crops 总体精度
OA
PA UA F1 score PA UA F1 score PA UA F1 score
场景1 Scenario 1 93.27% 88.99% 0.911 93.59% 92.41% 0.930 81.03% 90.38% 0.855 90.42%
场景2 Scenario 2 85.58% 78.76% 0.820 84.62% 83.54% 0.841 72.41% 87.5% 0.792 82.08%
场景3 Scenario 3 90.38% 84.68% 0.874 97.44% 96.20% 0.968 72.41% 84% 0.778 88.33%
场景4 Scenario 4 93.27% 89.81% 0.915 94.87% 92.50% 0.937 81.03% 90.38% 0.855 90.83%
场景5 Scenario 5 94.23% 89.90% 0.920 98.71% 95.06% 0.969 77.59% 90% 0.833 91.67%
场景6 Scenario 6 94.23% 89.90% 0.920 98.71% 95.06% 0.969 77.59% 90% 0.833 91.67%
场景7 Scenario 7 93.27% 90.65% 0.919 98.72% 95.06% 0.969 79.31% 88.46% 0.836 91.67%

Table 5

Classification accuracies of two scenarios with different combinations of sensors and features"

场景
Scenarios		 单季稻 Single rice 双季稻 Double rice 其他作物 Other crops 总体精度
OA
PA UA F1 score PA UA F1 score PA UA F1 score
场景8 Scenario 8 90.39% 90.39% 0.904 100% 95.12% 0.975 81.03% 87.03% 0.839 91.25%
场景9 Scenario 9 91.35% 89.62% 0.905 96.15% 91.46% 0.937 79.31% 88.46% 0.836 90.00%

Fig. 7

Classification results of different scenarios in four study areas Region A、B、C and D correspond to the study areas c, e, d and f, respectively"

Fig. 8

Spatial consistency of classification results based on optical and SAR remote sensing data Different colors in the legend indicate the difference between the classification results in scenario 8 and scenario 9. For example, the green pixel indicates that it is classified as non-rice in scenario 8, and classified as single cropping rice in scenario 9"

Table 6

Area percentages (S,%) and area deviation coefficients (D) of single and double cropping rice classification results based on optical and SAR remote sensing data"

类别
Types		 S-2 S-1 VH&VH/VV
A B C D All A B C D All
S D S D S D S D S D S D S D S D S D S D
单季稻
Single rice		 24.78 -11.35 47.21 -5.55 18.76 -15.52 21.43 -8.56 28.74 -9.03 31.14 11.35 52.76 5.55 25.65 15.52 25.44 8.56 34.44 9.03
双季稻
Double rice		 2.47 -22.35 5.02 -5.76 24.83 2.76 45.52 2.14 18.49 0.60 3.89 22.35 5.63 5.76 23.49 -2.76 43.61 -2.14 18.27 -0.60
其他作物
Other crops		 72.75 5.64 47.77 6.89 56.41 5.18 33.05 3.29 52.77 5.48 64.97 -5.64 41.61 -6.89 50.86 -5.18 30.95 -3.29 47.29 -5.48

Table 7

Area correlation (R) of classification results based on optical and SAR remote sensing data"

指标 Indicator 区域A Region A 区域B Region B 区域C Region C 区域D Region D All
R 98.95% 97.14% 97.55% 97.94% 95.70%
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