Scientia Agricultura Sinica ›› 2023, Vol. 56 ›› Issue (13): 2474-2490.doi: 10.3864/j.issn.0578-1752.2023.13.004

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

Integrating Multi-Source Gaofen Images and Object-Based Methods for Crop Type Identification in South China

WANG JiaYue1(), CAI ZhiWen2, WANG WenJing1, WEI HaoDong3, WANG Cong1(), LI ZeXuan1, LI XiuNi1, HU Qiong1()   

  1. 1 College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079
    2 College of Resources and Environment/Macro Agriculture Research Institute, Huazhong Agricultural University, Wuhan 430070
    3 College of Plant Science and Technology/Macro Agriculture Research Institute, Huazhong Agricultural University, Wuhan 430070
  • Received:2022-12-04 Accepted:2022-02-06 Online:2023-07-01 Published:2023-07-06
  • Contact: WANG JiaYue, WANG Cong, HU Qiong

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Abstract:

【Objective】 Due to frequent cloudy and rainy weather, it is challenging to map crop types over South China with fragmented agricultural landscapes. The Gaofen (GF) series satellites developed by China have high spatial-temporal resolution and good image quality. This study exploited the spatial and temporal advantages of multi-source GF images for fine crop classification in heterogeneous agricultural areas with frequent clouds and rain. 【Method】This study characterized the spatial geometry of agricultural fields based on GF-2 data with high spatial resolution, and cooperated with the encrypted observations from GF-1 and GF-6 time series to fully characterize the spectral seasonal variations of crops. By constructing three-dimensional classification features of spectrum-time-space, the crop classification based on random forest classifier were conducted, and the importance scores of different features were calculated. Furthermore, several classification scenarios were set based on different satellite combinations and different classification units, for further analyzing the performances of integrating different GF datasets on crop type mapping. 【Result】The overall accuracy of synergistic GF-1, GF-2 and GF-6 by object-oriented crop classification was 95.49% with Kappa of 0.94 in Qianjiang city; the overall accuracy in Zaoyang city was 93.78% with Kappa of 0.92. The accuracy of crop classification by integrating GF-2 and GF-6 was higher than that by GF-2 and GF-1. In addition, the object-oriented crop classification based on GF-2 outperformed the pixel-oriented one, in which the overall accuracy improved by 1.4% and 1.32% in Qianjiang and Zaoyang, respectively. Compared with the spectral and spatial features of GF-1 and GF-2, the GF-6 spectral bands had the largest contribution to crop type identification, and the cumulative importance score accounted for 82% (Qianjiang) and 77% (Zaoyang) of all spectral bands. Among them, the four new spectral bands of GF-6, namely, red-edge I band (B5), red-edge Ⅱ band (B6), purple band (B7) and yellow band (B8), accounted for 47% (Qianjiang) and 33% (Zaoyang) of all spectral bands for crop type identification. 【Conclusion】Integrating multi-source GF images by taking advantages of their spectral, spatial and temporal features could not only alleviate the "mixed pixel" problem caused by the fragmented agricultural landscape, but also reduce the uncertainty of crop type identification in cloudy and rainy areas, providing great potential for accurate crop mapping in South China.

Key words: GF data, object-oriented, random forest, remote sensing identification, crop classification

Fig. 1

Location of the study area and spatial distribution of crop samples"

Fig. 2

Crop calendars for major crops in study area and temporal information of GF images"

Table 1

Parameters of GF-1, GF-2 and GF-6 sensors"

参数Parameter GF-2 PMS GF-1 WFV GF-6 WFV
光谱范围
Spectral range (μm)		 全色Panchromatic band 0.45-0.90 — —
蓝Blue（B1） 0.45-0.52 0.45-0.52 0.45-0.52
绿Green（B2） 0.52-0.59 0.52-0.59 0.52-0.59
红Red（B3） 0.63-0.69 0.63-0.69 0.63-0.69
近红外NIR（B4） 0.77-0.89 0.77-0.89 0.77-0.89
红边 I Red-edge I（B5） — — 0.69-0.73
红边Ⅱ Red-edge Ⅱ（B6） — — 0.73-0.77
紫Purple（B7） — — 0.40-0.45
黄Yellow（B8） — — 0.59-0.63
幅宽Width (km) 45 800
空间分辨率Spatial resolution (m) 1（全色PAN）/4（多光谱MSS） 16
重访周期
Revisit cycle (d)		 5 2（GF-1和GF-6组网运行
Joint operation with GF-1 and GF-6 satellites）

Table 2

The classification scenarios based on GF-1, GF-2 and GF-6 images"

方案
Scenarios		 数据源与分类单元
Data source and classification units		 光谱特征
Spectral features
GF-1/GF-6		 空间特征
Spatial features
GF-2		 影像数量
Number of images		 特征总数
Number of features
潜江
Qianjiang		 枣阳
Zaoyang		 潜江
Qianjiang		 枣阳
Zaoyang
1
试验组
Experimental group		 GF-2（面向对象
Object-oriented）
&GF-1+GF-6		 蓝、绿、红、近红外、红边Ⅰ、红边Ⅱ、紫、黄
Blue, Green, Red, NIR, Red-edge I, Red-edge Ⅱ, Purple, Yellow		 范围、方向、最大边长、最小边长
Extent, Orientation, Major length, Minor length		 GF-1 3 8 56 116
GF-6 5 10
GF-2 1 1
2
对照组
Control group		 GF-2（面向对象
Object-oriented）
&GF-1		 蓝、绿、红、近红外
Blue, Green, Red, NIR		 范围、方向、最大边长、最小边长
Extent, Orientation, Major length, Minor length		 GF-1 3 8 16 36
3
对照组
Control group		 GF-2（面向对象
Object-oriented）
&GF-6		 蓝、绿、红、近红外、红边Ⅰ、红边Ⅱ、紫，黄
Blue, Green, Red, NIR, Red-edge I, Red-edge Ⅱ, Purple, Yellow		 范围、方向、最大边长、最小边长
Extent, Orientation, Major length, Minor length		 GF-6 5 10 44 84
GF-2 1 1
4
对照组
Control group		 GF-1+GF-6
（面向像元
Pixel-oriented）		 蓝、绿、红、近红外、红边Ⅰ、红边Ⅱ、紫、黄
Blue, Green, Red, NIR, Red-edge I, Red-edge Ⅱ, Purple, Yellow		 / GF-1 3 8 52 112
GF-6 5 10

Fig. 3

The ROC curves calculated by ESP2 tool"

Fig. 4

Results of farmland segmentation under different segmentation scales (Typical regions)"

Fig. 5

Examples of segmentation results with different shape and compactness parameters"

Fig. 6

Average spectral reflectance curves of major crop types The abscissa is the Day of year (DOY), and the ordinate is the average spectral reflectance calculated from the samples"

Table 3

Crop classification results based on different GF data sources in Qianjiang"

方案1 Scenario 1
GF-2（面向对象Object-oriented）
&GF-1+GF-6		 方案2 Scenario 2
GF-2（面向对象Object-oriented）
&GF-1		 方案3 Scenario 3
GF-2（面向对象Object-oriented）
&GF-6		 方案4 Scenario 4
GF-1+GF-6
（面向像元Pixel-oriented）
PA (%) UA (%) F1-score PA (%) UA (%) F1-score PA (%) UA (%) F1-score PA (%) UA (%)
小麦 Wheat 96.20 98.70 0.97 92.40 92.40 0.92 96.30 97.43 0.95 97.50 98.73
油菜 Rape 98.77 97.56 0.97 96.29 95.12 0.96 97.53 97.53 0.96 98.79 97.61
早稻 Early rice 98.57 94.52. 0.98 92.85 86.66 0.91 97.14 94.44 0.96 98.57 93.24
虾稻Rice-crayfish 98.79 92.13 0.92 90.36 91.46 0.88 97.59 90.00 0.91 97.59 88.04
其他作物
Other crops		 86.00 91.49 0.84 74.00 75.51 0.77 86.00 89.58 0.81 75.47 90.90
总体精度OA (%) 95.49 90.12 94.42 94.09
Kappa 0.94 0.88 0.93 0.93

Table 4

Crop classification results based on different GF data sources in Zaoyang"

方案1 Scenario 1
GF-2（面向对象Object-oriented）
&GF-1+GF-6		 方案2 Scenario 2
GF-2（面向对象Object-oriented）
&GF-1		 方案3 Scenario 3
GF-2（面向对象Object-oriented）
&GF-6		 方案4 Scenario 4
GF-1+GF-6
（面向像元Pixel-oriented）
PA (%) UA (%) F1-score PA (%) UA (%) F1-score PA (%) UA (%) F1-score PA (%) UA (%)
小麦 Wheat 94.85 98.92 0.97 91.75 96.73 0.94 92.78 98.90 0.95 93.00 96.87
早稻 Early rice 92.86 85.53 0.89 88.57 79.48 0.84 91.42 85.33 0.88 90.24 85.05
其他作物
Other crops		 80.36 83.33 0.82 73.21 78.84 0.76 82.14 79.31 0.80 77.04 79.66
总体精度OA (%) 93.78 91.35 92.97 92.46
Kappa 0.92 0.89 0.91 0.90

Fig. 7

Crop classification maps based on random forest in Qianjiang a and a1 are pixel-based classification results, b and b1 are object-based classification results, and c are GF-2 images of local typical areas on April 5. a1 and b1 are the results of random forest classification at pixel and object scales of the typical areas displayed, respectively"

Fig. 8

Crop classification maps based on random forest in Zaoyang a and a1 are pixel-based classification results, b and b1 are object-based classification results, and c is GF-2 images of local typical areas on March 15. a1 and b1 are the results of random forest classification at pixel and object scales of the typical areas displayed, respectively"

Fig. 9

Importance scores of crop identification features based on random forest The gray lattice is the background value, indicating that the corresponding image of DOY is GF-1, with only four spectral bands of blue, green, red and near infrared. The abscissa is the Day of Year (DOY), the ordinate is the spectral band of GF images, and the legend is the random forest evaluation index representing the feature importance score -- mean decrease impurity (MDI)"

Fig. 10

Cumulative importance scores of crop identification features for GF-1/6 based on random forest The abscissa is the Day of Year (DOY), and the ordinate is the random forest evaluation index representing the feature importance score—mean decrease impurity (MDI)"

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