|
|
|
|
|
|
| Extraction of Planting Structure of Winter Wheat Using GBDT and Google Earth Engine |
| ZHANG Hai-yang, ZHANG Yao*, TIAN Ze-zhong, WU Jiang-mei, LI Min-zan, LIU Kai-di |
Key Laboratory of Smart Agriculture System Integration, Ministry of Education, China Agricultural University, Beijing 100083, China
|
|
|
|
|
Abstract In view of the fragmented planting landscape and complex planting structure of Chinese farmland, achieving high accuracy identification of target crops is of great importance for subsequent crop yield estimation, grain policy adjustment and national food security guarantee. Based on Google Earth Engine (GEE) remote sensing data processing cloud platform, this study proposes a planting structure extraction method applicable to different fertility stages of winter wheat. The method adopts multi-temporal Sentinel-2 images covering key fertility stages of winter wheat in 2021 as the data source, and integrating multi-dimensional feature variables, including spectral band features, index features, texture features and topographic features. In this study, the GBDT (gradient boosting decision tree) classifier was employed to extract the planting area and spatial distribution information of winter wheat at different fertility stages at the field scale. The best fertility period for winter wheat identification was discussed. In addition, the crop recognition performance of different common classification models at the field scale was compared and analyzed. The experiments were conducted in Chengu Town, Henan Province, China, and the experimental results showed that the accuracy of planting area recognition was relatively high in the standing and jointing stage (3.11—4.10) of winter wheat, with an overall classification accuracy of 94.61% and a Kappa coefficient of 92.68%. The highest recognition accuracy was achieved in the heading and flowering stage (4.11—5.10), with an overall classification accuracy of 97.01% and a Kappa coefficient was 95.52%; however, the classification accuracy was low in grain-filling and milky stage (5.11—6.10), with an overall classification accuracy of 86.23% and a Kappa coefficient of 81.33%. The results showed that the GBDT classifier could effectively extract land cover information under field-scale conditions and achieve better feature classification recognition during winter wheat’s heading and flowering stage. In addition, this study compared GBDT with traditional classifiers such as Random Forest (RF), CART (classification and regression tree) and Naive Bayesian (NB). The results show that the GBDT classifier has the best performance in feature recognition, with an overall classification accuracy of 1.20 and 5.99 percentage points higher than the RF and CART classifiers, respectively, and a Kappa coefficient of 1.61 and 8.04 percentage points higher than the RF and CART classifiers, respectively. Moreover, the NB classifier has the worst recognition precision, with an overall classification accuracy and a Kappa coefficient of 84.43% and 78.69%, respectively. The results of this study can provide effective technical support for fine-grained crop extraction at the field scale.
|
|
Received: 2021-11-19
Accepted: 2022-03-30
|
|
|
|
Corresponding Authors:
ZHANG Yao
E-mail: zhangyao@cau.edu.com
|
|
[1] Fritz Steffen, See Linda, Bayas Juan Carlos Laso, et al. Agricultural Systems, 2019, 168: 258.
[2] Wu Wenbin, Yu Qiangyi, Peter Verburg H, et al. Journal of Integrative Agriculture, 2014, 13(7): 1432.
[3] Veloso Amanda, Mermoz Stephane, Bouvet Alexandre, et al. Remote Sensing of Environment, 2017, 199: 415.
[4] Gorelick Noel, Hancher Matt, Dixon Mike, et al. Remote Sensing of Environment, 2017, 202: 18.
[5] Dong Jinwei, Xiao Xiangming, Menarguez Michael A, et al. Remote Sensing of Environment, 2016, 185: 142.
[6] Jin Zhenong, Azzari George, You Calum, et al. Remote Sensing of Environment, 2019, 228: 115.
[7] Luo Chong, Liu Huanjun, Lu Luping, et al. Journal of Integrative Agriculture, 2021, 20(7): 1944.
[8] Shetty Shobitha, Gupta Prasun Kumar, Belgiu Mariana, et al. Remote Sensing, 2021, 13(8): 1433.
[9] Khosravi Iman, Alavipanah Seyed Kazem. International Journal of Remote Sensing, 2019, 40(18): 7221.
[10] Adepoju Kayode A, Adelabu Samuel A. Remote Sensing Letters, 2020, 11(2): 107.
[11] Zeng Linglin, Wardlow Brian D, Xiang Daxiang, et al. Remote Sensing of Environment, 2020, 237: 111511.
[12] Zhang Huanxue, Li Qiangzi, Liu Jiangui, et al. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10(12): 5334.
[13] Gong Peng, Chen Bin, Li Xuecao, et al. Science Bulletin, 2020, 65(3): 182.
[14] Belgiu Mariana, Csillik Ovidiu. Remote Sensing of Environment, 2018, 204: 509.
[15] Van Tricht Kristof, Gobin Anne, Gilliams Sven, et al. Remote Sensing, 2018, 10(10): 1642.
[16] Shelestov Andrii, Lavreniuk Mykola, Kussul Nataliia, et al. Frontiers in Earth Science, 2017, 5: 1.
|
| [1] |
YAN Xing-guang, LI Jing*, YAN Xiao-xiao, MA Tian-yue, SU Yi-ting, SHAO Jia-hao, ZHANG Rui. A Rapid Method for Stripe Chromatic Aberration Correction in
Landsat Images[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3483-3491. |
| [2] |
ZHU Zhi-cheng1, WU Yong-feng2*, MA Jun-cheng2, JI Lin2, LIU Bin-hui3*, JIN Hai-liang1*. Response of Winter Wheat Canopy Spectra to Chlorophyll Changes Under Water Stress Based on Unmanned Aerial Vehicle Remote Sensing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3524-3534. |
| [3] |
FENG Hai-kuan1, 2, FAN Yi-guang1, TAO Hui-lin1, YANG Fu-qin3, YANG Gui-jun1, ZHAO Chun-jiang1, 2*. Monitoring of Nitrogen Content in Winter Wheat Based on UAV
Hyperspectral Imagery[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3239-3246. |
| [4] |
ZHU Yu-chen1, 2, WANG Yan-cang3, 4, 5, LI Xiao-fang6, LIU Xing-yu3, GU Xiao-he4*, ZHAO Qi-chao3, 4, 5. Study on Quantitative Inversion of Leaf Water Content of Winter Wheat Based on Discrete Wavelet Technique[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2902-2909. |
| [5] |
LI Yun-xia1, MA Jun-cheng2, LIU Hong-jie3, ZHANG Ling-xian1*. Tillering Number Estimation of Winter Wheat Based on Visible
Spectrogram and Lightweight Convolutional Neural Network[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(01): 273-279. |
| [6] |
FENG Hai-kuan1, 2, TAO Hui-lin1, ZHAO Yu1, YANG Fu-qin3, FAN Yi-guang1, YANG Gui-jun1*. Estimation of Chlorophyll Content in Winter Wheat Based on UAV Hyperspectral[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(11): 3575-3580. |
| [7] |
YANG Xin1, 2, YUAN Zi-ran1, 2, YE Yin1, 2*, WANG Dao-zhong1, 2, HUA Ke-ke1, 2, GUO Zhi-bin1, 2. Winter Wheat Total Nitrogen Content Estimation Based on UAV
Hyperspectral Remote Sensing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(10): 3269-3274. |
| [8] |
ZHAO Ai-ping1, MA Jun-cheng1, WU Yong-feng1*, HU Xin2, REN De-chao2, LI Chong-rui1. Predicting Yield Reduction Rates of Frost-Damaged Winter Wheat After Jointing Using Sentinel-2 Broad-Waveband Spectral Indices[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(07): 2225-2232. |
| [9] |
KONG Yu-ru1, 2, WANG Li-juan1*, FENG Hai-kuan2, XU Yi1, LIANG Liang1, XU Lu1, YANG Xiao-dong2*, ZHANG Qing-qi1. Leaf Area Index Estimation Based on UAV Hyperspectral Band Selection[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(03): 933-939. |
| [10] |
YU Xin-hua1, ZHAO Wei-qing2*, ZHU Zai-chun2, XU Bao-dong3, ZHAO Zhi-zhan4. Research in Crop Yield Estimation Models on Different Scales Based on Remote Sensing and Crop Growth Model[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(07): 2205-2211. |
| [11] |
ZHANG Sha1, 2, BAI Yun2*, LIU Qi2, TONG De-ming2, XU Zhen-tian2, ZHAO Na2, WANG Zhao-xue2, WANG Xiao-peng2, LI Yong-sha1, 2, ZHANG Jia-hua3, 4. Estimations of Winter Wheat Yields in Shandong Province Based on Remote Sensed Vegetation Indices Data and CASA Model[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(01): 257-264. |
| [12] |
LIN Yi1, LIU Si-yuan1, YAN Lei1, FENG Hai-kuan2, ZHAO Shuai-yang1, ZHAO Hong-ying1*. Improvement of Hyperspectral Estimation of Nitrogen Content in Winter Wheat by Leaf Surface Polarized Reflection Measurement[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(06): 1956-1964. |
| [13] |
QUE Hua-li1, 2, YANG Wen-liang1, XIN Xiu-li1, MA Dong-hao1, ZHANG Xian-feng1, ZHU An-ning1*. Ammonia Volatilization from Farmland Measured by Laser Absorption Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(03): 885-890. |
| [14] |
CHEN Xiu-qing, YANG Qi, HAN Jing-ye, LIN Lin, SHI Liang-sheng*. Estimation of Winter Wheat Leaf Water Content Based on Leaf and Canopy Hyperspectral Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(03): 891-897. |
| [15] |
ZHANG Yue1, TIAN Yuan-sheng1, SUN Wen-yi1, 2*, MU Xing-min1, 2, GAO Peng1, 2, ZHAO Guang-ju1, 2. Effects of Different Fertilization Conditions on Canopy Spectral Characteristics of Winter Wheat Based on Hyperspectral Technique[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(02): 535-542. |
|
|
|
|
