|
|
|
|
|
|
| Intelligent Recognition of Corn Residue Cover Area by Time-Series
Sentinel-2A Images |
| TAO Wan-cheng1, 2, ZHANG Ying1, 2, XIE Zi-xuan1, 2, WANG Xin-sheng1, 2, DONG Yi1, 2, ZHANG Ming-zheng 1, 2, SU Wei1, 2*, LI Jia-yu1, 2, XUAN Fu1, 2 |
1. College of Land Science and Technology, China Agricultural University, Beijing 100083, China
2. Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing 100083, China
|
|
|
|
|
Abstract Crop residue covering is an important way to reduce soil erosion and increase soil organic carbon, which is very important for black soil protection. Therefore, the accurate and rapid identification of corn residue cover area plays an important role in local government monitoring and promoting conservation tillage. The study area is located in Siping City, Jilin Province. Moreover, the time-series Sentinel-2A images collected from GEE (Google Earth Engine) cloud platform are used to capture spectral index based on the characteristics of the corn growing season and after harvest. Index features include Normalized Difference Vegetation Index (NDVI) and Normalized Difference Residue Index (NDRI). The time series feature values are sorted by size, and the quantile method is used to select quartile (QT) features at 0%, 25%, 50%, 75%, and 100% to construct datasets. On this basis, the random forest method after parameter optimization is applied to train and verify the sample datasets divided according to 7∶3, and then the datasets are classified, combined with the connected domain calibration method to remove the small connected domains generated in the classification process, and further optimize the global result. Through the quantitative and qualitative evaluation of Kappa and Overall Accuracy (OA), the experimental results show that: (1) The quantitative evaluation results of the classification model (M1/M2/M3/M4/M5) based on the dataset composed of the different feature are superior 90%. Among them, the classification model M5 of the dataset designed in this paper has the best performance, of which Kappa and OA are 97.41% and 97.91%, respectively. Compared with the classification model M2 without the QT feature, the Kappa and OA are increased by 4.52% and 3.64%, respectively. At the same time, the M5 recognition result can effectively retain edge detail information; (2) For QT feature of different time scales, using the QT feature classification model M5_6/M5 of time series remote sensing images from May to November can greatly restrain another crop residue. Compared with the Kappa and OA of the M5_1 model classification result using only the QT features of the time series images in November, the Kappa and OA increased by 3.9% and 3.12%, respectively; (3) Based on the M5 model, the Kappa and OA of the classification model M6 combined with the connected domain calibration method are 96.76% and 97.36%, respectively, second only to the recognition results of the M5 model. The model M6 avoids fine-grained patches while ensuring high accuracy and optimizing the classification visualization effect. Therefore, the M6 model proposed in this paper is suitable for identifying areas covered by corn residue in the study area. This method can be quickly implemented in the GEE cloud platform environment and is suitable for popularization and application in a corn residue covered areas in Northeast China.
|
|
Received: 2021-06-01
Accepted: 2021-08-01
|
|
|
|
Corresponding Authors:
SU Wei
E-mail: suwei@cau.edu.cn
|
|
[1] Daughtry C S T, Doraiswamy P C, Hunt Jr E R, et al. Soil and Tillage Research, 2006, 91(1-2): 101.
[2] Shen Y, McLaughlin N, Zhang X, et al. Scientific Reports, 2018, 8(1): 1.
[3] Du Z, Ren T, Hu C. Soil Science Society of America Journal, 2010, 74(1): 196.
[4] Watts D B, Runion G B, Balkcom K S. Field Crops Research, 2017, 201: 184.
[5] Sindelar M, Blanco-Canqui H, Jin V L, et al. Soil Science Society of America Journal, 2019, 83(1): 221.
[6] Jin X, Ma J, Wen Z, et al. Remote Sensing, 2015, 7(11): 14559.
[7] Mupangwa W, Thierfelder C, Cheesman S, et al. Renewable Agriculture and Food Systems, 2020, 35(3): 322.
[8] Guan H X, Liu H J, Meng X T, et al. Remote Sensing, 2020, 12(19): 3149.
[9] Rodriguez-Galiano V F, Ghimire B, Rogan J, et al. ISPRS Journal of Photogrammetry and Remote Sensing, 2012, 67: 93.
[10] Hu Y, Hu Y. Remote Sensing, 2019, 11(5): 554.
[11] Venkatappa M, Sasaki N, Shrestha R P, et al. Remote Sensing, 2019, 11(13): 1514.
[12] Gelder B K, Kaleita A L, Cruse R M. Agronomy Journal, 2009, 101(3): 635.
[13] Bao Y, Meng X, Ustin S, et al. Catena, 2020, 195: 104703.
[14] Du Z, Yang J, Ou C, et al. Remote Sensing, 2019, 11(7): 888.
|
| [1] |
YANG Qun1, 2, LING Qi-han1, WEI Yong1, NING Qiang1, 2, KONG Fa-ming1, ZHOU Yi-fan1, 2, ZHANG Hai-lin1, WANG Jie1, 2*. Non-Destructive Monitoring Model of Functional Nitrogen Content in
Citrus Leaves Based on Visible-Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3396-3403. |
| [2] |
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. |
| [3] |
DONG Jian-jiang1, TIAN Ye1, ZHANG Jian-xing2, LUAN Zhen-dong2*, DU Zeng-feng2*. Research on the Classification Method of Benthic Fauna Based on
Hyperspectral Data and Random Forest Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3015-3022. |
| [4] |
HUANG Hua1, LIU Ya2, KUERBANGULI·Dulikun1, ZENG Fan-lin1, MAYIRAN·Maimaiti1, AWAGULI·Maimaiti1, MAIDINUERHAN·Aizezi1, GUO Jun-xian3*. Ensemble Learning Model Incorporating Fractional Differential and
PIMP-RF Algorithm to Predict Soluble Solids Content of Apples
During Maturing Period[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3059-3066. |
| [5] |
LIU Fei1, TAN Jia-jin1*, XIE Gu-ai2, SU Jun3, YE Jian-ren1. Early Diagnosis of Pine Wilt Disease Based on Hyperspectral Data and Needle Resistivity[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3280-3285. |
| [6] |
LI Quan-lun1, CHEN Zheng-guang1*, JIAO Feng2. Prediction of Oil Content in Oil Shale by Near-Infrared Spectroscopy Based on Stacking Ensemble Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(04): 1030-1036. |
| [7] |
LIU Xin-yu1, SHAO Wen-wu2*, ZHOU Shi-rui3. Spectral Pattern Recognition of Cardiac Tissue in Electric Shock Death and Post-Mortem Electric Shock[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(04): 1126-1133. |
| [8] |
YANG Cheng-en1, SU Ling2, FENG Wei-zhi1, ZHOU Jian-yu1, WU Hai-wei1*, YUAN Yue-ming1, WANG Qi2*. Identification of Pleurotus Ostreatus From Different Producing Areas Based on Mid-Infrared Spectroscopy and Machine Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(02): 577-582. |
| [9] |
YAN Wen-hao1, YANG Xiao-ying1, GENG Xin1, WANG Le-shan1, LÜ Liang1, TIAN Ye1*, LI Ying1, LIN Hong2. Rapid Identification of Fish Products Using Handheld Laser Induced Breakdown Spectroscopy Combined With Random Forest[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(12): 3714-3718. |
| [10] |
GONG Sheng1, ZHU Ya-ning2, ZENG Chen-juan3, MA Xiu-ying3, PENG Cheng1, GUO Li1*. Near-Infrared Spectroscopy Combined With Random Forest Algorithm: A Fast and Effective Strategy for Origin Traceability of Fuzi[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(12): 3823-3829. |
| [11] |
GAO Feng1, 2, 3, JIANG Qun-ou1, 2, 3*, XIN Zhi-ming4, XIAO Hui-jie1, 2, LÜ Ke-xin1, QIAO Zhi1. Extraction Method of Oasis Shelterbelt Systems Based on Remote-Sensing Images ——A Case Study of Dengkou County[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(12): 3896-3905. |
| [12] |
GAO Rong-hua1, 2, FENG Lu1, 2*, ZHANG Yue3, YUAN Ji-dong3, WU Hua-rui1, 2, GU Jing-qiu1, 2. Early Detection of Tomato Gray Mold Disease With Multi-Dimensional Random Forest Based on Hyperspectral Image[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(10): 3226-3234. |
| [13] |
LI Zi-zhao, BI Shou-dong, CUI Yu-huan, HAO Shuang*. Comparison of Forest Stock Volume Inversion Methods Coupled With Multiple Features——A Case Study of Forest in Yarlung Zangbo River Basin[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(10): 3263-3268. |
| [14] |
YANG Cheng-en1, WU Hai-wei1*, YANG Yu2, SU Ling2, YUAN Yue-ming1, LIU Hao1, ZHANG Ai-wu3, SONG Zi-yang3. A Model for the Identification of Counterfeited and Adulterated Sika Deer Antler Cap Powder Based on Mid-Infrared Spectroscopy and Support
Vector Machines[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(08): 2359-2365. |
| [15] |
WANG Cai-ling1, WANG Bo2, JI Tong3, XU Jun4, JU Feng5, WANG Hong-wei6*. Simulated Estimation of Nitrite Content in Water Based on
Transmission Spectrum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(07): 2181-2186. |
|
|
|
|
