|
|
|
|
|
|
Maize Yield Forecasting and Associated Optimum Lead Time Research Based on Temporal Remote Sensing Data and Different Model |
LIU Zhao1, 2, LI Hua-peng1, CHEN Hui1, 2, ZHANG Shu-qing1* |
1. Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
2. University of Chinese Academy of Sciences, Beijing 100049, China
|
|
|
Abstract For the inadequate generalization ability of the quantitative evaluation model of crop yield, the lag of forecasting time and the difficulty of establishing the optimum lead yield estimation time, this paper takes Sentinel-2 remote sensing data and the measured maize yield as the data source to research the establishment of county-scale maize yield estimation and optimum lead yield estimation time. Based on the time-series image data of maize growth-satges, through building the correlation between maize yield measured data and vegetation index, the time-series maize yield estimation model was established by MLRM (multivariable linear regression model), GPR (Gaussian process regression model) and LSTM (Long short-term memory artificial neural network model). The experimental results show that LSTM is superior to GPR and MLRM in terms of the accuracy, and reliability of the yield prediction model, the capture of the abnormal yield value, and the optimum lead yield estimation time in the time series yield estimation model established with NDVI, GNDVI and GN ( NDVI and GNDVI combination ) as parameters. At the same time, based on the LSTM estimation model, the NDVI time-series image data up to tasseling stage were used as parameters and the yield prediction results showed that the R2(determination coefficient) was 0.83, RMSE(root mean square error) was 0.26 t·ha-1, RPD(relative percent deviation) was 3.52; The GNDVI time-series image data up to tasseling stage were used as parameters, and the yield prediction results showed that the R2 was 0.79, RMSE was 0.30 t·ha-1, RPD was 2.87; The GN time-series image data up to tasseling stage were used as parameters, and the yield prediction results showed that the R2 was 0.83, RMSE was 0.27 t·ha-1, RPD was 3.05. Using the NDVI time-series image data as the LSTM model parameter has the optimal yield estimation, and the maize yield could be predicted 2 months in advance compared with the maize harvest stage. As a result, we developed a crop yield forecasting method in this study to predict crop yield for county-scale. It has practical significance for maize yield forecasting and provides a relevant reference for similar crop yield estimation research.
|
Received: 2022-04-09
Accepted: 2022-07-20
|
|
Corresponding Authors:
LIU Zhao1, 2, LI Hua-peng1, CHEN Hui1, 2, ZHANG Shu-qing1*
E-mail: zhangshuqing@neigae.ac.cn
|
|
[1] HAN Dong-hui, ZHAO Jin-yuan, HU Qi, et al(韩冬荟, 赵金媛, 胡 琦, 等). Journal of China Agricultural University(中国农业大学学报), 2021, 26(3): 188.
[2] Sergio M, Vicente-Serrano, Jose M, et al. International Journal of Remote Sensing, 2006, 27(3): 511.
[3] Liu L Y, Wang J H, Bao Y S, et al. International Journal of Remote Sensing, 2006, 27(4): 737.
[4] Pan H, Chen Z, Wit A D, et al. Sensors (Basel, Switzerland), 2019, 19(14): 3161.
[5] JIN An-hua, WANG Peng-xin, QI Xuan, et al(靳安华, 王鹏新, 齐 璇, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2012, 28(6): 162.
[6] REN Jian-qiang, CHEN Zhong-xin, TANG Hua-jun, et al(任建强, 陈仲新, 唐华俊, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2011, 27(8): 257.
[7] Araya A, Hoogenboom G, Luedeling E, et al. Agricultural and Forest Meteorology, 2015, 214: 252.
[8] Anothai J, Soler C M T, Green A, et al. Agricultural and Forest Meteorology, 2013, 176: 64.
[9] Becker-Reshef I, Vermote E, Lindeman M, et al. Remote Sensing of Environment, 2010, 114(6): 1312.
[10] Sun J, Lai Z, Di L, et al. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, 13: 5048.
[11] Huang J, Tian L, Liang S, et al. Agricultural and Forest Meteorology, 2015, 204: 106.
[12] Xie Y, Wang P, Bai X, et al. Agricultural and Forest Meteorology, 2017, 246: 194.
[13] WANG Peng-xin, QI Xuan, LI Li, et al(王鹏新, 齐 璇, 李 俐, 等). Journal of Agricultural Machinery(农业机械学报), 2019, 50(7): 237.
[14] YU Hai-yang, CHEN Sheng-bo, YANG Bei-ping, et al(于海洋, 陈圣波, 杨北萍, 等). Global Geology(世界地质), 2020, 39(1): 208.
[15] Zhang J, Zhu Y, Zhang X, et al. Journal of Hydrology, 2018, 561: 918.
[16] Jiang H, Hu H, Zhong R, et al. Global Change Biology, 2020, 26(3): 1754.
[17] Greff K, Srivastava R K, Koutník J, et al. IEEE Transactions on Neural Networks and Learning Systems, 2016, 28(10): 2222.
[18] Lin T, Zhong R, Wang Y, et al. Environmental Research Letters, 2020, 15(3): 034016.
[19] Tian H, Wang P, Tansey K, et al. Agricultural and Forest Meteorology, 2021, 310: 108629.
[20] Zhang H, Kang J, Xu X, et al. Computers and Electronics in Agriculture, 2020, 176: 105618.
[21] Ren J Q, Chen Z X, Zhou Q B, et al. Journal of Remote Sensing, 2015, 19(4): 568.
[22] Shanahan J F, Schepers J S, Francis D D, et al. Agronomy Journal, 2021, 93(3): 583.
[23] AN Qin, CHEN Sheng-bo, SUN Shi-chao(安 秦, 陈圣波, 孙士超). Geospatial Information(地理空间信息),2018, 16(3): 14.
[24] Cao Q, Miao Y, Shen J, et al. Agronomy Journal, 2016, 17(4): 136.
[25] KANG Jun-feng, HUANG Lie-xing, ZHANG Chun-yan, et al(康俊锋, 黄烈星, 张春艳, 等). China Environmental Science(中国环境科学), 2020, 40(5): 1895.
[26] WANG Teng-jun, FANG Ke, YANG Yun, et al(王腾军, 方 珂, 杨 耘, 等). Bulletin of Surveying and Mapping(测绘通报), 2021,(11): 92.
[27] Saeys W, Mouazen A M, Ramon H. Biosystems Engineering, 2005, 91(4): 393.
[28] WANG Hai-jiang, JIANG Tian-chi, Yunger John A, et al(王海江, 蒋天池, Yunger John A, 等). Journal of Agricultural Machinery(农业机械学报), 2018, 49(5): 263.
[29] HAN Wen-ting, PENG Xing-shuo, ZHANG Li-yuan, et al(韩文霆, 彭星硕, 张立元, 等). Journal of Agricultural Machinery(农业机械学报), 2020, 51(1): 148.
[30] Li L, Wang B, Feng P, et al. Agricultural and Forest Meteorology, 2021, 308-309(4): 108558.
[31] WANG Lai-gang, XU Jian-hua, HE Jia, et al(王来刚, 徐建华, 贺 佳, 等). Journal of Maize Science(玉米科学),2020, 28(6): 88.
|
[1] |
LI Hu1, ZHONG Yun1, 2, FENG Ya-ting1, LIN Zhen1, ZHU Shi-jiang1, 2*. Multi-Vegetation Index Soil Moisture Inversion Model Based on UAV
Remote Sensing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 207-214. |
[2] |
LI Si-yuan, JIAO Jian-nan, WANG Chi*. Specular Reflection Removal Method Based on Polarization Spectrum
Fusion and Its Application in Vegetation Health Monitoring[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3607-3614. |
[3] |
ZHENG Shu-yuan1, 2, HAI Yan1, 2, HE Meng-qi1, 2, WANG Jian-xiong1, 2. Construction of Vegetation Index in Visible Light Band of GF-6 Image With Higher Discrimination[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3509-3517. |
[4] |
FU Xiao-man1, 2, BAO Yu-long1, 2*, Bayaer Tubuxin1, 2, JIN Eerdemutu1, 2, BAO Yu-hai1, 2. Spectral Characteristics Analysis of Desert Steppe Vegetation Based on Field Online Multi-Angle Spectrometer[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3170-3179. |
[5] |
DAI Shuo1, XIA Qing1*, ZHANG Han1, HE Ting-ting2, ZHENG Qiong1, XING Xue-min1, LI Chong3. Constructing of Tidal Flat Extraction Index in Coastal Zones Using Sentinel-2 Multispectral Images[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1888-1894. |
[6] |
MA Bao-dong, YANG Xiang-ru, JIANG Zi-wei, CHE De-fu. Influence and Quantitative Analysis of Coal Dust Retention on Reflectance Spectra and Vegetation Index of Leaves[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1947-1952. |
[7] |
REN Hong-rui1, 2, ZHANG Yue-qi2, HE Qi-jin3, LI Rong-ping1, ZHOU Guang-sheng4, 5*. Extraction of Pddy Rice Planting Area Based on Multi-Temporal FY-3 MERSI Remote Sensing Images[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(05): 1606-1611. |
[8] |
FAN Yi-guang1, 3, 5, FENG Hai-kuan1, 2, 3*, LIU Yang1, 3, 4, BIAN Ming-bo1, 3, ZHAO Yu1, 3, YANG Gui-jun1, 3, QIAN Jian-guo5. Estimation of Nitrogen Content in Potato Plants Based on Spectral Spatial Characteristics[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(05): 1532-1540. |
[9] |
MENG Hao-ran1, 2, LI Cun-jun1, 3*, ZHENG Xiang-yu1, 2, GONG Yu-sheng2, LIU Yu1, 3, PAN Yu-chun1, 3. Research on Extraction of Camellia Oleifera by Integrating Spectral, Texture and Time Sequence Remote Sensing Information[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(05): 1589-1597. |
[10] |
WANG Shao-yan1, CHEN Zhi-fei2, LUO Yang1, JIAN Chun-xia1, ZHOU Jun-jie3, JIN Yuan1, XU Pei-dan3, LEI Si-yue3, XU Bing-cheng1, 4*. Study on Nutrient Content of Bothriochloa Ischaemum Community in the Loess Hilly-Gully Region Based on Spectral Characteristics[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(05): 1612-1621. |
[11] |
ZHANG Hai-yang, ZHANG Yao*, TIAN Ze-zhong, WU Jiang-mei, LI Min-zan, LIU Kai-di. Extraction of Planting Structure of Winter Wheat Using GBDT and Google Earth Engine[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(02): 597-607. |
[12] |
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. |
[13] |
CHEN Li1, 2, 3, WANG Shi-yong 1, 3, GAO Si-li1, 3, TAN Chang1, 2, 3, LI Lin-han1, 2, 3. Multispectral Lightweight Ship Target Detection Algorithm for
Sentinel-2 Satellite[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(09): 2862-2869. |
[14] |
WANG Xiao-xuan1, LU Xiao-ping1*, MENG Qing-yan2, 3, LI Guo-qing4, WANG Jun4, ZHANG Lin-lin2, 3, YANG Ze-nan1. Inversion of Leaf Area Index Based on GF-6 WFV Spectral Vegetation
Index Model[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(07): 2278-2283. |
[15] |
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. |
|
|
|
|