光谱学与光谱分析 |
|
|
|
|
|
Winter Wheat Area Estimation with MODIS-NDVI Time Series Based on Parcel |
LI Le, ZHANG Jin-shui*, ZHU Wen-quan, HU Tan-gao, HOU Dong |
State Key Laboratory of Earth Processes and Resource Ecology, College of Resources Science & Technology, Beijing Normal University, Beijing 100875, China |
|
|
Abstract Several attributes of MODIS (moderate resolution imaging spectrometer) data, especially the short temporal intervals and the global coverage, provide an extremely efficient way to map cropland and monitor its seasonal change. However, the reliability of their measurement results is challenged because of the limited spatial resolution. The parcel data has clear geo-location and obvious boundary information of cropland. Also, the spectral differences and the complexity of mixed pixels are weak in parcels. All of these make that area estimation based on parcels presents more advantage than on pixels. In the present study, winter wheat area estimation based on MODIS-NDVI time series has been performed with the support of cultivated land parcel in Tongzhou, Beijing. In order to extract the regional winter wheat acreage, multiple regression methods were used to simulate the stable regression relationship between MODIS-NDVI time series data and TM samples in parcels. Through this way, the consistency of the extraction results from MODIS and TM can stably reach up to 96% when the amount of samples accounts for 15% of the whole area. The results shows that the use of parcel data can effectively improve the error in recognition results in MODIS-NDVI based multi-series data caused by the low spatial resolution. Therefore, with combination of moderate and low resolution data, the winter wheat area estimation became available in large-scale region which lacks completed medium resolution images or has images covered with clouds. Meanwhile, it carried out the preliminary experiments for other crop area estimation.
|
Received: 2010-05-17
Accepted: 2010-08-28
|
|
Corresponding Authors:
ZHANG Jin-shui
E-mail: zhangjsh@ires.cn
|
|
[1] JIAO Xian-feng,YANG Bang-jie,PEI Zhi-yuan(焦险峰,杨邦杰,裴志远). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报),2006,22(5): 105. [2] CHEN Shu-peng(陈述彭). Geo-Science Explore Ⅲ(Remote Sensing Application)(地学的探索, 第3卷, 遥感应用). Beijing: Science Press(北京: 科学出版社), 1990. [3] ZHOU Qing-bo(周清波). Journal of China Agricultural Resources and Regional Plannin(中国农业资源与区划), 2004, 25(5): 9. [4] Lobell D B, Asner G P. Remote Sensing of Environment, 2004, 93: 412. [5] Langley S K, Cheshire H M, Humes K S, et al. Journal of Arid Environments, 2001, 49: 401. [6] Wessels K J, De Fries R S, Dempewolf J, et al. Remote Sensing of Environment, 2004, 92: 67. [7] Chen Dao-yi, Huang Jing-feng, Jackson J T. Remote Sensing of Environment, 2005, 98: 225. [8] Busetto L, Meroni M, Colombo R. Remote Sensing of Environment, 2008,112:118. [9] Fang H, Wu B, Liu H, et al. International Journal of Remote Sensing, 1998, 19: 521. [10] GAO Jian-feng, JIANG Xiao-san, LIU Shao-gui, et al(高建峰,姜小三,刘绍贵,等). Soils(土壤), 2008, 40(3):484. [11] Zhang Mingwei, Zhou Qingbo, Chen Zhongxin, et al. International Journal of Applied Earth Observation and Geoinformation, 2008, 10:476. [12] GU Xiao-he, PAN Yao-zhong, ZHU Xiu-fang, et al(顾晓鹤,潘耀忠,朱秀芳,等). Journal of Remote Sensing(遥感学报), 2007, 11(3): 350. [13] XU Wen-bo, ZHANG Guo-ping, FAN Jin-long, et al(许文波, 张国平, 范锦龙,等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报),2007,23(12): 144. [14] Lobo A, Chic O,Casterad A. International Journal of Remote Sensing, 1996, 17: 2385. [15] Aplin P,Atkinson P M. Photogrammetric Engineering and Remote Sensing, 2004, 70: 141. [16] Chen Jin, Jonsson P, Tamura M, et al. Remote Sensing of Environment, 2004, 91:332. [17] Oetter Doug R, Cohen Warren B, Berterretche Mercedes, et al. Remote Sensing of Environment, 2000, 76: 139. [18] QI La, ZHAO Chun-jiang, LI Cun-jun, et al(齐 腊, 赵春江, 李存军,等). Chinese Journal of Applied Ecology(应用生态学报), 2008, 19 (10): 2201. [19] https://wist.echo.nasa.gov/api/. [20] XU Jian-hua(徐建华). Mathematical Methods in Contemporary Geography(现代地理学中的数学方法). Beijing: Higher Education Press(北京:高等教育出版社), 2002. 37.
|
[1] |
XUE Fang-jia, YU Jie*, YIN Hang, XIA Qi-yu, SHI Jie-gen, HOU Di-bo, HUANG Ping-jie, ZHANG Guang-xin. A Time Series Double Threshold Method for Pollution Events Detection in Drinking Water Using Three-Dimensional Fluorescence Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3081-3088. |
[2] |
HUANG Chao1, 2, ZHAO Yu-hong1, ZHANG Hong-ming2*, LÜ Bo2, 3, YIN Xiang-hui1, SHEN Yong-cai4, 5, FU Jia2, LI Jian-kang2, 6. Development and Test of On-Line Spectroscopic System Based on Thermostatic Control Using STM32 Single-Chip Microcomputer[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2734-2739. |
[3] |
TANG Ting, PAN Xin*, LUO Xiao-ling, GAO Xiao-jing. Fusion of ConvLSTM and Multi-Attention Mechanism Network for
Hyperspectral Image Classification[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2608-2616. |
[4] |
ZHANG Zhi-yue, ZHANG Wen-jie, HAN Xiang-na*. Evaluation of the Aging Property of Bamboo Paper Used for the Restoration of Pengbihushi in the Palace Museum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1968-1973. |
[5] |
LI Zhao, WANG Ya-nan, XU Yi-pu, CAO Jing, WANG Yong-feng, WU Kun-yao, DENG Lu. Synthesis and Photoluminescence of Blue-Emitting Phosphor
YVO4∶Tm3+ for White Light Emitting Diodes[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(02): 623-628. |
[6] |
YAN Peng-cheng1, 2, ZHANG Xiao-fei2*, SHANG Song-hang2, ZHANG Chao-yin2. Research on Mine Water Inrush Identification Based on LIF and
LSTM Neural Network[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(10): 3091-3096. |
[7] |
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. |
[8] |
WANG Hong-wei1, WANG Bo2, JI Tong3, XU Jun4, JU Feng5, WANG Cai-ling6*. Simulation Estimation of BOD Content in Water Based on Hyperspectra[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(03): 978-983. |
[9] |
WANG Lei1, 2, QIN Hong1,2*, LI Jing3, ZHANG Xiao-bo3, YU Li-na1, 2, LI Wei-jun1, 2, HUANG Lu-qi4*. Geographical Origin Identification of Lycium Barbarum Using Near-Infrared Hyperspectral Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(04): 1270-1275. |
[10] |
YU Hao-yue, SHEN Tao*, ZHU Yan, LIU Ying-li, YU Zheng-tao. Terahertz Spectral Recognition Based on Bidirectional Long Short-Term Memory Recurrent Neural Network[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(12): 3737-3742. |
[11] |
ZHANG Yi-zhuo, XU Miao-miao, WANG Xiao-hu, WANG Ke-qi*. Hyperspectral Image Classification Based on Hierarchical Fusion of Residual Networks[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(11): 3501-3507. |
[12] |
YANG Chang-bao1, GAO Wen-bo1*, HOU Guang-yu2, LI Xing-zhe1, GAO Man-ting1. Response Relationship between Feldspar Content and Characteristic Spectra in Igneous Rocks[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(07): 2077-2082. |
[13] |
HUANG Shuang-yan1, 2, YANG Liao1, CHEN Xi1*, YAO Yuan1, 2. Study of Typical Arid Crops Classification Based on Machine Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(10): 3169-3176. |
[14] |
Jong-Moon Kim1, Myung Duk Jang2, JIN Biao3*, Yoon Jung Jang4*. Comparison of Enhancement Effect of DNA-Mediated Energy Transfer by Divalent Cations: Mg2+, Ca2+, Mn2+, Co2+, and Ni2+[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(06): 1970-1974. |
[15] |
SUN Gui-fen1, QIN Xian-lin1*, YIN Ling-yu1, LIU Shu-chao1, LI Zeng-yuan1, CHEN Xiao-zhong2, ZHONG Xiang-qing2. Changes Analysis of Post-Fire Vegetation Spectrum and Index Based on Time Series GF-1 WFV Images[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(02): 511-517. |
|
|
|
|