光谱学与光谱分析 |
|
|
|
|
|
Study on the Prediction of Cotton Yield within Field Scale with Time Series Hyperspectral Imagery |
LIU Huan-jun1, KANG Ran1, Susan Ustin2, ZHANG Xin-le1*, FU Qiang1, SHENG Lei1, SUN Tian-yi1 |
1. College of Natural Resources and the Environment, Northeast Agricultural University, Harbin 150030, China 2. Center for Spatial Technologies and Remote Sensing (CSTARS), Department of Land, Air, and Water Resources, University of California, Davis, USA |
|
|
Abstract Pixel-based processing method mainly extracts spectral information from hyperspectral remote sensing images, but site specific management zone (SSMZ) delineation and crop yield estimation with images need to take spatiotemporal heterogeneity into account. As the spatial resolution of remote sensing data increases, the so-called “salt-and-pepper” problem of pixel-based classification becomes more serious. The spatiotemporal heterogeneity of soil properties and crop biophysical parameters are mainly delineated with grid sampling and geostatistics interpolation, but the widely used method has some problems: time consuming and high cost. Satellite imageries are introduced to delineate SSMZ, but there are also problems needed to be resolved: (1) single date imagery is used to map SSMZ which is difficult to determine the optimal date for SSMZ delineation; (2) only few SSMZs were mapped, which limited application of site specific fertilizing and management; (3) pixel-based method for SSMZ delineation didn’t concern the spatial relationship between pixels and site specific management does not implement at pixel level, but at SSMZ level. To improve the accuracy of crop yield estimation, a time-series of hyperspectral airborne images with high spatial resolution (1 m) of a cotton field, which is located in San Joaquin Valley, California US, were acquired and classified by using object-oriented segmentation, then yield predicting models were built, and the accuracy and stability of yield models were validated with determining coefficients R2 and the root mean square error (RMSE). Results are as follows: (1) object-oriented SSMZ delineating method combines spectral, spatial and temporal information, reduces noises in images and yield data, improves the accuracy of yield prediction; (2) for same SSMZ number, first derivative predicting model is more accurate; (3) for same spectral input, models with fewer SSMZs show higher accuracy, which is due to spatial errors of airborne images and yield data. The results will improve monitoring methods for crop growth and yield while accelerate the application of UAV remote sensing in precision agriculture.
|
Received: 2015-04-24
Accepted: 2015-08-20
|
|
Corresponding Authors:
ZHANG Xin-le
E-mail: zhangxinle@gmail.com
|
|
[1] Davatgar N, Neishabouri M R, Sepaskhah A R. Geoderma, 2012, 173: 111. [2] López-Lozano R, Casterad M A, Herrero J. Computers and Electronics in Agriculture, 2010, 73: 219. [3] Fleming K L, Westfall D G, Wiens D W, et al. Precision Agriculture, 2001, 2: 201. [4] Khosla R, Fleming K, Delagado J A, et al. Journal of Soil and Water Conservation, 2002, 57(6): 513. [5] Schepers A R, Shanahan J F, Liebig M A, et al. Agronomy Journal, 2004,96: 195. [6] LI Yan, SHI Zhou, WU Ci-fang, et al(李 艳,史 舟, 吴次芳,等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2007, 23(8): 84. [7] MENG Ji-hua, WU Bing-fang, DU Xin, et al(蒙继华, 吴炳方, 杜 鑫, 等). Remote Sensing for Land & Resources(国土资源遥感), 2011, 3: 1. [8] Yang C, Bradford J M, Wiegand C L. Transactions of the ASAE, 2001, 44(6): 1983. [9] Fleming K L, Heermann D F, Westfall D G. Agronomy Journal, 2004, 96: 1581. [10] JIANG Cheng, JIN Ji-yun(姜 城,金继运). Remote Sensing Technology and Application(遥感技术与应用), 2001, 16(1): 23. [11] WANG Ren-chao, ZHU De-feng(王人潮, 朱德峰). Journal of Remote Sensing(遥感学报), 1998, 2(2): 119. [12] Castillejo-González I L, Pena-Barragán J M, Jurado-Expósito M, et al. European Journal of Agronomy, 2014, 59: 57. [13] Hardin P J, Jensen R R. GIScience & Remote Sensing, 2011, 48(1): 99. [14] Scudiero E, Teatini P, Corwin D L, et al. Computersand Electronics in Agriculture, 2013, 99: 54. [15] Zarco-Tejada P J, Ustin S, Whiting M. Agronomy Journal, 2005, 97(3): 641. [16] Baatz M, Benz U, Dehghani S, et al. eCognition Professional User Guide 4, Definiens Imaging, Munich,2004. [17] Tian J, Chen D M. International Journal of Remote Sensing, 2007, 28(20): 4625. |
[1] |
SHEN Si-cong, ZHANG Jing-xue, CHEN Ming-hui, LI Zhi-wei, SUN Sheng-nan, YAN Xue-bing*. Estimation of Above-Ground Biomass and Chlorophyll Content of
Different Alfalfa Varieties Based on UAV Multi-Spectrum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3847-3852. |
[2] |
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. |
[3] |
GUO Zhou-qian1, 2, LÜ Shu-qiang1, 2, HOU Miao-le1, 2*, SUN Yu-tong1, 2, LI Shu-yang1, 2, CUI Wen-yi1. Inversion of Salt Content in Simulated Mural Based on Hyperspectral
Mural Salt Index[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3272-3279. |
[4] |
FENG Hai-kuan1, 2, YUE Ji-bo3, FAN Yi-guang2, YANG Gui-jun2, ZHAO Chun-jiang1, 2*. Estimation of Potato Above-Ground Biomass Based on VGC-AGB Model and Hyperspectral Remote Sensing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2876-2884. |
[5] |
JIN Chun-bai1, YANG Guang1*, LU Shan2*, LIU Wen-jing1, LI De-jun1, ZHENG Nan1. Band Selection Method Based on Target Saliency Analysis in Spatial Domain[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2952-2959. |
[6] |
GAO Yu1, SUN Xue-jian1*, LI Guang-hua2, ZHANG Li-fu1, QU Liang2, ZHANG Dong-hui1, CHANG Jing-jing2, DAI Xiao-ai3. Study on the Derivation of Paper Viscosity Spectral Index Based on Spectral Information Expansion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2960-2966. |
[7] |
KONG Bo1, YU Huan2*, SONG Wu-jie2, 3, HOU Yu-ting2, XIANG Qing2. Hyperspectral Characteristics and Quantitative Remote Sensing Inversion of Gravel Grain Size in the North Tibetan Plateau[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2381-2390. |
[8] |
LIU Zhao1, 2, LI Hua-peng1, CHEN Hui1, 2, ZHANG Shu-qing1*. Maize Yield Forecasting and Associated Optimum Lead Time Research Based on Temporal Remote Sensing Data and Different Model[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2627-2637. |
[9] |
ZHANG Xia1, WANG Wei-hao1, 2*, SUN Wei-chao1, DING Song-tao1, 2, WANG Yi-bo1, 2. Soil Zn Content Inversion by Hyperspectral Remote Sensing Data and Considering Soil Types[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2019-2026. |
[10] |
SONG Cheng-yang1, GENG Hong-wei1, FEI Shuai-peng2, LI Lei2, GAN Tian2, ZENG Chao-wu3, XIAO Yong-gui2*, TAO Zhi-qiang2*. Study on Yield Estimation of Wheat Varieties Based on Multi-Source Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2210-2219. |
[11] |
WANG Hui-min1, 2, YU Lei1, XU Kai-lei1, 2, JIANG Xiao-guang1, 2, WAN Yu-qing1, 2*. Estimation of Salt Content of Saline Soil in Arid Areas Based on GF-5 Hyperspectral Image[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2278-2286. |
[12] |
CAO Yang1, 2, LI Yan-hong1, 2*. Study on the Effects of NO2 Pollution Under COVID-19 Epidemic
Prevention and Control in Urumqi[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1981-1987. |
[13] |
CAO Yue1, BAO Ni-sha1, 2*, ZHOU Bin3, GU Xiao-wei1, 2, LIU Shan-jun1, YU Mo-li1. Research on Remote Sensing Inversion Method of Surface Moisture Content of Iron Tailings Based on Measured Spectra and Domestic Gaofen-5 Hyperspectral High-Resolution Satellites[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(04): 1225-1233. |
[14] |
ZHANG Chao1*, SU Xiao-yu1, XIA Tian2, YANG Ke-ming3, FENG Fei-sheng4. Monitoring the Degree of Pollution in Different Varieties of Maize Under Copper and Lead Stress[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(04): 1268-1274. |
[15] |
HU Yi-bin1, BAO Ni-sha1, 2*, LIU Shan-jun1, 2, MAO Ya-chun1, 2, SONG Liang3. Research on Hyperspectral Features and Recognition Methods of Typical Camouflage Materials[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(01): 297-302. |
|
|
|
|