|
|
|
|
|
|
| Inversion of Rice Leaf Chlorophyll Content Based on Sentinel-2 Satellite Data |
| YANG Xu, LU Xue-he, SHI Jing-ming, LI Jing, JU Wei-min* |
International Institute for Earth System Science, Nanjing University, Nanjing 210023, China
|
|
|
|
|
Abstract Chlorophyll content is an important indicator of crop health, plant productivity, and environmental stress. Real-time, fast and accurate acquisition of leaf chlorophyll content of crops is of significant for monitoring crop growth. Remote sensing is an effective way to retrieve leaf chlorophyll content of crops at regional and global scales. However, previous studies retrieving leaf chlorophyll content of crops does not fully consider the impact of underlying surface background, limiting retrieval accuracy. To this end, this paper aims at the inversion of rice leaf chlorophyll content from Sentinel-2 remote sensing satellite data using a look-up table based approach. The look-up table was simulated using the PRAOSAIL radiation transfer model. The applicability of chlorophyll indices (CI) calculated from the reflectance of the green band and different red-edge bands and the spectral index (Zarco and Miller, ZM) constructed by two different red edge bands in inverting leaf chlorophyll content was evaluated using field measurements. The greenness index (G) was integrated with CI and ZM to constrain the impact of background on the inversion of leaf chlorophyll content. The main findings of this study are: (1) The accuracy of leaf element content inversion based on the spectral index constructed in different bands is different, and CI740 performed the best (R2=0.79, RMSE=9.02 μg·cm-2), followed by ZM (R2=0.71, RMSE=10.53 μg·cm-2), CI705(R2=0.69, RMSE=9.17 μg·cm-2), and CI783(R2=0.67, RMSE=10.84 μg·cm-2); (2) The inverted leaf chlorophyll content is significantly affected by the background, especially at the early stage of rice growth. The inverted leaf chlorophyll content was systematically lower than observations (mean relative error (MRE) in the range from -18.87% to -31.94%) owing to strong background interference; (3) CI/G and ZM/G can effectively eliminate the influence of background and improve the accuracy of rice leaf chlorophyll inversion. At the early stage of rice growth, inversion based on CI/G and ZM/G significantly improves agreement between inverted and observed leaf chlorophyll content (MRE in the range from 8.11% to 18.11%). These findings are of great significance for improving the inversion of leaf chlorophyll content under different leaf area index levels of rice from remote sensing data.
|
|
Received: 2021-02-08
Accepted: 2021-04-01
|
|
|
|
Corresponding Authors:
JU Wei-min
E-mail: juweimin@nju.edu.cn
|
|
[1] Houborg R, Cescatti A, Migliavacca M, et al. Agricultural & Forest Meteorology, 2013, 177: 10.
[2] Xue L, Yang L. ISPRS Journal of Photogrammetry and Remote Sensing, 2009, 64(1): 97.
[3] Combal B, Baret F, Weiss M, et al. Remote Sensing of Environment, 2003, 84(1): 1.
[4] Darvish Z R, Skidmore A, Schlerf M, et al. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2012, 5(4): 1222.
[5] Houborg R, Soegaard H, Ryad B, et al. Remote Sensing of Environment, 2007, 106(1): 39.
[6] Jay S, Maupas F, Bendoula R, et al. Field Crops Research, 2017, 210: 33.
[7] Croft H, Chen J M, Wang R, et al. Remote Sensing of Environment, 2020, 236: 111479.
[8] Zhang Y, Chen J M, Miller J R, et al. Remote Sensing of Environment, 2008, 112(7): 3234.
[9] Xu M, Liu R, Chen J M, et al. Remote Sensing of Environment, 2019, 224: 60.
[10] Hilker T, Lênio S G, Arago L E O C, et al. International Journal of Applied Earth Observation and Geoinformation, 2017, 58: 278.
[11] Croft H, Chen J M, Zhang Y. Ecological Complexity, 2014, 17: 119.
[12] Zarco T P J, Miller J R, Noland T L, et al. IEEE Transactions on Geoscience and Remote Sensing, 2001, 39(7): 1491.
[13] Gitelson A A, Merzlyak M N. International Journal of Remote Sensing, 1997, 18(12): 2691.
[14] Yin C, He B, Quan X W, et al. International Journal of Remote Sensing, 2016, 37(3): 615.
[15] Feret J B, Francois C, Asner G P, et al. Remote Sensing of Environment, 2011, 112(6): 3030.
[16] Verhoef W, Jia L, Xiao Q, et al. IEEE Transactions on Geoscience and Remote Sensing, 2007, 45(6): 1808.
[17] Zarco T P J, Berjón A, Lozano L R, et al. Remote Sensing of Environment, 2005, 99(3): 271.
[18] Blackburn G A. Remote Sensing of Environment, 1998, 66(3): 273.
|
| [1] |
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. Intelligent Recognition of Corn Residue Cover Area by Time-Series
Sentinel-2A Images[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1948-1955. |
| [2] |
LIANG Hao1, XU Wei-xin1*, DUAN Xu-hui1, ZHANG Juan2, DAI Na1, XIAO Qiang-zhi1, WANG Qi-yu1. Threshold Calibration of Key Parameters of Withered Grass Based on PROSAIL Model in Qinghai-Tibet Plateau[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1144-1149. |
| [3] |
JIANG Ling-ling1, WANG Long-xiao1, 2 , WANG Lin2*, GAO Si-wen1, YUE Jian-quan1. Research on Remote Sensing Retrieval of Bohai Sea Transparency
Based on Sentinel-3 OLCI Image[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1209-1216. |
| [4] |
WANG Chun-juan1, 2, ZHOU Bin1, 2*, ZHENG Yao-yao3, YU Zhi-feng1, 2. Navigation Observation of Reflectance Spectrum of Water Surface in Inland Rivers[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(03): 878-883. |
| [5] |
NIU Teng1, 3, LU Jie1, 2*, YU Jia-xin4, WU Ying-da5, LONG Qian-qian3, YU Qiang3. Research on Inversion of Water Conservation Distribution of Forest Ecosystem in Alpine Mountain Based on Spectral Features[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(02): 530-536. |
| [6] |
LONG Qian-qian1, ZHOU Ren-hao2, YUE De-peng1, NIU Teng1, MAO Xue-qing1, WANG Peng-chong3, YU Qiang1*. Research on Inversion of Water Conservation Capacity of Forest Litter in Yarlung Zangbo Grand Canyon Based on Spectral Features[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(01): 229-235. |
| [7] |
ZHANG Zi-han1, YAN Lei1,2, LIU Si-yuan1, FU Yu1, JIANG Kai-wen1, YANG Bin3, LIU Sui-hua4, ZHANG Fei-zhou1*. Leaf Nitrogen Concentration Retrieval Based on Polarization Reflectance Model and Random Forest Regression[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(09): 2911-2917. |
| [8] |
SU Wei1,2, WU Jia-yu1,2, WANG Xin-sheng1,2, XIE Zi-xuan1,2, ZHANG Ying1,2, TAO Wan-cheng1,2, JIN Tian1,2. Retrieving Corn Canopy Leaf Area Index Based on Sentinel-2 Image and PROSAIL Model Parameter Calibration[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(06): 1891-1897. |
| [9] |
PANG Shu-na1, ZHU Wei-ning1*, CHEN Jiang2, SUN Nan3, HUANG Li-tong1, ZHANG Yu-sen1, ZHANG Ze-liang1. Using Landsat-8 to Remotely Estimate and Observe Spatio-Temporal Variations of Total Suspended Matter in Zhoushan Coastal Regions[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(12): 3826-3832. |
| [10] |
ZOU Bin, TU Yu-long, JIANG Xiao-lu, TAO Chao, ZHOU Mo, XIONG Li-wei. Estimation of Cd Content in Soil Using Combined Laboratory and Field DS Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(10): 3223-3231. |
| [11] |
SU Wei1, 2, ZHAO Xiao-feng3, SUN Zhong-ping4*, ZHANG Ming-zheng1, 2, ZOU Zai-chao1, 2, WANG Wei1, 2, SHI Yuan-li4. Estimating the Corn Canopy Chlorophyll Content Using the Sentinel-2A Image[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(05): 1535-1542. |
| [12] |
FENG Hai-ying1, FENG Zhong-ke1*, FENG Hai-xia2. One New Method of PM2.5 Concentration Inversion Based on Difference Index[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(10): 3012-3016. |
| [13] |
YANG Liu1，2, FENG Zhong-ke1*, YUE De-peng1, SUN Jin-hua3. Forest Stock Volume Estimation Model Using Textural and Topographic Factors of Landsat8 OLI[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2017, 37(07): 2140-2145. |
| [14] |
JIANG Hai-ling1, 2, ZHANG Li-fu2*, YANG Hang2, CHEN Xiao-ping3, TONG Qing-xi1, 2 . Research on Spectral Scale Effect in the Estimation of Vegetation Leaf Chlorophyll Content [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2016, 36(01): 169-176. |
| [15] |
MA Wei-wei1, GONG Cai-lan1*, HU Yong1, WEI Yong-lin2, LI Long3, LIU Feng-yi1, MENG Peng1 . Hyperspectral Remote Sensing Estimation Models for Pasture Quality [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2015, 35(10): 2851-2855. |
|
|
|
|
