Satellite Bands Based Estimation of Nitrogen Concentration in Potato Plants
YANG Hai-bo1,2, GAO Xing1,2, HUANG Shao-fu1,2, ZHANG Jia-kang1,2, YANG Liu1,2, LI Fei1,2*
1. College of Grassland, Resources and Environment, Inner Mongolia Agricultural University, Huhhot 010011, China
2. Inner Mongolia Key Laboratory of Soil Quality and Nutrient Resources, Inner Mongolia Agricultural University, Huhhot 010011, China
Abstract:Plant nitrogen concentration (PNC) is one of the most important nutrient elements that directly affectscrop growth and yield. High-throughput ground-based remote sensing, passive or active re?ectance sensors, has the potential to provide more information for making better-informed management decisions for nitrogen fertilizer inputs at the canopy scale in real time but it is difficult to obtain data at the regional scale. For that multi-channel satellites with high spatial resolution (WorldView-2 and VENμS satellites with red edge bands) were tested in this study to estimating PNC on large scale at different growth stages of potato varieties. Experiments were conducted in 2014, 2015 and 2016 to remotely estimate the PNC of diverse potato varieties under different nitrogen levels in Wuchuan County of Northern Yin mountain, Inner Mongolia. The results showed that the combined spectral index NDRE/NDVI based on the red, red edge and near-infrared channels of the VENμS and WorldView-2 satellite is superior to other indices in estimating the PNC of potato varieties. The spectral index (NDRE/NDVI) of VENμS and WorldView-2 satellites had a high correlation with PNC at different growth stages, and the correlation coefficient ranged between 0.63 and 0.81. The spectral index (NDRE/NDVI) of VENμS had the highest correlation coefficient with PNC (r=0.81) at reproduction growth stage. The growth stages significantly affected the spectral index to estimate the PNC. The calibration models of spectral index (NDRE/NDVI) of two satellites based on the data of three years was validated to predict the PNC at reproduction growth stage. The predictive model of VENμS-NDRE/NDVI had the highest coefficient of determination (0.56), the lowest RMSE (0.38%) and RE (10.45%) with a slope of 0.82, as well as the predictive model of WorldView-2-NDRE/NDV had higher coefficient of determination (0.49), lower RMSE (0.41%) and RE (11.12%) with a slope of 0.78. In conclusion, the results of multi-channel satellite simulations showed that the combined spectral index based on the red edge width band can be used to monitor the PNC of potato varieties.
Key words:Potato; Nitrogen concentration; Spectral index
[1] Hadden D, Grelle A. Agricultural and Forest Meteorology, 2017, 243: 1.
[2] Gilliam F S, Billmyer J H, Walter C A, et al. Atmospheric Environment, 2016, 146: 261.
[3] Lui Y T, Song Y Z, Ma X Y, et al. Journal of Agricultural Mechanization Research, 2018, 2: 029.
[4] Cheng T, Yang Z G, Zhu Y, et al. Remote Sensing, 2016, 8(2), 116.
[5] Zhao B, Liu Z, Ata-Ul-Karim S T, et al. Field Crops Research, 2016, 185: 59.
[6] Zhao C, Li H, Li P, et al. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(1): 236.
[7] Li H, Zhao C, Yang G, et al. Remote Sensing of Environment, 2015, 169: 358.
[8] Tian Y C, Gu K J, Chu X, et al. Plant and Soil, 2014, 376(1-2): 193.
[9] WANG Ren-hong, SONG Xiao-yu, YANG Gui-jun,et al(王仁红,宋晓宇,杨贵军, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报),2014, 30(19): 191.
[10] LI Dan, LI Fei, HU Yun-cai(李 丹, 李 斐, 胡云才). Spectroscopy and Spectral Analysis(光谱学与光谱分析),2016, 36(4): 1150.
[11] Croft H, Chen J M, Zhang Y, et al. Remote Sensing of Environment, 2013, 133:128.
[12] Li J, Fan K, Zhou L. Remote Sensing, 2017, 9(7): 628.
[13] WANG Bei-zhan, FENG Xiao, WEN Nuan, et al(王备战,冯 晓,温 暖,等). Scientia Agricultura Sinica(中国农业科学), 2012 45(15):3049.
[14] LI Fen-ling, CHANG Qing-rui, SHEN Jian,et al(李粉玲, 常庆瑞, 申 健, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2016, 32(9):157.
[15] Li F, Mistele B, Hu Y, et al. ISPRS Journal of Photogrammetry and Remote Sensing, 2014, 92: 112.
[16] Samborski S M, Gozdowski D, Walsh O S, et al. Agronomy Journal, 2015, 107(6): 2097.
[17] Marx A, Kleinschmit B. iForest-Biogeosciences and Forestry, 2017, 10(4): 659.
[18] Manzo C, Mei A, Salvatori R, et al. Construction and Building Materials, 2014, 61: 147.