1. 中国科学院数字地球重点实验室，中国科学院空天信息创新研究院，北京 100094
2. 三亚中科遥感研究所，海南 三亚 572029
3. Department of Earth Sciences, Indiana University-Purdue University Indianapolis (IUPUI), IN 46202, USA
4. 南京师范大学，江苏 南京 210023
5. 福建农林大学，福建 福州 350002
Effects of Different Spectral Resolutions on Modeling Soil Components
CHEN Yu1, WEI Yong-ming1, WANG Qin-jun1,2*, LI Lin3, LEI Shao-hua4, LU Chun-yan5
1. CAS Key Laboratory of Digital Earth Science，Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
2. Sanya Institute of Remote Sensing, Sanya 572029, China
3. Department of Earth Sciences, Indiana University-Purdue University Indianapolis (IUPUI), IN 46202, USA
4. Nanjing Normal University, Nanjing 210023, China
5. Fujian Agriculture and Forestry University, Fuzhou 350002, China
Abstract：The laboratory visible-near infrared (VIS-NIR) spectroscopy has been frequently used in quantifying soil components because it is effective, fast and nondestructive etc. The higher spectral resolution is the richer soil information we could obtain. However, hyperspectral data are red undant and should be preprocessed. The study of the effects of different spectral resolutions on the modeling of soil components is relatively inadequate. Taking advantage of the European Land Use/Cover Area Frame Statistical Survey (LUCAS) dataset having 19 036 soil samples, we investigate the effects of different spectral resolutions on modeling soil components: total soil nitrogen (N), organic carbon (OC), calcium carbonate (CaCO3), and clay. To achieve this, we took the partial least squares regression (PLS) method as the evaluation model and randomly chose 30% samples for independent verification. Firstly, the spectral data which have 4 200 bands with 0.5 nm spectral resolution were resampled to 2，4，8，…，1 024 nm respectively using average reflection value by of uniform interval sampling. The results are as follows: (1) when the spectral resolution was decreased, the inversion accuracy of soil components showed a downward trend; (2) when the spectral resolution was higher than 64 nm, higher model validation accuracies were obtained for estimating the four selected soil components (R2>0.65, RPD>1.7); (3) the accuracy for CaCO3 and clay components was significantly reduced when the spectral resolution was lower than 128 nm; (4) of the four soil components, CaCO3 was the most sensitive to spectral resolution. It has higher accuracy (R2>0.86，RPD>2.72) at high spectral resolutions, but the accuracy reduced most rapidly as the spectral resolution decreases. Secondly, based on the spectral response functions for a group of common satellite sensors, the inversion performances of using GF2, S3A, L8, Aster, S3OLCI, and Modis spectral bands are summarized as follows: (1) all sensors achieved higher accuracy for soil N and OC even if GF2 has 4 different bands (R2=0.56; RPD=1.51); (2) a low accuracy was obtained for CaCO3 and clay; (3) besides the number of spectral bands, the band positions are also important and the sensors (S3A, L8, Aster, and MODIS) having bands in the spectral range 1 100～2 500 nm showed a stronger performance than the sensor (e. g. S3OLCI) without the corresponding bands. The results from this study provide a guiding reference for preprocessing hyperspectral data of soil, selecting suitable satellite data sources and designing new optical sensors for soil Vis-NIR spectroscopy.
Key words：Soil components; Laboratory Vis-NIR spectroscopy (VIS-NIR); Satellite sensor; Spectral resolution; Partial least squares regression model (PLS)
陈 玉，魏永明，王钦军，LI Lin，雷少华，路春燕. 光谱分辨率对土壤组分建模影响分析[J]. 光谱学与光谱分析, 2021, 41(03): 865-870.
CHEN Yu, WEI Yong-ming, WANG Qin-jun, LI Lin, LEI Shao-hua, LU Chun-yan. Effects of Different Spectral Resolutions on Modeling Soil Components. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(03): 865-870.
[1] Rinnan Å, Berg F V D, Engelsen S B. Trends in Analytical Chemistry, 2009，28(10): 1201.
[2] Vašát R, Kodešová R, Klement A, et al. Geoderma, 2017，298: 46.
[3] WANG Wen-jun, LI Zhi-wei, WANG Can, et al(王文俊，李志伟，王 璨，等). Spectroscopy and Spectral Analysis（光谱学与光谱分析），2019，39(5): 1579.
[4] Li Y，Altaner C M. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2019，213: 111.
[5] Shi L, Westerhuis J A, Rosen J, et al. Bioinformatics, 2019，35(6): 972.
[6] Jia S, Li H, Wang Y, et al. Geoderma, 2016，268: 92.
[7] Cécillon L, Cassagne N, Czarnes S, et al. Soil Biology and Biochemistry, 2008，40(7): 1975.
[8] González Costa J J, Reigosa M J, Matías J M, et al. Science of The Total Environment, 2017，593-594: 508.
[9] Tsakiridis Nikolaos L, Chadoulos Christos G, Theocharis John B, et al. Neurocomputing, 2020, 389: 27.
[10] ZHAO Liang, WANG Li-guo, LIU Dan-feng(赵 亮, 王立国, 刘丹凤). Journal of Remote Sensing(遥感学报)，2019，23(5): 904.
[11] XIAO Wen-ping, LÜ Cheng-wen, QIAO Tian, et al(肖文凭，吕成文，乔 天，等). Chinese Journal of Soil Science(土壤通报), 2018，49(6): 1279.
[12] Adeline K R M, Gomez C, Gorretta N, et al. Geoderma, 2017，288: 143.
[13] Orgiazzi A, Ballabio C, Panagos P, et al. European Journal of Soil Science, 2018，69: 140.
[14] Nocita M, Stevens A, Toth G, et al. Soil Biology and Biochemistry, 2014，68: 337.
[15] Saeys W, Mouazen A M，Ramon H. Biosystems Engineering, 2005，91(4): 393.
