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Predicting Surface Roughness and Moisture of Bare Soils Using Multiband Spectral Reflectance Under Field Conditions

CHEN Si ZHAO Kai JIANG Tao LI Xiaofeng ZHENG Xingming WAN Xiangkun ZHAO Xiaowei

CHEN Si, ZHAO Kai, JIANG Tao, LI Xiaofeng, ZHENG Xingming, WAN Xiangkun, ZHAO Xiaowei. Predicting Surface Roughness and Moisture of Bare Soils Using Multiband Spectral Reflectance Under Field Conditions[J]. 中国地理科学, 2018, 28(6): 986-997. doi: 10.1007/s11769-018-1007-x
引用本文: CHEN Si, ZHAO Kai, JIANG Tao, LI Xiaofeng, ZHENG Xingming, WAN Xiangkun, ZHAO Xiaowei. Predicting Surface Roughness and Moisture of Bare Soils Using Multiband Spectral Reflectance Under Field Conditions[J]. 中国地理科学, 2018, 28(6): 986-997. doi: 10.1007/s11769-018-1007-x
CHEN Si, ZHAO Kai, JIANG Tao, LI Xiaofeng, ZHENG Xingming, WAN Xiangkun, ZHAO Xiaowei. Predicting Surface Roughness and Moisture of Bare Soils Using Multiband Spectral Reflectance Under Field Conditions[J]. Chinese Geographical Science, 2018, 28(6): 986-997. doi: 10.1007/s11769-018-1007-x
Citation: CHEN Si, ZHAO Kai, JIANG Tao, LI Xiaofeng, ZHENG Xingming, WAN Xiangkun, ZHAO Xiaowei. Predicting Surface Roughness and Moisture of Bare Soils Using Multiband Spectral Reflectance Under Field Conditions[J]. Chinese Geographical Science, 2018, 28(6): 986-997. doi: 10.1007/s11769-018-1007-x

Predicting Surface Roughness and Moisture of Bare Soils Using Multiband Spectral Reflectance Under Field Conditions

doi: 10.1007/s11769-018-1007-x
基金项目: Under the auspices of the Excellent Youth Talent Project of Jilin Science and Technology Development Program (No. 20170520078JH), Science and Technology Basic Work of Science and Technology (No. 2014FY210800-4), National Natural Science Foundation of China (No. 41601382)
详细信息
    通讯作者:

    ZHENG Xingming.E-mail:zhengxingming@iga.ac.cn

Predicting Surface Roughness and Moisture of Bare Soils Using Multiband Spectral Reflectance Under Field Conditions

Funds: Under the auspices of the Excellent Youth Talent Project of Jilin Science and Technology Development Program (No. 20170520078JH), Science and Technology Basic Work of Science and Technology (No. 2014FY210800-4), National Natural Science Foundation of China (No. 41601382)
More Information
    Corresponding author: ZHENG Xingming.E-mail:zhengxingming@iga.ac.cn
  • 摘要: Soil surface roughness, denoted by the root mean square height (RMSH), and soil moisture (SM) are critical factors that affect the accuracy of quantitative remote sensing research due to their combined influence on spectral reflectance (SR). In regards to this issue, three SM levels and four RMSH levels were artificially designed in this study; a total of 12 plots was used, each plot had a size of 3 m×3 m. Eight spectral observations were conducted from 14 to 30 October 2017 to investigate the correlation between RMSH, SM, and SR. On this basis, 6 commonly used bands of optical satellite sensors were selected in this study, which are red (675 nm), green (555 nm), blue (485 nm), near infrared (845 nm), shortwave infrared 1 (1600 nm), and shortwave infrared 2 (2200 nm). A negative correlation was found between SR and RMSH, and between SR and SM. The bands with higher coefficient of determination R2 values were selected for stepwise multiple nonlinear regression analysis. Four characterized bands (i.e., blue, green, near infrared, and shortwave infrared 2) were chosen as the independent variables to estimate SM with R2 and root mean square error (RMSE) values equal to 0.62 and 2.6%, respectively. Similarly, the four bands (green, red, near infrared, and shortwave infrared 1) were used to estimate RMSH with R2 and RMSE values equal to 0.48 and 0.69 cm, respectively. These results indicate that the method used is not only suitable for estimating SM but can also be extended to the prediction of RMSH. Finally, the evaluation approach presented in this paper highly restores the real situation of the natural farmland surface on the one hand, and obtains high precision values of SM and RMSH on the other. The method can be further applied to the prediction of farmland SM and RMSH based on satellite and unmanned aerial vehicle (UAV) optical imagery.
  • [1] Álvarez-Mozos J, Verhoest N E C, Larrañaga A et al., 2009. In-fluence of surface roughness spatial variability and temporal dynamics on the retrieval of soil moisture from SAR observa-tions. Sensors, 9(1):463-489. doi:10.3390/s 90100463
    [2] Anderson K, Kuhn N J, 2008. Variations in soil structure and reflectance during a controlled crusting experiment. Interna-tional Journal of Remote Sensing, 29(12):3457-3475. doi: 10.1080/01431160701767435
    [3] Bögel T, Osinenko P, Herlitzius T, 2016. Assessment of soil roughness after tillage using spectral analysis. Soil and Tillage Research, 159:73-82. doi: 10.1016/j.still.2016.02.004
    [4] Bowers S A, Smith S J, 1972. Spectrophotometric determination of soil water content. Soil Science Society of America Proceedings, 36:978-980. doi:10.2136/sssaj1972.03615995 003600060045x
    [5] Bryant R, Moran M S, Thoma D P et al., 2007. Measuring surface roughness height to parameterize radar backscatter models for retrieval of surface soil moisture. IEEE Geoscience and Remote Sensing Letters, 4(1):137-141. doi:10.1109/LGRS. 2006.887146
    [6] Cierniewski J, 1993. Soil moisture tension and soil spectral re-flectance on the example of the Koscian'Plain soils. Fotoin-terpretacja w Geografii, 23:107-23.
    [7] Cierniewski J, Ceglarek J, Karnieli A et al., 2017. Predicting the diurnal blue-sky albedo of soils using their laboratory reflec-tance spectra and roughness indices. Journal of Quantitative Spectroscopy and Radiative Transfer, 200:25-31. doi: 10.1016/j.jqsrt.2017.05.033
    [8] Cierniewski J, Karnieli A, Ka?mierowski C et al., 2015. Effects of soil surface irregularities on the diurnal variation of soil broadband blue-sky albedo. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(2):493-502. doi: 10.1109/JSTARS.2014.2330691
    [9] Croft H, Anderson K, Kuhn N J, 2009. Characterizing soil surface roughness using a combined structural and spectral approach. European Journal of Soil Science, 60(3):431-442. doi: 10.1111/j.1365-2389.2009.01129.x
    [10] Croft H, Anderson K, Kuhn N J, 2012a. Reflectance anisotropy for measuring soil surface roughness of multiple soil types. Catena, 93(6):87-96. doi: 10.1016/j.catena.2012.01.007
    [11] Croft H, Anderson K, Kuhn N J, 2014. Evaluating the influence of surface soil moisture and soil surface roughness on optical directional reflectance factors. European Journal of Soil Sci-ence, 65(4):605-612. doi: 10.1111/ejss.12142
    [12] Croft H, Kuhn N J, Anderson K, 2012b. On the use of remote sensing techniques for monitoring spatio-temporal soil organic carbon dynamics in agricultural systems. Catena, 94(9):64-74. doi: 10.1016/j.catena.2012.01.00
    [13] Deng Ruru, Tian Guoliang, Liu Qinhuo et al., 2004. Research on remote sensing model for soil water on rough surface. Journal of Remote Sensing, 8(1):75-80. (in Chinese)
    [14] Dor E B, Ong C, Lau I C, 2015. Reflectance measurements of soils in the laboratory:standards and protocols. Geoderma, 245-246:112-124. doi: 10.1016/j.geoderma.2015.01.002
    [15] Dotto A C, Dalmolin R S D, Grunwald S et al., 2017. Two pre-processing techniques to reduce model covariables in soil property predictions by Vis-NIR spectroscopy. Soil and Tillage Research, 172:59-68. doi: 10.1016/j.still.2017.05.008
    [16] Helming K, Römkens M J M, Prasad S N, 1998. Surface rough-ness related processes of runoff and soil loss:a flume study. Soil Science Society of America Journal, 62(1):243-250. doi: 10.2136/sssaj1998.03615995006200010031x
    [17] Huang C H, Bradford J, M, 1992. Applications of a laser scanner to quantify soil microtopography. Soil Science Society of America Journal, 56(1):14-21. doi:10.2136/sssaj1992.03615 995005600010002x
    [18] Irons J R, Weismiller R A, Petersen G W, 1989. Soil reflectance. In:Asrar G (ed.). Theory and Applications of Optical Remote Sensing. New York:John Wiley and Sons, 66-106.
    [19] Karunatilake U P, Van Es H M V, 2002. Rainfall and tillage effects on soil structure after alfalfa conversion to maize on a clay loam soil in New York. Soil and Tillage Research, 67(2):135-146. doi: 10.1016/S0167-1987(02)00056-9
    [20] Liu W D, Baret F, Gu X F et al., 2002. Relating soil surface moisture to reflectance. Remote Sensing of Environment, 81(2-3):238-246. doi: 10.1016/S0034-4257(01)00347-9
    [21] Li Xiaojie, Zhao Kai, Zheng Xingming, 2012. Development of surface roughness tester based on laser triangulation method. Transactions of the Chinese Society of Agricultural Engineer-ing, 28(8):116-121. (in Chinese)
    [22] Lobell D B, Asner G P, 2002. Moisture effects on soil reflectance. Soil Science Society of America Journal, 66(3):722-727. doi: 10.2136/sssaj2002.722
    [23] Marzahn P, Ludwig R, 2009. On the derivation of soil surface roughness from multi parametric PolSAR data and its potential for hydrological modeling. Hydrology and Earth System Sci-ences, 13(3):381-394. doi: 10.5194/hess-13-381-2009
    [24] Matthias A D, Fimbres A, Sano E E et al., 2000. Surface roughness effects on soil albedo. Soil Science Society of America Journal, 64(3):1035-1041. doi:10.2136/sssaj2000. 6431035x
    [25] Mouazen A M, Karoui R, De Baerdemaeker J et al., 2006. Char-acterization of soil water content using measured visible and near infrared spectra. Soil Science Society of America Journal, 70(4):1295-1302. doi: 10.2136/sssaj2005.0297
    [26] Music H B, Pelletier R E, 1986. Response of some thematic mapper band rations to variation in soil water content. Photogrammetric Engineering and Remote Sensing, 52(10):1661-1668
    [27] Oguntunde P G, Ajayi A E, Van De Giesen N, 2006. Tillage and surface moisture effects on bare-soil albedo of a tropical loamy sand. Soil and Tillage Research, 85(1-2):107-114. doi: 10.1016/j.still.2004.12.009
    [28] Piekarczyk J, Ka?mierowski C, Królewicz S et al., 2016. Effects of soil surface roughness on soil reflectance measured in la-boratory and outdoor conditions. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(2) 827-834. doi: 10.1109/JSTARS.2015.2450775
    [29] Potter K N, Horton R, Cruse R M, 1987. Soil surface roughness effects on radiation reflectance and soil heat flux. Soil Science Society of America Journal, 51(4):855-860. doi: 10.2136/sssaj1987.03615995005100040003x
    [30] Qi H, Paz-Kagan T, Karnieli A et al., 2018. Evaluating calibration methods for predicting soil available nutrients using hyper-spectral vnir data. Soil and Tillage Research, 175:267-275. doi: 10.1016/j.still.2017.09.006
    [31] Stevens A, Van Wesemael B, Bartholomeus H et al., 2008. Labor-atory, field and airborne spectroscopy for monitoring organic carbon content in agricultural soils. Geoderma, 144(1-2):395-404. doi: 10.1016/j.geoderma.2007.12.009
    [32] Taconet O, Ciarletti V, 2007. Estimating soil roughness indices on a ridge-and-furrow surface using stereo photogrammetry. Soil and Tillage Research, 93(1):64-76. doi: 10.1016/j.still.2006.03.018
    [33] Wight J P, Ashworth A J, Allen F L, 2016. Organic substrate, clay type, texture, and water influence on NIR carbon measurements. Geoderma, 261:36-43. doi:org/10.1016/j.geoderma. 2015.06.021
    [34] Wu W R, Geller M A, Dickinson R E, 2009. The response of soil moisture to long-term variability of precipitation. Journal of Hydrometeorology, 3(5):604-613. doi:10.1175/1525-7541 (2002)003
    [35] Zheng Xingming, Zhao Kai, Li Xiaojie, 2013. Accuracy analysis of agriculture soil surface roughness parameter. Journal of Geo-Information Science, 15(5):752-760. (in Chinese)
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  • 收稿日期:  2018-03-30
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  • 刊出日期:  2018-12-27

Predicting Surface Roughness and Moisture of Bare Soils Using Multiband Spectral Reflectance Under Field Conditions

doi: 10.1007/s11769-018-1007-x
    基金项目:  Under the auspices of the Excellent Youth Talent Project of Jilin Science and Technology Development Program (No. 20170520078JH), Science and Technology Basic Work of Science and Technology (No. 2014FY210800-4), National Natural Science Foundation of China (No. 41601382)
    通讯作者: ZHENG Xingming.E-mail:zhengxingming@iga.ac.cn

摘要: Soil surface roughness, denoted by the root mean square height (RMSH), and soil moisture (SM) are critical factors that affect the accuracy of quantitative remote sensing research due to their combined influence on spectral reflectance (SR). In regards to this issue, three SM levels and four RMSH levels were artificially designed in this study; a total of 12 plots was used, each plot had a size of 3 m×3 m. Eight spectral observations were conducted from 14 to 30 October 2017 to investigate the correlation between RMSH, SM, and SR. On this basis, 6 commonly used bands of optical satellite sensors were selected in this study, which are red (675 nm), green (555 nm), blue (485 nm), near infrared (845 nm), shortwave infrared 1 (1600 nm), and shortwave infrared 2 (2200 nm). A negative correlation was found between SR and RMSH, and between SR and SM. The bands with higher coefficient of determination R2 values were selected for stepwise multiple nonlinear regression analysis. Four characterized bands (i.e., blue, green, near infrared, and shortwave infrared 2) were chosen as the independent variables to estimate SM with R2 and root mean square error (RMSE) values equal to 0.62 and 2.6%, respectively. Similarly, the four bands (green, red, near infrared, and shortwave infrared 1) were used to estimate RMSH with R2 and RMSE values equal to 0.48 and 0.69 cm, respectively. These results indicate that the method used is not only suitable for estimating SM but can also be extended to the prediction of RMSH. Finally, the evaluation approach presented in this paper highly restores the real situation of the natural farmland surface on the one hand, and obtains high precision values of SM and RMSH on the other. The method can be further applied to the prediction of farmland SM and RMSH based on satellite and unmanned aerial vehicle (UAV) optical imagery.

English Abstract

CHEN Si, ZHAO Kai, JIANG Tao, LI Xiaofeng, ZHENG Xingming, WAN Xiangkun, ZHAO Xiaowei. Predicting Surface Roughness and Moisture of Bare Soils Using Multiband Spectral Reflectance Under Field Conditions[J]. 中国地理科学, 2018, 28(6): 986-997. doi: 10.1007/s11769-018-1007-x
引用本文: CHEN Si, ZHAO Kai, JIANG Tao, LI Xiaofeng, ZHENG Xingming, WAN Xiangkun, ZHAO Xiaowei. Predicting Surface Roughness and Moisture of Bare Soils Using Multiband Spectral Reflectance Under Field Conditions[J]. 中国地理科学, 2018, 28(6): 986-997. doi: 10.1007/s11769-018-1007-x
CHEN Si, ZHAO Kai, JIANG Tao, LI Xiaofeng, ZHENG Xingming, WAN Xiangkun, ZHAO Xiaowei. Predicting Surface Roughness and Moisture of Bare Soils Using Multiband Spectral Reflectance Under Field Conditions[J]. Chinese Geographical Science, 2018, 28(6): 986-997. doi: 10.1007/s11769-018-1007-x
Citation: CHEN Si, ZHAO Kai, JIANG Tao, LI Xiaofeng, ZHENG Xingming, WAN Xiangkun, ZHAO Xiaowei. Predicting Surface Roughness and Moisture of Bare Soils Using Multiband Spectral Reflectance Under Field Conditions[J]. Chinese Geographical Science, 2018, 28(6): 986-997. doi: 10.1007/s11769-018-1007-x
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