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Advances in Meteorology Volume 2018 ,2018-08-15
Combined Use of GF-3 and Landsat-8 Satellite Data for Soil Moisture Retrieval over Agricultural Areas Using Artificial Neural Network
Research Article
Qingyan Meng 1 , 2 Linlin Zhang 1 , 2 , 3 Qiuxia Xie 1 , 2 , 3 Shun Yao 4 Xu Chen 1 , 2 , 3 Ying Zhang 1 , 2 , 3
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DOI:10.1155/2018/9315132
Received 2018-02-22, accepted for publication 2018-07-12, Published 2018-07-12
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摘要

Soil moisture is the basic condition required for crop growth and development. Gaofen-3 (GF-3) is the first C-band synthetic-aperture radar (SAR) satellite of China, offering broad land and ocean imaging applications, including soil moisture monitoring. This study developed an approach to estimate soil moisture in agricultural areas from GF-3 data. An inversion technique based on an artificial neural network (ANN) is introduced. The neural network was trained and tested on a training sample dataset generated from the Advanced Integral Equation Model. Incidence angle and HH or VV polarization data were used as input variables of the ANN, with soil moisture content (SMC) and surface roughness as the output variables. The backscattering contribution from the vegetation was eliminated using the water cloud model (WCM). The acquired soil backscattering coefficients of GF-3 and in situ measurement data were used to validate the SMC estimation algorithm, which achieved satisfactory results (R2 = 0.736; RMSE = 0.042). These results highlight the contribution of the combined use of the GF-3 synthetic-aperture radar and Landsat-8 images based on an ANN method for improving SMC estimates and supporting hydrological studies.

授权许可

Copyright © 2018 Qingyan Meng et al. 2018
This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

通讯作者

Linlin Zhang.Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China, cas.cn;Sanya Institute of Remote Sensing, Sanya 572029, China;University of Chinese Academy of Sciences, Beijing 100101, China, ucas.ac.cn.zhangll@radi.ac.cn

推荐引用方式

Qingyan Meng,Linlin Zhang,Qiuxia Xie,Shun Yao,Xu Chen,Ying Zhang. Combined Use of GF-3 and Landsat-8 Satellite Data for Soil Moisture Retrieval over Agricultural Areas Using Artificial Neural Network. Advances in Meteorology ,Vol.2018(2018)

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参考文献
[1] T.J. Jackson, D. Chen, M. Cosh. (2004). Vegetation water content mapping using Landsat data derived normalized difference water index for corn and soybeans. Remote Sensing of Environment.92(4):475-482. DOI: 10.1109/tgrs.2008.2004711.
[2] N. Baghdadi, N. Holah, M. Zribi. (2006). Soil moisture estimation using multi-incidence and multi-polarization ASAR data. International Journal of Remote Sensing.27(10):1907-1920. DOI: 10.1109/tgrs.2008.2004711.
[3] M. Zribi, M. Dechambre. (2003). A new empirical model to retrieve soil moisture and roughness from C-band radar data. Remote Sensing of Environment.84(1):42-52. DOI: 10.1109/tgrs.2008.2004711.
[4] C. Wang, J. Qi, S. Moran, R. Marsett. et al.(2004). Soil moisture estimation in a semiarid rangeland using ERS-2 and TM imagery. Remote Sensing of Environment.90(2):178-189. DOI: 10.1109/tgrs.2008.2004711.
[5] P. Guo, J. Shi, B. Gao, H. Wan. et al.Evaluation of errors induced by soil dielectric models for soil moisture retrieval at L-band. :1679-1682. DOI: 10.1109/tgrs.2008.2004711.
[6] A. Mialon, P. Richaume, D. Leroux. (2015). Comparison of Dobson and Mironov dielectric models in the SMOS soil moisture retrieval algorithm. IEEE Transactions on Geoscience and Remote Sensing.53(6):3084-3094. DOI: 10.1109/tgrs.2008.2004711.
[7] C. Xing, N. Chen, X. Zhang, J. Gong. et al.(2017). A machine learning based reconstruction method for satellite remote sensing of soil moisture images with in situ observations. Remote Sensing.9(5):484. DOI: 10.1109/tgrs.2008.2004711.
[8] M. T. Yilmaz, E. R. Hunt, T. J. Jackson. (2008). Remote sensing of vegetation water content from equivalent water thickness using satellite imagery. Remote Sensing of Environment.112(5):2514-2522. DOI: 10.1109/tgrs.2008.2004711.
[9] M. S. Dawson, A. K. Fung, M. T Manry. (1997). A robust statistical-based estimator for soil moisture retrieval from radar measurements. IEEE Transactions on Geoscience and Remote Sensing.35(1):57-67. DOI: 10.1109/tgrs.2008.2004711.
[10] L. Serrano, S. L. Ustin, D. A. Roberts, J. A. Gamon. et al.(2000). Deriving water content of chaparral vegetation from AVIRIS data. Remote Sensing of Environment.74(3):570-581. DOI: 10.1109/tgrs.2008.2004711.
[11] M. C. Dobson, F. T. Ulaby, M. T. Hallikainen, M. A. El-Rayes. et al.(1985). Microwave dielectric behavior of wet soil-part II: dielectric mixing models. IEEE Transactions on Geoscience and Remote Sensing.23(1):35-46. DOI: 10.1109/tgrs.2008.2004711.
[12] W. Dorigo, W. Wagner, R. Hohensinn. (2011). The International Soil Moisture Network: a data hosting facility for global in situ soil moisture measurements. Hydrology and Earth System Sciences.15(5):1675-1698. DOI: 10.1109/tgrs.2008.2004711.
[13] X. Wei, X. Gu, Q. Meng. (2017). Leaf area index estimation using Chinese GF-1 wide field view data in an agriculture region. Sensors.17(7):1593. DOI: 10.1109/tgrs.2008.2004711.
[14] B.-C. Gao. (1996). NDWI—a normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment.58(3):257-266. DOI: 10.1109/tgrs.2008.2004711.
[15] C. Notarnicola, M. Angiulli, F. Posa. (2006). Use of radar and optical remotely sensed data for soil moisture retrieval over vegetated areas. IEEE Transactions on Geoscience and Remote Sensing.44(4):925-935. DOI: 10.1109/tgrs.2008.2004711.
[16] H. Xu, T. Cheng, X. Gu, T. Yu. et al.(2014). New Asia dust storm detection method based on the thermal infrared spectral signature. Remote Sensing.7(1):51-71. DOI: 10.1109/tgrs.2008.2004711.
[17] H. Xu, T. Cheng, D. Xie, J. Li. et al.(2014). Dust identification over arid and semiarid regions of Asia using AIRS thermal infrared channels. Advances in Meteorology.2014-16. DOI: 10.1109/tgrs.2008.2004711.
[18] H. Xu, T. Yu, T.-H. Cheng, Q. Liu. et al.(2013). The research on remote sensing dust aerosol by using split window emissivity. Spectroscopy and Spectral Analysis.33(5):1189-1193. DOI: 10.1109/tgrs.2008.2004711.
[19] L. Toan. Active microwave signatures of soil and crops-significant results of three years of experiments. . DOI: 10.1109/tgrs.2008.2004711.
[20] S. Paloscia, S. Pettinato, E. Santi, C. Notarnicola. et al.(2013). Soil moisture mapping using Sentinel-1 images: algorithm and preliminary validation. Remote Sensing of Environment.134:234-248. DOI: 10.1109/tgrs.2008.2004711.
[21] F. T. Ulaby, P. P. Batlivala, M. C. Dobson. (1978). Microwave backscatter dependence on surface roughness, soil moisture, and soil texture: part I-bare soil. IEEE Transactions on Geoscience Electronics.16(4):286-295. DOI: 10.1109/tgrs.2008.2004711.
[22] M. Sadeghi, E. Babaeian, M. Tuller, S. B Jones. et al.(2017). The optical trapezoid model: a novel approach to remote sensing of soil moisture applied to Sentinel-2 And Landsat-8 observations. Remote Sensing of Environment.198:52-68. DOI: 10.1109/tgrs.2008.2004711.
[23] E. Santi, S. Paloscia, S. Pettinato, G Fontanelli. et al.(2016). Application of artificial neural networks for the soil moisture retrieval from active and passive microwave spaceborne sensors. International Journal of Applied Earth Observation and Geoinformation.48:61-73. DOI: 10.1109/tgrs.2008.2004711.
[24] M. El Hajj, N. Baghdadi, M. Zribi, H. Bazzi. et al.(2017). Synergic use of Sentinel-1 and Sentinel-2 images for operational soil moisture mapping at high spatial resolution over agricultural areas. Remote Sensing.9(12):1292. DOI: 10.1109/tgrs.2008.2004711.
[25] N. Baghdadi, S. Gaultier, C. King. (2002). Retrieving surface roughness and soil moisture from synthetic aperture radar (SAR) data using neural networks. Canadian Journal of Remote Sensing.28(5):701-711. DOI: 10.1109/tgrs.2008.2004711.
[26] H. Xu, T. Cheng, X. Gu, T. Yu. et al.(2015). Spatiotemporal variability in dust observed over the Sinkiang and Inner Mongolia regions of Northern China. Atmospheric Pollution Research.6(4):562-571. DOI: 10.1109/tgrs.2008.2004711.
[27] C. Mattar, J.-P. Wigneron, J. A. Sobrino. (2012). A combined optical–microwave method to retrieve soil moisture over vegetated areas. IEEE Transactions on Geoscience and Remote Sensing.50(5):1404-1413. DOI: 10.1109/tgrs.2008.2004711.
[28] N. Pierdicca, L. Pulvirenti, C. Bignami. (2010). Soil moisture estimation over vegetated terrains using multitemporal remote sensing data. Remote Sensing of Environment.114(2):440-448. DOI: 10.1109/tgrs.2008.2004711.
[29] M. Hosseini, M. Saradjian. (2011). Soil moisture estimation based on integration of optical and SAR images. Canadian Journal of Remote Sensing.37(1):112-121. DOI: 10.1109/tgrs.2008.2004711.
[30] M. Zribi, A. Gorrab, N. Baghdadi, Z. Lili-Chabaane. et al.(2014). Influence of radar frequency on the relationship between bare surface soil moisture vertical profile and radar backscatter. IEEE Geoscience and Remote Sensing Letters.11(4):848-852. DOI: 10.1109/tgrs.2008.2004711.
[31] E. Santi, S. Paloscia, S. Pettinato, C. Notarnicola. et al.(2013). Comparison between SAR soil moisture estimates and hydrological model simulations over the Scrivia test site. Remote Sensing.5(10):4961-4976. DOI: 10.1109/tgrs.2008.2004711.
[32] C. Pathe, W. Wagner, D. Sabel, M. Doubkova. et al.(2009). Using ENVISAT ASAR global mode data for surface soil moisture retrieval over Oklahoma, USA. IEEE Transactions on Geoscience and Remote Sensing.47(2):468-480. DOI: 10.1109/tgrs.2008.2004711.
[33] M. S. Moran, D. C. Hymer, J. Qi, E. E Sano. et al.(2000). Soil moisture evaluation using multi-temporal synthetic aperture radar (SAR) in semiarid rangeland. Agricultural and Forest meteorology.105(1–3):69-80. DOI: 10.1109/tgrs.2008.2004711.
[34] P. C. Dubois, J. Van Zyl, T. Engman. (1995). Measuring soil moisture with imaging radars. IEEE Transactions on Geoscience and Remote Sensing.33(4):915-926. DOI: 10.1109/tgrs.2008.2004711.
[35] R. Bindlish, A. P. Barros. (2001). Parameterization of vegetation backscatter in radar-based, soil moisture estimation. Remote Sensing of Environment.76(1):130-137. DOI: 10.1109/tgrs.2008.2004711.
[36] J. Zeng, K.-S. Chen, H. Bi, T. Zhao. et al.(2017). A comprehensive analysis of rough soil surface scattering and emission predicted by AIEM with comparison to numerical simulations and experimental measurements. IEEE Transactions on Geoscience and Remote Sensing.55(3):1696-1708. DOI: 10.1109/tgrs.2008.2004711.
[37] E. Attema, F. T Ulaby. (1978). Vegetation modeled as a water cloud. Radio Science.13(2):357-364. DOI: 10.1109/tgrs.2008.2004711.
[38] N. Pierdicca, L. Pulvirenti, F. Ticconi, M. Brogioni. et al.(2008). Radar bistatic configurations for soil moisture retrieval: a simulation study. IEEE Transactions on Geoscience and Remote Sensing.46(10):3252-3264. DOI: 10.1109/tgrs.2008.2004711.
[39] G. Satalino, F. Mattia, M. W. Davidson, T. Le Toan. et al.(2002). On current limits of soil moisture retrieval from ERS-SAR data. IEEE Transactions on Geoscience and Remote Sensing.40(11):2438-2447. DOI: 10.1109/tgrs.2008.2004711.
[40] P. Yao, J. Shi, T. Zhao, H. Lu. et al.(2017). Rebuilding long time series global soil moisture products using the neural network adopting the microwave vegetation index. Remote Sensing.9(1):35. DOI: 10.1109/tgrs.2008.2004711.
[41] M. B. Kia, S. Pirasteh, B. Pradhan, A. R. Mahmud. et al.(2012). An artificial neural network model for flood simulation using GIS: Johor River Basin, Malaysia. Environmental Earth Sciences.67(1):251-264. DOI: 10.1109/tgrs.2008.2004711.
[42] Q. Chen, J. Zeng, C. Cui. (2018). Soil moisture retrieval from SMAP: a validation and error analysis study using ground-based observations over the Little Washita watershed. IEEE Transactions on Geoscience and Remote Sensing.56(3):1394-1408. DOI: 10.1109/tgrs.2008.2004711.
[43] D.-H. Chang, S. Islam. (2000). Estimation of soil physical properties using remote sensing and artificial neural network. Remote Sensing of Environment.74(3):534-544. DOI: 10.1109/tgrs.2008.2004711.
[44] S.-S. Chai, B. Veenendaal, G. West, J. P. Walker. et al.(2008). Backpropagation neural network for soil moisture retrieval using NAFE’05 data: a comparison of different training algorithms. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences.37:1345. DOI: 10.1109/tgrs.2008.2004711.
[45] X. Bai, B. He, X. Li. (2017). First assessment of Sentinel-1A data for surface soil moisture estimations using a coupled water cloud model and advanced integral equation model over the Tibetan Plateau. Remote Sensing.9(7):714. DOI: 10.1109/tgrs.2008.2004711.
[46] H. Lievens, N. E. Verhoest. (2011). On the retrieval of soil moisture in wheat fields from L-band SAR based on water cloud modeling, the IEM, and effective roughness parameters. IEEE Geoscience and remote sensing Letters.8(4):740-744. DOI: 10.1109/tgrs.2008.2004711.
[47] K.-S. Chen, T.-D. Wu, L. Tsang, Q. Li. et al.(2003). Emission of rough surfaces calculated by the integral equation method with comparison to three-dimensional moment method simulations. IEEE Transactions on Geoscience and Remote Sensing.41(1):90-101. DOI: 10.1109/tgrs.2008.2004711.
[48] B. He, M. Xing, X. Bai. (2014). A synergistic methodology for soil moisture estimation in an alpine prairie using radar and optical satellite data. Remote Sensing.6(11):10966-10985. DOI: 10.1109/tgrs.2008.2004711.
[49] Y. Oh, K. Sarabandi, F. T. Ulaby. (1992). An empirical model and an inversion technique for radar scattering from bare soil surfaces. IEEE transactions on Geoscience and Remote Sensing.30(2):370-381. DOI: 10.1109/tgrs.2008.2004711.
[50] J. Shi, J. Wang, A. Y. Hsu, P. E. O'Neill. et al.(1997). Estimation of bare surface soil moisture and surface roughness parameter using L-band SAR image data. IEEE Transactions on Geoscience and Remote Sensing.35:1254-1266. DOI: 10.1109/tgrs.2008.2004711.
[51] Y. Oh. (2004). Quantitative retrieval of soil moisture content and surface roughness from multipolarized radar observations of bare soil surfaces. IEEE Transactions on Geoscience and Remote Sensing.42(3):596-601. DOI: 10.1109/tgrs.2008.2004711.
[52] N. Baghdadi, R. Cresson, M. El Hajj, R. Ludwig. et al.(2012). Estimation of soil parameters over bare agriculture areas from C-band polarimetric SAR data using neural networks. Hydrology and Earth System Sciences.16(6):1607-1621. DOI: 10.1109/tgrs.2008.2004711.
[53] Y. Oh, K. Sarabandi, F. T. Ulaby. (2002). Semi-empirical model of the ensemble-averaged differential Mueller matrix for microwave backscattering from bare soil surfaces. IEEE Transactions on Geoscience and Remote Sensing.40(6):1348-1355. DOI: 10.1109/tgrs.2008.2004711.
[54] N. Baghdadi, M. El Hajj, M. Zribi, S. Bousbih. et al.(2017). Calibration of the water cloud model at C-band for winter crop fields and grasslands. Remote Sensing.9(9):969. DOI: 10.1109/tgrs.2008.2004711.
[55] A. Joseph, R. van der Velde, P. O'neill, R. Lang. et al.(2010). Effects of corn on C- and L-band radar backscatter: a correction method for soil moisture retrieval. Remote Sensing of Environment.114(11):2417-2430. DOI: 10.1109/tgrs.2008.2004711.
[56] D. D. Alexakis, F.-D. K. Mexis, A.-E. K. Vozinaki, I. N. Daliakopoulos. et al.(2017). Soil moisture content estimation based on Sentinel-1 and auxiliary earth observation products. A hydrological approach. Sensors.17(6):1455. DOI: 10.1109/tgrs.2008.2004711.
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