Active and passive cooperative algorithm at L-Band for bare soil moisture inversion

Ma Hongzhang; Liu Sumei; Peng Aihua; Sun Lin; Sun Genyun

doi:10.11975/j.issn.1002-6819.2016.19.019

Volume 32 Issue 19

Sep. 2016

Turn off MathJax

Article Contents

Abstract

References

Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE) > 2016 > 32(19): 133-138. > DOI: 10.11975/j.issn.1002-6819.2016.19.019

Ma Hongzhang, Liu Sumei, Peng Aihua, Sun Lin, Sun Genyun. Active and passive cooperative algorithm at L-Band for bare soil moisture inversion[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2016, 32(19): 133-138. DOI: 10.11975/j.issn.1002-6819.2016.19.019

Citation:

PDF (2331 KB)

Active and passive cooperative algorithm at L-Band for bare soil moisture inversion

1.
College of Science, China University of Petroleum, Qingdao 266580, China
2.
College of Geomatics, Shandong University of Science and Technology, Qingdao 266590, China
3.
School of Geosciences, China University of Petroleum, Qingdao 266580, China

More Information

Received Date: November 16, 2015
Revised Date: June 20, 2016
Published Date: September 30, 2016

Graphical Abstract

Abstract

Abstract

Abstract: Soil moisture (SM) is an important parameter for drought monitoring as well as soil erosion, crop production and surface temperature. Microwave remote sensing especially at L-band is one of the most promising approaches to monitor the variable of SM at the global scale with frequent revisiting times. We carried out a large number of experimental and theoretical studies to assess the potential of monitoring soil moisture by means of both active and passive microwave satellite observations. Improving the inversion accuracy of soil moisture by combining active and passive microwave remote sensing data is an important task for the development of quantitative remote sensing. In this paper, the main objective was to estimate soil volumetric water content (SVWC) of bare soil collaborative using the active and passive observations. A novel approach, which was based on the simulation database of the advanced integral equation model (AIEM), was presented. In the method, we took the advantages of the active and passive microwave remote sensing data to obtain SVWC. Based on the analysis of the simulation database, some significant patterns had been found that the characteristics of the vertical polarization microwave emissivity (EV) had stronger sensitivity to the soil moisture than the roughness while the active microwave backscatter coefficient was more sensitive to the soil roughness than passive microwave. Among the vertical polarization microwave emissivity (EV), horizontal polarization microwave emissivity (EH) and VV backscatter coefficient (σvv), the effect of roughness on the σvv was greater than on passive microwave surface emissivity (EV and EH) and the effect of roughness on EV was minimized. The EV and the σvv were used to estimate the soil surface roughness, and on the premise of knowing soil surface roughness, soil moisture can be accurately calculated by passive microwave data. This algorithm had following two steps, the first step was to estimate the soil surface roughness using the vertical polarization emissivity and VV polarization backscatter coefficient; and the second step was to estimate SVWC using a combination of the EV and EH at L band under the condition of soil roughness was known. At last, based on the dataset of the SMAP experimental campaigns carried out in 2012 (SMAPVEX12), we evaluated the superiority of the algorithm. The validation data were obtained through the online Data Pool at the National Snow and Ice Data Center of the SMAPVEX12, the good inversion results were achieved with R2 equal to 0.6637 and the RMSE equal to 0.0607 cm3/cm3. One of the biggest advantages of the algorithm developed in this paper was that the coefficient of algorithm was calculated by the simulated data set, which different from the conventional experience algorithm that depended on the field data, which reduced greatly the limitations of the algorithm in practical application. The threshold value 0.25 of NDVI was used to select the appropriate sampling points for validation and the error of the result partly came from the effect of the little vegetation covering the surface, so the algorithm proposed in this paper should be revalidated using the observed data of the bare soil in the future.
- soil moisture,
- remote sensing,
- algorithms,
- models,
- active and passive microwave,
- bare soil,
- surface roughness

FullText(HTML)

References (13)

References

[1]	e Lannoy G J M, Reichle R H, Pauwels V. Global calibration of the GEOS-5 L-Band microwave radiative transfer model over nonfrozen land using SMOS observations[J]. Journal of Hydrometeorology, 2013, 14(3): 765－785.
[2]	Johnson J T, Tsang L, Shin R T, et al. Backscattering enhancement of electromagnetic waves from two-dimensional perfectly conducting random rough surfaces: A comparison of Monte Carlo simulations with experimental data[J]. IEEE Transactions on Antennas and Propagation, 1993, 44(5): 667－678.
[3]	Kerr Y H, Waldteufel P, Wigneron J P, et al. Soil moisture retrieval from space: The soil moisture and ocean salinity (SMOS) mission[J]. IEEE Transactions on Geoscience and Remote Sensing, 2001, 39(8): 1729－1735.
[4]	Njoku E G, Chan S K. Vegetation and surface roughness effects on AMSR-E land observations[J]. Remote Sensing of Environment, 2006, 100(2): 190－199.
[5]	Entekhabi D, Njoku E G, Neill P E O, et al. The soil moisture active passive (SMAP) mission[J]. Proceedings of the IEEE, 2010, 98(5): 704－716.
[6]	Oh Y, Sarabandi K, Ulaby F T. An empirical model and an inversion technique for radar scattering from bare soil surfaces[J]. IEEE Transactions on Geoscience and Remote Sensing, 1992, 30(2): 370－381.
[7]	Zribi M, André C, Decharme B. A method for soil moisture estimation in Western Africa based on ERS scatter meter[J]. IEEE Transactions on Geoscience and Remote Sensing, 2008, 46(2): 438－448.
[8]	Fung A K, Li Z Q, Chen K S. Backscattering from a randomly rough dielectric surface[J]. IEEE Transactions on Geoscience and Remote Sensing, 1992, 30(2): 356－369.
[9]	Chen K S, Wu T D, Tsang L, et al. Emission of rough surfaces calculated by the integral equation method with comparison to three-dimensional moment method simulations[J]. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41(1): 90－101.
[10]	Njoku E G, Wilson W J, Yueh S, et al. Observations of soil moisture using a passive and active low-frequency microwave airborne sensor during SGP99[J]. IEEE Transactions on Geoscience and Remote Sensing, 2002, 40(12): 2659-2673.[11] Njoku E G, Jackson T J, Lakshmi V. et al. Soil moisture re-trieval from AMSR-E[J]. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41(2): 215－229.
[11]	Ulaby F T, Moore R K, Fung A K. Microwave remote sensing active and passive[M]. Norwood: Artech House, Inc., 1986.
[12]	http://nsidc.org/data/smap/validation/val-data.html.
[13]	McNairn H, Jackson T J, Wiseman G, et al. The soil moisture active passive validation experiment 2012 (SMAPVEX12): prelaunch calibration and validation of the SMAP soil moisture algorithms[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(6): 3312－3324.

[1]	Wang Shutao, Xu Ce, Li Ziliang, Chen Yaheng. Spatial variability of cultivated land productivity in county territory[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2013, 29(17): 230-239. DOI: 10.3969/j.issn.1002-6819.2013.17.030
[2]	Xiang Hui, Kong Xiangbin,, Wu Zhaokun, Shi Jingran, Zhang Qingpu. Spatial distribution characteristics of potential productivity of arable land in main crop production area in China[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2012, 28(24): 235-244.
[3]	Li Jianping, Shangguan Zhouping. Spatial-temporal distribution of cultivated land production capacity in Shaanxi province[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2012, 28(10): 239-246.
[4]	Li Ziliang, Wang Shutao, Zhang Li, Men Mingxin, Xu Hao. Spatial pattern of cultivated land productivity in rapid economic development region[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2010, 26(11): 323-331.
[5]	Men Mingxin, Chen Yaheng, Liu Yu, Wei Liang, Xu Hao. Quantitative assessment of urban expansion impact on comprehensive productivity of cultivated land in Tangshan city based on RS and GIS[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2009, 25(9): 282-288.
[6]	Wu Kening, Cheng Xianjun, Huang Qin, Zhao Huafu, Tang Huaizhi. Comprehensive productivity of agricultural land based on the agricultural land classification[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2008, 24(11): 51-56.
[7]	Li Cuizhen, Kong Xiangbin, Qin Jing, Li Jianchun, Ma Song. Cultivated land use of peasant households in metropolitan suburbs and its effect on grain production capacity[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2008, 24(1): 101-107.
[8]	Yang Pingguo, Mao Renzhao, Zhao Jianlin, Zhang Lianmin. Analysis of regional grain comprehensive productive capacity and food security—A case study of Shijiazhuang City[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2006, 22(14): 279-282.
[9]	Shen Genxiang, Huang Lihua, Qian Xiaoyong, Xu Jiele, Wu Jian, Tang Hao. Comparative research on protein production capacity and freeze resistance capability of duckweed in nitrogen-phosphorus wastewater purification[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2006, 22(6): 144-147.
[10]	Zhang Junxia, Zhu Wennong. Study on the Method to Calculate New Double Tooth-Roller Crusher[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 1999, 15(4): 116-120.