Parameterzing a multi-angular model of L-band microwave emissions from corn using matrix doubling algorithm

Qian ZHANG; Linna CHAI; Jiancheng SHI

doi:10.11834/jrs.20175322

Views : 0 下载量: 541 CSCD: 1

PDF
Export
Share
Collection
Album

Parameterzing a multi-angular model of L-band microwave emissions from corn using matrix doubling algorithm
Vol. 21, Issue 2, Pages: 182-192(2017)
Received：31 October 2016，

Published：2017-03
DOI： 10.11834/jrs.20175322
稿件说明：

移动端阅览

DOI：

Qian ZHANG, Linna CHAI, Jiancheng SHI. Parameterzing a multi-angular model of L-band microwave emissions from corn using matrix doubling algorithm[J]. Journal of Remote sensing, 2017, 21(2): 182-192. DOI： 10.11834/jrs.20175322.

摘要

双矩阵MD(Matrix-Doubling)算法是辐射传输方程的一种数值解，考虑了植被层内的多次散射信号，具有较高的模拟精度。但受限于算法的复杂性，很难直接应用到地表参数的反演中。本文以玉米覆盖地表为研究对象，基于MD算法的模拟数据，发展了一种L波段多角度微波辐射参数化模型。基于模拟数据的对比结果表明，参数化模型具有与理论模型相当的精度，两者之间的发射率误差不超过0.004(V极化)和0.007(H极化)。同时，结合黑河流域的地面实测数据，利用本论文中发展的参数化模型模拟了纯玉米像元的辐射亮温。该模拟结果与相同像元尺度上机载L波段微波辐射计(PLMR)观测亮温之间的差异基本在10 K以内。

Abstract

Matrix Doubling (MD) algorithm is an analytical solution for radiation transfer equation. MD presents relative accuracy in simulating the vegetation canopy microwave signals because the algorithm considers the multi-scattering effects. However

MD is difficult to apply in retrieving work because of its complexity. In this regard

the development of a microwave vegetation emission model

which not only takes a simple form but also considers volume scattering

is of considerable interest.In this study

we present a parameterized multi-angular model of L-band microwave emission from corn. The model is derived using the MD algorithm. First

we validated the theoretical MD algorithm with in situ data obtained from the field experiment conducted in Huailai. A good agreement with experimental data lays a solid foundation for parameterization work. To start

we simulated the corn terrain emissions in both V and H polarization at 1.4 GHz by using the theoretical MD algorithm. The incident angles were set from 5°to 59° with 2° intervals in each band. The simulations covered a broad range of corn canopy and soil dielectric. Finally

the parameterized model related the total emissivity to the optical depth of the corn and the emissivity of ground. Moreover

this model provides a series of coefficients at different incident angles. The functions between the coefficient and incident angles were provided to determine the form of the model when the angle was set.The differences between the parameterized model and the theoretical model are minimal and have RMSE in the 10

–3

range of the L-band. We also validated the parameterized model using airborne PLMR observations. The simulation error in three nonconsecutive days (June 7

June 26 and August 2) is within 10000 overall. This finding shows that the parameterization model is successful in the simulation of L-band microwave emissions from corn.The parameterization model not only presents a simple form but also exhibits high accuracy by including volume scattering. Moreover

unlike other semi-empirical models

the input of the parameterization model possesses physical meaning and is not based on prior knowledge. Therefore

the parameterization model is of significant importance in retrieval work. Meanwhile

the capability to simulate corn emission from the L-band at a broad angle range improves the possibility for model applications to data from satellite. However

owing to the coarse resolution of passive microwave remote sensing

the parameterization model requires further improvement to achieve universal applicability particularly in highly heterogeneous land surfaces.

关键词

Keywords

references

Bracaglia M, Ferrazzoli P and Guerriero L. 1995. A fully polarimetric multiple scattering model for crops. Remote Sensing of Environment, 54(3): 170–179

Chauhan N S, Le Vine D M and Lang R H. 1994. Discrete scatter model for microwave radar and radiometer response to corn: comparison of theory and data. IEEE Transactions on Geoscience and Remote Sensing, 32(2): 416–426

Chen K S, Wu T D, Tsang L, Li Q, Shi J C and Fung A K. 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

Della Vecchia A, Ferrazzoli P, Wigneron J P and Grant J P. 2007. Modeling forest emissivity at L-band and a comparison with multitemporal measurements. IEEE Geoscience and Remote Sensing Letters, 4(4): 508–512

Dobson M C, Ulaby F T, Hallikainen M T and El-Rayes M A. 1985. Microwave dielectric behavior of wet soil-part Ⅱ: dielectric mixing models. IEEE Transactions on GE Geoscience and Remote Sensing, GE-23(1): 35–46

Ferrazzoli P and Guerriero L. 1996. Passive microwave remote sensing of forests: a model investigation. IEEE Transactions on Geoscience and Remote Sensing, 34(2): 433–443

Ferrazzoli P, Guerriero L and Wigneron J P. 2002. Simulating L-band emission of forests in view of future satellite applications. IEEE Transactions on Geoscience and Remote Sensing, 40(12): 2700–2708

Fung A K. 1994. Microwave Scattering and Emission Models and Their Applications. Norwood, MA: Artech House

Jackson T J and Schmugge T J. 1991. Vegetation effects on the microwave emission of soils. Remote Sensing of Environment, 36(3): 203–212

Jiang L M, Shi J C, Tjuatja S, Dozier J, Chen K S and Zhang L X. 2007. A parameterized multiple-scattering model for microwave emission from dry snow. Remote Sensing of Environment, 111(2/3): 357–366

Jin R, Li X, Yan B P, Li X H, Luo W M, Ma M G, Guo J W, Kang J, Zhu Z L and Zhao S J. 2014. A nested ecohydrological wireless sensor network for capturing the surface heterogeneity in the midstream areas of the Heihe River basin, China. IEEE Geoscience and Remote Sensing Letters, 11(11): 2015–2019

Karam M A and Fung A K. 1988. Electromagnetic scattering from a layer of finite length, randomly oriented, dielectric, circular cylinders over a rough interface with application to vegetation. International Journal of Remote Sensing, 9(6): 1109–1134

Karam M A, Fung A K and Antar Y M M. 1988. Electromagnetic wave scattering from some vegetation samples. IEEE Transactions on Geoscience and Remote Sensing, 26(6): 799–808

Kurum M, Lang R H, O’Neill P E, Joseph A T, Jackson T J and Cosh M H. 2011. A first-order radiative transfer model for microwave radiometry of forest canopies at L-band. IEEE Transactions on Geoscience and Remote Sensing, 49(9): 3167–3179

Kurum M, O’Neill P E, Lang R H, Joseph A T, Cosh M H and Jackson T J. 2012. Effective tree scattering and opacity at L-band. Remote Sensing of Environment, 118: 1–9

Lang R H, Utku C, O’Neill P E and Tsegaye T D. 2004. Role of albedo in sensing soil moisture under vegetation with passive L-band algorithms // Proceedings of the 2004 IEEE International Geoscience and Remote Sensing Symposium. Anchorage, AK: IEEE: 343

Li X, Cheng G D, Liu S M, Xiao Q, Ma M G, Jin R, Che T, Liu Q H, Wang W Z, Qi Y, Wen J G, Li H Y, Zhu G F, Guo J W, Ran Y H, Wang S G, Zhu Z L, Zhou J, Hu X L and Xu Z W. 2013. Heihe watershed allied telemetry experimental research (HiWATER): Scientific objectives and experimental design. Bulletin of the American Meteorological Society, 94(8): 1145–1160

Monsivais-Huertero A and Judge J. 2011. Comparison of backscattering models at L-band for growing corn. IEEE Geoscience and Remote Sensing Letters, 8(1): 24–28

中华人民共和国国家统计局(NBSC). 2007. 中国统计年鉴. 北京: 中国统计出版社

National Bureau of Statistics of China. 2007. China Statistical Yearbook. Beijing: China Statistics Press

Ulaby F T, Moore R K and Fung A K. 1986. Microwave Remote Sensing: Active and Passive. Vol. Ⅲ: From Theory to Applications. Norwood, MA: Artech House.

Vecchia A D, Ferrazzoli P, Guerriero L, Blaes X, Defourny P, Dente L, Mattia F, Satalino G, Strozzi T and Wegmuller U. 2006. Influence of geometrical factors on crop backscattering at C-band. IEEE Transactions on Geoscience and Remote Sensing, 44(4): 778–790

Yan S, Jiang L M, Chai L N, Yang J T and Kou X K. 2015. Calibration of the L-MEB model for croplands in HiWATER using PLMR observation. Remote Sensing, 7(8): 10878–10897

Zhang Z J, Yu X C, Zhao S J and Chai L N. 2014. Assessment and analysis of microwave emissivity and transmissivity of a deciduous forest towards the estimate of vegetation biomass. Science China Earth Sciences, 57(3): 534–541

Zhong B, Ma P, Nie A H, Yang A X, Yao Y J, Lü W B, Zhang H and Liu Q H. 2014. Land cover mapping using time series HJ-1/CCD Data. Science China Earth Sciences, 57(8): 1790–1799

Alert me when the article has been cited

提交

Methods， progresses， and challenges of passive microwave soil moisture spatial downscaling

Indicative features for identifying corn and soybean using remote sensing imagery at middle and later growth season

Downscaling method for near-surface freeze/thaw state monitoring in Genhe area of China

A Wide-Area LuTan-1 Data Processing Method Considering Image Overlap Rate： A Case Study of Landslide Monitoring in the Chongqing Section of the Three Gorges Reservoir Area

An experimental study on the depth of microwave radiation sensitivity of black soil under frozen condition