برآورد رطوبت حجمی خاک از تداخل‌سنجی سنجش بازتاب سیستم‌های ماهواره‌ای ناوبری جهانی و تحلیل سری زمانی حاصل با شبکه‌های عصبی مصنوعی حافظه طولانی کوتاه‌مدت

نوع مقاله : مقاله پژوهشی

نویسندگان

گروه نقشه برداری، دانشکده مهندسی عمران، دانشگاه تبریز، تبریز، ایران.

چکیده

تداخل‌سنجی سنجش بازتاب سیستم‌های ماهواره‌ای ناوبری جهانی (GNSS-IR) را می‌توان به‌عنوان یکی دیگر از روش‌های سنجش از دور برای پایش رطوبت خاک به‌صورت پیوسته و البته در مقیاس محلی در نظر گرفت که در وضعیت‌های مختلف جوی مانند شرایط بارانی و مه‌آلود و در شرایط متفاوت نور و روشنایی مانند روز و شب قابل اجرا است. سیگنال‌های بازتابی از سطح زمین توسط آنتن‌های GNSS  قابل دریافت است. تغییرات در رطوبت خاک باعث تغییر در مقدار مؤلفه نسبت سیگنال به نویز  SNRسیگنال‌های بازتابی می‌شود. با تجزیه‌ و تحلیل سیگنال‌های بازتابی، می‌توان به اطلاعات مفیدی در مورد سطح بازتاب دست یافت. SNR به شدت به رطوبت خاک وابسته است. در این تحقیق داده‌های ایستگاه P038 در منطقه نیومکزیکو مورد استفاده قرار می‌گیرد. بدین‌صورت که از سیگنال‌های چندمسیری برای برآورد تغییرات رطوبت خاک در طول چهار سال، از 2017 تا 2020 استفاده می‌شود. طبق برآورد انجام شده سطح محتوای حجمی آب در سال 2017، برابر 88/8 درصد می‌باشد، که در سال 2018 به 74/11 درصد افزایش می‌یابد. سپس اندکی کاهش یافته و در سال 2019 به 88/10 درصد رسیده و نهایتاً در سال 2020 به 49/12 درصد افزایش می‌یابد. در این مقاله کارایی شبکه‌های عصبی حافظه طولانی کوتاه‌مدت (LSTM) در پیش‌بینی سری زمانی رطوبت حجمی خاک به‌دست آمده از تداخل سیگنال‌های بازتابی GNSS مورد ارزیابی قرار می‌گیرد. آموزش مدل با استفاده از 80 درصد مشاهدات ایستگاه انجام می‌گیرد. با به‌روزرسانی وضعیت شبکه با مقادیر مشاهده شده به جای مقادیر پیش‌بینی‌شده، مقدار جذر خطای مربعی میانگین از 09/0 به 04/0 کاهش یافته و پیش‌بینی‌ها دقیق‌تر انجام می‌شوند.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Estimation of volumetric soil moisture from GNSS-IR and analysis of the resulting time series with LSTM artificial neural networks

نویسندگان [English]

  • Asghar Rastbood
  • Patricia Danghian
Department of Surveying, Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran.
چکیده [English]

One of the ways for measuring environmental parameters is using GNSS (Global Navigation Satellite System) reflected signals from the Earth surface that are received by GNSS antennas. Environmental parameters include soil moisture, seasonal snow accumulation, ice thickness, vegetation cover and water level changes in dams, lakes and seas (tide). The focus of this research is on soil moisture.
Reflection of GNSS signals from a surface is called multipath. When the goal is positioning, multipath is one of the most significant sources of error in GNSS observations. But, by analyzing those reflected signals, we will get useful information about the reflection surface. This technique is called GNSS interferometric reflectometry (GNSS-IR). By this definition, GNSS-IR can be considered as a remote sensing technique for continuous and local monitoring of environmental parameters which can be performed in various weather conditions such as rainy and cloudy conditions, as well as different lighting conditions such as day and night.
Signal-to-Noise Ratio (SNR) is a measure of the strength of a signal relative to the background noise level. In GNSS, SNR is used to evaluate the quality of the received signal. It is calculated as the ratio of the power of the received signal to the power of the noise in the receiver's bandwidth. Some of the receivers can also record SNR data which includes SNR component of reflected signals. In GNSS-IR, changes in soil moisture result in changes in the SNR component of the reflected signals. Specifically, as the soil moisture content increases, the dielectric constant of the soil increases, which causes the reflected signals to have higher amplitudes and higher SNR. Conversely, as the soil moisture content decreases, the reflected signals have lower amplitudes and lower SNR. Therefore, analyzing the SNR of the reflected signals can provide useful information about the soil moisture content. In addition to soil moisture, SNR can also be affected by other factors such as atmospheric conditions and receiver noise. Therefore, it is important to carefully analyze and process the SNR data to accurately estimate the soil moisture content.
The soil moisture algorithm to be used in this study, is currently implemented at stations in the EarthScope PBO H2O network with the greatest variations in vegetation. Among the sites in the PBO H2O network, data from P038 in the New Mexico region is used. This site is located at in a flat area in a ecosystem characterized as grass land.
SNR data from the new L2C signals are used by this site because the quality of the data are higher than those either the legacy L1 or L2P signals. The frequency of the L2C signal corresponds to a maximum penetration depth of 5 cm. The multipath signals are used to estimate soil moisture changes over a four-years period from 2017 to 2020.
The calculations were done in four main steps. In the first step, appropriate satellite tracks with elevation angle between 5 to 30 degrees were selected and SNR data were extracted from RINEX files. In the second step the initial reflector height is estimated for each track and then the phase is obtained for each satellite track on each day. In the third step, SNR metrics are calculated, and finally, vegetation cover effects are removed and the result is converted to volumetric water content. According to the estimations, the volumetric water content in 2017 was 8.88%, which increased to 11.74% in 2018, then slightly decreased to 10.88% in 2019 and finally increased to 12.49% in 2020. In the fourth step, the effectiveness of the LSTM neural network model in predicting the time series of volumetric soil moisture obtained from GNSS-IR signals is investigated. The LSTM neural network can maintain its content over a long period of time and essentially remember previous information. This prediction will help farmers to prepare their irrigation schedules more efficiently. For this purpose, it is suggested to use cheap GPS receivers in agricultural lands in rural areas. The model is trained using 80% of station observations. By updating the network status with observed values instead of predicted values, the root mean square error decreased from 0.09 to 0.04, and the predictions became more accurate.
Handling the location and type of receivers located in the Iranian Permanent GPS Network for Geodynamics (IPGN) and making the necessary settings in order to determine environmental parameters are suggested as by-products for IPGN. Investigations have shown that performing GNSS observations produces more homogeneous reflective effects. Therefore, in order to increase the accuracy and quality of the results, it is suggested to use GNSS-IR instead of just GPS-IR.

کلیدواژه‌ها [English]

  • volumetric soil moisture
  • SNR
  • GNSS-IR
  • time series
  • LSTM

مراجع

Alonso-Arroyo, A., Camps, A., Park, H., Pascual, D., Onrubia, R., & Martín, F. (2014). Retrieval of significant wave height and mean sea surface level using the GNSS-R interference pattern technique: Results from a three-month field campaign. IEEE Transactions on Geoscience and Remote Sensing, 53(6), 3198-3209.
Al-Yaari, A., Wigneron, J.-P., Kerr, Y., Rodriguez-Fernandez, N., O'Neill, P.E., Jackson, T. J., De Lannoy, G. J. M., Al Bitar, A., Mialon, A., Richaume, P., Walker, J. P., Mahmoodi, A., & Yueh, S. (2017), Evaluating soil moisture retrievals from ESA's SMOS and NASA's SMAP brightness temperature datasets. Remote Sensing of Environment, 193, 257-273.
Baroni, G., Ortuani, B., Facchi, A., & Gandolfi, C. (2013). The role of vegetation and soil properties on the spatio-temporal variability of the surface soil moisture in a maize-cropped field. J. Hydrol, 489,148–159. doi:10.1016/j.jhydrol.2013.03.007.
Brocca, L., Morbidelli, R., Melone, F., & Moramarco, T. (2007) Soil moisture spatial variability in experimental areas of central Italy. J. Hydrol, 333, 356–373. doi:10.1016/j.jhydrol.2006.09.004.
Cardellach, E., Fabra, F., Rius, A., Pettinato, S., & D'Addio, S. (2012). Characterization of dry-snow sub-structure using GNSS reflected signals. Remote Sensing of Environment, 124, 122-134.
Chen, Q., Jiang, W., Meng, X., Jiang, P., Wang, K., Xie, Y., & Ye, J. (2018). Vertical deformation monitoring of the suspension bridge tower using GNSS: A case study of the forth road bridge in the UK. Remote Sensing, 10(3), 364.
Chew, C. C., Small, E. E., Larson, K. M., & Zavorotny, V. U. (2013). Effects of near-surface soil moisture on GPS SNR data: Development of a retrieval algorithm for soil moisture. IEEE Transactions on Geoscience and Remote Sensing, 52(1), 537-543.
Chew, C. C., Small, E. E., Larson, K. M. & Zavorotny, V. U. (2014). Vegetation sensing using GPS-interferometric reflectometry: Theoretical effects of canopy parameters on signal-to-noise ratio data. IEEE Transactions on Geoscience and Remote Sensing, 53(5), 2755-2764.
Chew, C. C., Small, E. E., Larson, K. M., & Zavorotny, V. U. (2015). Vegetation sensing using GPS-interferometric reflectometry: theoretical effects of canopy parameters on signal-to-noise ratio data. IEEE Trans Geosci Remote Sens, 53, 2755–2764
Dobson, M. C., Ulaby, F. T., Hallikainen, M. T., & El-Rayes, M. A. (1985). Microwave dielectric behavior of wet soil-Part II: Dielectric mixing models. IEEE Transactions on geoscience and remote sensing, 1, 35-46.
Du, K. L., & Swamy, M. N., (2006). Neural networks in a softcomputing framework, Springer.
Entekhabi, B. D., Njoku, E. G., Neill, P. E. O., Kellogg, K. H., Crow, W. T., Edelstein, W. N., Entin, J. K., Goodman, S. D., Jackson, T. J., Johnson, J., Kimball, J., Piepmeier, J. R., Koster, R. D., Martin, N., McDonald, K. C., Moghaddam, M., Moran, S., Reichle, R., & Shi, J. C. (2010). The soil moisture active passive (SMAP) mission. Proc IEEE, 98,704–716.
Famiglietti, J. S., Rudnicki, J. W., & Rodell, M. (1998). Variability in surface moisture content along a hillslope transect: Rattlesnake Hill, Texas. J. Hydrol., 210, 259–281. doi:10.1016/S0022-1694(98)00187-5.
Ferrazzoli, P., Guerriero, L., Pierdicca, N., & Rahmoune, R. (2011). Forest biomass monitoring with GNSS-R: Theoretical simulations. Advances in Space Research, 47(10), 1823-1832.
Gers, F. A., Schmidhuber, J., & Cummins, F. (2000). Learning to Forget: Continual Prediction with LSTM. Neural Computation. 12 (10), 2451–2471. doi:10.1162/089976600300015015.
Gomez-Plaza, A., Martinez-Mena, M., Albaladejo, J., & Castillo, V. M. (2001). Factors regulating spatial distribution of soil water content in small semiarid catchments. J. Hydrol., 253, 211–226.
Greff, K., Srivastava, R. K., Koutník, J., Steunebrink, B. R., & Schmidhuber, J. (2017). LSTM: A Search Space Odyssey, in IEEE Transactions on Neural Networks and Learning Systems, 28 (10), 2222-2232, doi: 10.1109/TNNLS.2016.2582924.
Hochreiter, S., & Schmidhuber, J. (1996). LSTM can solve hard long time lag problems. Advances in Neural Information Processing Systems.
Jackson, T. J., Le-Vine, D. M., Swifi, C. T., & Schmugge, T. J. (1995). Large area mapping of soil moisture using the ESTAR passive microwave radiometer in washita’92. Remote Sens Environ, 53,27–37.
Jackson, T. J., & Le-Vine, D. E. (1996). Mapping surface soil moisture using an aircraft-based passive microwave instrument: algorithm and example. J. Hydrol, 184, 85–99.
Kerr, Y. H., Waldteufel, P., Wigneron, J., Martinuzzi, J., Font, J., & Berger, M. (2001). Soil moisture retrieval from space: the soil moisture and ocean salinity (SMOS) mission. IEEE Trans Geosci Remote Sens, 39,1729–1735.
Larson, K. M., Braun, J. J., Small, E. E., Zavorotny, V. U., Gutmann, E. D., & Bilich, A. L. (2009). GPS multipath and its relation to near-surface soil moisture content. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 3(1), 91-99.
Larson, K. M., Small, E. E., Gutmann, E., Bilich, A., Axelrad, P., & Braun, J. (2008). Using GPS multipath to measure soil moisture fluctuations: initial results. GPS solutions, 12(3), 173-177.
Larson, K. M., & Small, K. M. (2013). Estimation of Snow Depth Using L1 GPS Signal-to-Noise Ratio Data. in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(10), 4802-4808. doi: 10.1109/JSTARS.2015.2508673.
Larson, K. M., & Nievinski, F. G. (2013). GPS snow sensing: results from the Earthscope Plate Boundary Observatory. GPS Solutions, 17, 41–52. doi:10.1007/s10291-012-0259-7.
Li, X., Dick, G., Lu, C., Ge, M., Nilsson, T., Ning, T., Wickert, J., & Schuh, H. (2015). Multi-GNSS meteorology: real-time retrieving of atmospheric water vapor from BeiDou, Galileo, GLONASS, and GPS observations. IEEE Transactions on Geoscience and Remote Sensing, 53(12), 6385-6393.
Luo, X., Yan, S., Shan, J., Yan, H., & Wang, H. (2016). Using the BDS-R signal for soil moisture estimation. China Satellite Navigation Conference (CSNC) 2016 Proceedings: Volume I, Springer.
Mao, K., Wang, J., & Zhang, M. (2009). The study of soil moisture retrieval from GNSS_R signals based on AIEM model and experiment data. High Tech Lett, 3, 295-301.
Martín, A., Ibáñez, S., Baixauli, C., Blanc, S., & Anquela, A. B., (2020), Multi-constellation GNSS interferometric reflectometry with mass-market sensors as a solution for soil moisture monitoring. Hydrology and Earth System Sciences, 24, 3573–3582.
Mashburn, J., P. Axelrad, S. T. Lowe & K. M. Larson (2016). An assessment of the precision and accuracy of altimetry retrievals for a monterey bay GNSS-R experiment. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(10), 4660-4668.
Mashburn, J., Axelrad, P., Lowe, S. T. & Larson, K. M., (2018). Global ocean altimetry with GNSS reflections from TechDemoSat-1. IEEE Transactions on Geoscience and Remote Sensing, 56(7), 4088-4097.
Masters, D. (2004). Surface remote sensing applications of GNSS bistatic radar: Soil moisture and aircraft altimetry, University of Colorado at Boulder.
Mladenova, I., Lakshmi, V., Jackson, T. J., Walker, J. P., Merlin, O., & De-Jeu, R. A., (2011). Validation of AMSR-E soil moisture using L-band airborne radiometer data from National Airborne Field Experiment 2006. Remote Sens Environ, 115, 2096–2103. doi:10.1016/j.rse.2011.04.011.
Rajkai, K., & Ryden, B. E. (1992). Measuring areal soil moisture distribution with the TDR method. Geoderma, 52, 73–85.
Robock, A., Vinnikov, K. Y., Srinivasan, G., Entin, J. K., Hollinger, S. E., Speranskaya, N. A., Liu, S., & Namkhai, A. (2000). The global soil moisture data bank. Bull Am Meteorol Soc, 81, 1281–1299.
Rodriguez-Alvarez, N., Aguasca, A., Valencia, E., Bosch-Lluis, X., Ramos-Pérez, I., Park, H., Camps, A., & Vall-Llossera, M. (2011a). Snow monitoring using GNSS-R techniques. 2011 IEEE International Geoscience and Remote Sensing Symposium, IEEE.
Rodriguez-Alvarez, N., Bosch-Lluis, X., Camps, A., Ramos-Perez, I., Valencia, E., Park, H., & Vall-Llossera, M. (2011b). Vegetation water content estimation using GNSS measurements. IEEE Geoscience and Remote Sensing Letters, 9(2), 282-286.
Schwank, M., Matzler, C., Guglielmetti, M., & Fluhler, H. (2005). L-Band radiometer measurements of soil water under growing clover grass. IEEE Trans Geosci Remote Sens, 43, 2225–2237.
Tavakol, A., Rahmani, V., Quiring, S. M., Kumar, S. V. (2019). Evaluation analysis of NASA SMAP L3 and L4 and SPoRT-LIS soil moisture data in the United States. Remote Sensing of Environment, 229, 234-246.
Wan, W., Larson, K. M., Small, E. E., Chew, C. C., & Braun, J. J. (2014). Using geodetic GPS receivers to measure vegetation water content. GPS Solut. doi:10.1007/s10291-014-0383-7.
Xin, W., Qiang, S., XunXie, Z., DaRen, L., LianJun, S., Xiong, H., Giulio, R., Stephen, D., & Soulat, F. (2008). First China ocean reflection experiment using coastal GNSS-R. Chinese Science Bulletin, 53(7), 1117-1120.
Yu, K. (2014). Tsunami-wave parameter estimation using GNSS-based sea surface height measurement. IEEE Transactions on Geoscience and Remote Sensing, 53(5), 2603-2611.
Yu, K. (2016). Tsunami lead wave reconstruction based on noisy sea surface height measurements. Proc. Int. Archives Photogrammetry, Remote Sens. Spatial Inf. Sci.
Zhang, S., Wang, T., Wang, L., Zhang, J., Peng, J., & Liu, Q. (2021). Evaluation of GNSS-IR for Retrieving Soil Moisture and Vegetation Growth Characteristics in Wheat Farmland. Journal of Surveying Engineering, 147(3), https://doi.org/10.1061/(ASCE)SU.1943-5428.0000355.
Zavorotny, V. U., Larson, K. M., Braun, J. J., Small, E. E., Gutmann, E. D., Bilich, A. L. (2010). A physical model for GPS multipath caused by land reflections: toward bare soil moisture retrievals. IEEE J Sel Top Appl Earth Obs Remote Sens, 3, 1–11.
فیزیک زمین و فضا
دوره 50، شماره 2
تاریخ انتشار الکترونیکی: 16 تیر 1403
تیر 1403
صفحه 283-304

فایل ها

هم رسانی

ارجاع به این مقاله

آمار