Develope Daily Soil Moisture Maps for Binh Thuan Province Using Multi-Source Remote Sensing Data and Machine Learning Models
Main Article Content
Abstract
Soil moisture is a critical parameter influencing the survival of plants and soil organisms. It is a key climate variable for many hydrological processes as well as in carbon and energy cycles. Therefore, accurate remote monitoring of soil moisture is essential. This study proposes the utilization of multi-source remote sensing data, including CYGNSS, SMAP, and ancillary data from NOAA, to develop a daily soil moisture dataset. The experiment was conducted in Binh Thuan province during the period of 2020-2021. The standardized soil moisture index (SSMI) was calculated based on the obtained soil moisture data to create an agricultural drought risk map. The application of advanced machine learning algorithms enhances the spatial and temporal resolution and accuracy of soil moisture data. The results produced a series of daily soil moisture maps with a resolution of 250 meters, which serve as a foundation for forecasting agricultural drought risk in Binh Thuan province. This study highlights the potential of satellite-derived soil moisture data combined with machine learning techniques for effective soil moisture monitoring. The generated surface-level soil moisture maps can be utilized for daily monitoring, precise yield estimation, and the analysis of significant climate trends.
Keywords:
Soil Moisture, SMAP; CYGNSS; SAITS; SSMI, Binh Thuan.
References
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[37] T. Wang, C. S. Ruf, B. Block, D. S. McKague, S. Gleason, Design and Performance of a GPS Constellation Power Monitor System for Improved CYGNSS L1B Calibration, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 12, No. 1, 2018, pp. 26-36, DOI: 10.1109/JSTARS.2018.2867773
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[2] S. Kumar, P. Dirmeyer, C. P. Lidard, R. Bindlish and J. Bolten, Information Theoretic Evaluation of Satellite Soil Moisture Retrievals, Remote Sensing of Environment, Vol. 204, 2018, pp. 392-400, https://doi.org/10.1016/j.rse.2017.10.016.
[3] G. Jaime et al., Interactions between Precipitation, Evapotranspiration and Soil-Moisture-based Indices to Characterize Drought with High-Resolution Remote Sensing and Land-Surface Model Data, Natural Hazards and Earth System Sciences, Vol. 22, No. 10, 2022, pp. 3461-3485, https://doi.org/10.5194/nhess-22-3461-2022.
[4] H. Q. Lan, X. J. Lan, S. W. Yu, Responses of Terrestrial Evapotranspiration to Extreme Drought: A Review, Water, Vol. 14, No. 23, 2022, pp. 3847, https://doi.org/10.3390/w14233847.
[5] W. Wagner, K. Scipal, C. Pathe, D. Gerten, W. Lucht, B. Rudolf, Evaluation of the Agreement between the First Global Remotely Sensed Soil Moisture Data with Model and Precipitation Data, Journal of Geophysical Research Atmospheres, Vol. 108, No. D19, 2003, pp. 4611, https://doi.org/10.1029/2003JD003663.
[6] S. Evans, S. Allison, C. Hawkes, Microbes, Memory and Moisture: Predicting Microbial Moisture Responses and Their Impact on Carbon Cycling, Functional Ecology, Vol. 36, No. 6, 2022, pp. 1430-1441,
https://doi.org/10.1111/1365-2435.14034.
[7] S. Shukla, A. W. Wood, Use of a Standardized Runoff Index for Characterizing Hydrologic Drought, Geophysical Research Letters, Vol. 35, No. 2, 2008, https://doi.org/10.1029/2007GL032487.
[8] C. Funk and S. Shukla, Drought Early Warning and Forecasting, in Theory and Practice, Elsevier, 2020, https://pubs.usgs.gov/publication/70210188
[9] M. Li, H. Sun, R. Zhao, A Review of Root Zone Soil Moisture Estimation Methods Based on Remote Sensing, Remote Sensing, Vol. 15, No. 22, 2023, pp. 5361, https://doi.org/10.3390/rs15225361.
[10] S. Lekshmi, D. N. Singh and M. S. Baghini, A Critical Review of Soil Moisture Measurement, Measurement, Vol. 54, 2014, pp. 92-105, https://doi.org/10.1016/j.measurement.2014.04.007.
[11] J. P. Walker, G. R. Willgoose and J. D. Kalma, In Situ Measurement of Soil Moisture: A Comparison of Techniques, Journal of Hydrology, Vol. 293, No. 1-4, 2004, pp. 85-99, https://doi.org/10.1016/j.jhydrol.2004.01.008
[12] H. Wang, S. Gao, X. Yue, X. Cheng, Q. Liu, R. Min, H. Qu, X. Hu, Humidity-Sensitive PMMA Fiber Bragg Grating Sensor Probe for Soil Temperature and Moisture Measurement Based on Its Intrinsic Water Affinity, Sensors, Vol. 21,
No. 21, 2021, pp. 6946, https://doi.org/10.3390/s21216946.
[13] T. Li, T. Mu, G. Liu, X. Yang, G. Zhu, C. Shang, A Method of Soil Moisture Content Estimation at Various Soil Organic Matter Method of Soil Moisture Content Estimation at Various Soil Organic Matter, Remote Sensing, Vol. 14, No. 10, 2022, pp. 2411, https://doi.org/10.3390/rs14102411.
[14] F. Zakeri, G. Mariethoz, A Review of Geostatistical Simulation Models Applied to Satellite Remote Sensing: Methods and Applications, Remote Sensing of Environment, Vol. 259, 2021, pp. 112381, https://doi.org/10.1016/j.rse.2021.112381.
[15] D. J. Parsons et al., Regional Variations in The Link between Drought Indices and Reported Agricultural Impacts of Drought, Agricultural Systems, Vol. 173, 2019, pp. 119-129, https://doi.org/10.1016/j.agsy.2019.02.015.
[16] L. Meng, T. Ford, Y. Guo, Logistic Regression Analysis of Drought Persistence in East China, International Journal of Climatology, Vol. 37, No. 3, 2017, pp. 1444-1455, https://doi.org/10.1002/joc.4789.
[17] J. Li, S. Zhou, R. Hu, Hydrological Drought Class Transition Using SPI and SRI Time Series by Loglinear Regression, Water Resources Management, Vol. 30, 2016, pp. 669-684, https://doi.org/10.1007/s11269-015-1184-7
[18] D. J. Lorenz, J. A. Otkin, M. Svoboda, C. R. Hain, M. C. Anderson and Y. Zhong, Predicting the U.S. Drought Monitor Using Precipitation, Soil Moisture, and Evapotranspiration Anomalies. Part II: Intraseasonal Drought Intensification Forecasts, Journal of Hydrometeorology, Vol. 18, No. 7, 2017, pp. 1963-1982, https://doi.org/10.1175/JHM-D-16-0067.1
[19] S. W. Kim et al., Development of a Multiple Linear Regression Model for Meteorological Drought Index Estimation Based on Landsat Satellite Imagery, Water, Vol. 12, No. 12, 2020, pp. 3393, https://doi.org/10.3390/w12123393.
[20] H. Park, K. Kim, D. K. Lee, Prediction of Severe Drought Area Based on Random Forest: Using Satellite Image and Topography Data, Water, Vol. 11, No. 4, 2019, pp. 705, https://doi.org/10.3390/w11040705.
[21] S. Park, E. Seo, D. Kang, J. Im and M. I. Lee, Prediction of Drought on Pentad Scale Using Remote Sensing Data and MJO Index through Random Forest over East Asia, Vol. 10, No. 11, 2018, pp. 1811, https://doi.org/10.3390/rs10111811.
[22] F. A. Prodhan et al., A Review of Machine Learning Methods for Drought Hazard Monitoring and Forecasting: Current Research Trends, Challenges, and Future Research Directions, Environmental Modelling & Software, Vol. 149, 2022, pp. 105327, https://doi.org/10.1016/j.envsoft.2022.105327.
[23] D. Zhang, G. Zhou, Estimation of Soil Moisture from Optical and Thermal Remote Sensing: A Review, Sensors, Vol. 16, No. 8, 2016, pp. 1308, https://doi.org/10.3390/s16081308.
[24] S. Shafian, S. Maas, Index of Soil Moisture Using Raw Landsat Image Digital Count Data in Texas High Plains, Remote Sensing, Vol. 7, No. 3, 2015, pp. 2352-2372, https://doi.org/10.3390/rs70302352.
[25] S. Huang, J. Ding, J. Zou, B. Liu, J. Zhang, W. Chen, Soil Moisture Retrival Based on Sentinel-1 Imagery under Sparse Vegetation Coverage, Sensors, Vol. 19, No. 3, 2019, pp. 589, https://doi.org/10.3390/s19030589.
[26] V. Pauwels, A. Balenzano, G. Satalino, H. Skriver, N. Verhoest, F. Mattia, Optimization of Soil Hydraulic Model Parameters Using Synthetic Aperture Radar Data: An Integrated Multidisciplinary Approach, IEEE Transactions on Geoscience and Remote Sensing, Vol. 7, No. 2, 2009, pp. 455-467, https://doi.org/10.1109/TGRS.2008.2007849.
[27] Y. Zhu, F. Guo, X. Zhang, Effect of Surface Temperature On Soil Moisture Retrieval Using CYGNSS, International Journal of Applied Earth Observation and Geoinformation, Vol. 112, 2022, pp. 102929, https://doi.org/10.1016/j.jag.2022.102929.
[28] Y. Chen, X. Feng, B. Fu, An Improved Global Remote-Sensing-Based Surface Soil Moisture (RSSSM) Dataset Covering 2003–2018, Earth System Science Data, Vol. 13, No. 1, 2021, pp. 1-31, https://doi.org/10.5194/essd-13-1-2021.
[29] Z. Wang, T. Che, T. Zhao, L. Dai, X. Li, J. Wigneron, Evaluation of SMAP, SMOS, and AMSR2 Soil Moisture Products Based on Distributed Ground Observation Network in Cold and Arid Regions of China, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 14, 2021, pp. 8955-8970, https://doi.org/10.1109/JSTARS.2021.3108432.
[30] C. Chew, E. E. Small, Soil Moisture Sensing Using Spaceborne GNSS Reflections: Comparison of CYGNSS Reflectivity to SMAP Soil Moisture, Geophysical Research Letters, Vol. 45, No. 9, 2018, pp. 4049-4057, https://doi.org/10.1029/2018GL077905.
[31] M. M. A. Khaldi, J. T. Johnson, A. J. O’Brien, A. Balenzano, F. Mattia, Time-Series Retrieval of Soil Moisture Using CYGNSS, IEEE Transactions on Geoscience and Remote Sensing, Vol. 57, No. 7, 2019, pp. 4322-4331, https://doi.org/10.1109/TGRS.2018.2890646.
[32] M. Clarizia, N. Pierdicca, F. Costantini, N. Floury, Analysis of CYGNSS Data for Soil Moisture Retrieval, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 12, No. 7, 2019, pp. 2227-2235, https://doi.org/ 10.1109/JSTARS.2019.2895510.
[33] H. Kim, V. Lakshmi, Use of Cyclone Global Navigation Satellite System (CyGNSS) Observations for Estimation of Soil Moisture, Geophysical Research Letters, Vol. 45, No. 16, 2018, pp. 8272-8282, https://doi.org/10.1029/2018GL078923.
[34] Z. Dong, S. Jin, Evaluation of the Land GNSS-Reflected DDM Coherence on Soil Moisture Estimation from CYGNSS Data, Remote Sensing, Vol. 13, No. 4, 2021, pp. 570, https://doi.org/10.3390/rs13040570.
[35] Y. Zhu, F. Guo, X. Zhang, Effect of Surface Temperature on Soil Moisture Retrieval Using CyGNSS, International Journal of Applied Earth Observation and Geoinformation, Vol. 112, 2022, pp. 102929, https://doi.org/10.1016/j.jag.2022.102929.
[36] C. S. Ruf, S. Gleason, D. S. McKague, Assessment of CYGNSS Wind Speed Retrieval Uncertainty, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 12, No. 1, 2018, pp. 87-97, https://doi.org/10.1109/JSTARS.2018.2825948.
[37] T. Wang, C. S. Ruf, B. Block, D. S. McKague, S. Gleason, Design and Performance of a GPS Constellation Power Monitor System for Improved CYGNSS L1B Calibration, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 12, No. 1, 2018, pp. 26-36, DOI: 10.1109/JSTARS.2018.2867773
[38] V. Y. Senyurek et al., Evaluations of a Machine Learning-Based CYGNSS Soil Moisture Estimates against SMAP Observations, Remote Sensing, Vol. 12, No. 21, 2020, pp. 3503, https://doi.org/10.3390/rs12213503.
[39] P. Rao, Y. Wang, F. Wang, Y. Liu, X. Wang, Z. Wang, Daily Soil Moisture Mapping at 1km Resolution Based on Smap Data for Desertification Areas in Northern China, Earth System Science Data, Vol. 14, No. 7, 2022, pp. 3053-3073, https://doi.org/10.5194/essd-14-3053-2022.
[40] W. Du, D. Côté, Y. Liu, Saits: Attention-Based Attribution Ideas for Time Series, Expert Systems with Applications, Vol. 219, 2023, pp. 119619, https://doi.org/10.1016/j.eswa.2023.119619.
[41] N. R. Alvarez, E. Podest, K. Jensen, K. C. McDonald, Classifying Inundation in a Tropical Wetlands Complex with GNSS-R, Remote Sensing, Vol. 11, No. 9, 2019, pp. 1053, https://doi.org/10.3390/rs11091053.
[42] O. Eroglu, M. Kurum, D. Boyd, A. C. Gurbuz, High Spatio-Temporal Resolution CYGNSS Soil Moisture Estimates Using Artificial Neural Networks, Remote Sensing, Vol. 11, No. 19, 2019, https://doi.org/10.3390/rs11192272.