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  2. Vol 9, No 6 (2023)
  3. Kanmani

Estimation of Soil Moisture for Different Crops Using SAR Polarimetric Data

K. Kanmani, Vasanthi P., Packirisamy Pari, N. S. Shafeer Ahamed

Abstract


Soil moisture is an essential factor that influences agricultural productivity and hydrological processes. Soil moisture estimation using field detection methods takes time and is challenging. However, using Remote Sensing (RS) and Geographic Information System (GIS) technology, soil moisture parameters become easier to detect. In microwave remote sensing, synthetic aperture radar (SAR) data helps to retrieve soil moisture from more considerable depths because of its high penetration capability and the illumination power of its light source. This study aims to process the SAR Sentinel-1A data and estimate soil moisture using the Water Cloud Model (WCM). Many physical and empirical models have been developed to determine soil moisture from microwave remote sensing platforms. However, the Water Cloud Model gives more accurate results. In this study, the WCM model is used for mixed crop types. The experimental soil moisture was determined from in-situ soil samples collected from various agricultural areas. The soil backscattering values corresponding to the different soil sampling locations were derived from Sentinel SAR data. Using linear regression analysis, the laboratory's soil moisture results and soil backscattering values were correlated to arrive at a model. The model was validated using a secondary set of in-situ moisture content values taken during the same period. The R2 and RMSE of the model were observed to be 0.825 and 0.0274, respectively, proving a strong correlation between the experimental soil moisture and satellite-derived soil moisture for mixed crop field types. This paper explains the methodology for arriving at a model for soil moisture estimation. This model helps to recommend suitable crop types in large, complex areas based on predicted moisture content.

 

Doi: 10.28991/CEJ-2023-09-06-08

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Keywords


Water Cloud Model (WCM); Synthetic Aperture Radar (SAR); Soil Backscattering.

References


Baghdadi, N., Holah, N., & Zribi, M. (2006). Soil moisture estimation using multi-incidence and multi-polarization ASAR data. International Journal of Remote Sensing, 27(10), 1907–1920. doi:10.1080/01431160500239032.

Das, K., & Paul, P. K. (2015). Soil moisture retrieval model by using RISAT-1, C-band data in tropical dry and sub-humid zone of Bankura district of India. Egyptian Journal of Remote Sensing and Space Science, 18(2), 297–310. doi:10.1016/j.ejrs.2015.09.004.

Baghdadi, N., Choker, M., Zribi, M., El Hajj, M., Paloscia, S., Verhoest, N. E. C., Lievens, H., Baup, F., & Mattia, F. (2016). A new empirical model for radar scattering from bare soil surfaces. Remote Sensing, 8(11), 920. doi:10.3390/rs8110920.

Santi, E., Paloscia, S., Pettinato, S., Notarnicola, C., Pasolli, L., & Pistocchi, A. (2013). Comparison between SAR soil moisture estimates and hydrological model simulations over the Scrivia test site. Remote Sensing, 5(10), 4961–4976. doi:10.3390/rs5104961.

Tao, L., Wang, G., Chen, X., Li, J., & Cai, Q. (2019). Estimation of soil moisture using a vegetation scattering model in wheat fields. Journal of Applied Remote Sensing, 13(04), 1. doi:10.1117/1.jrs.13.4.044503.

Song, K., Zhou, X., & Fan, Y. (2010). Retrieval of Soil Moisture Content from Microwave Backscattering Using a Modified IEM Model. Progress In Electromagnetics Research B, 26, 383–399. doi:10.2528/pierb10072905.

Sahadevan, D. K., Rao, S. S., & Pandey, A. K. (2019). Soil Moisture Monitoring with Dual-Incidence-Angle RISAT-1 Data: A Pilot Study from Vidarbha Region. Journal of the Indian Society of Remote Sensing, 47(9), 1497–1506. doi:10.1007/s12524-019-00998-4.

Sanli, F. B., Kurucu, Y., Esetlili, M. T., & Abdikan, S. (2008). Soil moisture estimation from RADARSAT-1, ASAR and PALSAR data in agricultural fields of Menemen plane of western Turkey. International Society for Photogrammetry and Remote Sensing, 37, 75-81.

Jackson, T. J., Schugge, T. J., Nicks, A. D., Coleman, G. A., & Engman, E. T. (1981). Soil moisture updating and microwave remote sensing for hydrological simulation. Hydrological Sciences Bulletin, 26(3), 305–319. doi:10.1080/02626668109490889.

Oh, Y., Sarabandi, K., & Ulaby, F. T. (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.2002.800232.

Srinivasa Rao, S., Dinesh kumar, S., Das, S. N., Nagaraju, M. S. S., Venugopal, M. V., Rajankar, P., Laghate, P., Reddy, M. S., Joshi, A. K., & Sharma, J. R. (2013). Modified Dubois Model for Estimating Soil Moisture with Dual Polarized SAR Data. Journal of the Indian Society of Remote Sensing, 41(4), 865–872. doi:10.1007/s12524-013-0274-3.

Thanabalan, P., & Vidhya, R. (2018). Derivation of soil moisture using modified Dubois model with field assisted surface roughness on RISAT-1 data. Earth Sciences Research Journal, 22(1), 13–18. doi:10.15446/esrj.v22n1.59972.

Mirsoleimani, H. R., Sahebi, M. R., Baghdadi, N., & El Hajj, M. (2019). Bare soil surface moisture retrieval from sentinel-1 SAR data based on the calibrated IEM and dubois models using neural networks. Sensors (Switzerland), 19(14), 3209. doi:10.3390/s19143209.

Collingwood, A., Charbonneau, F., Shang, C., & Treitz, P. (2018). Spatiotemporal variability of arctic soil moisture detected from high-resolution RADARSAT-2 SAR Data. Advances in Meteorology, 2018, 1–17. doi:10.1155/2018/5712046.

Said, S., Kothyari, U. C., & Arora, M. K. (2008). ANN-Based Soil Moisture Retrieval over Bare and Vegetated Areas Using ERS-2 SAR Data. Journal of Hydrologic Engineering, 13(6), 461–475. doi:10.1061/(asce)1084-0699(2008)13:6(461).

Gopal, S., & Woodcock, C. (1996). Remote sensing of forest change using artificial neural networks. IEEE Transactions on Geoscience and Remote Sensing, 34(2), 398–404. doi:10.1109/36.485117.

Angiuli, E., del Frate, F., & Monerris, A. (2008). Application of Neural Networks to Soil Moisture Retrievals from L-Band Radiometric Data. IGARSS, IEEE International Geoscience and Remote Sensing Symposium. doi:10.1109/igarss.2008.4778927.

Mas, J. F., & Flores, J. J. (2008). The application of artificial neural networks to the analysis of remotely sensed data. International Journal of Remote Sensing, 29(3), 617–663. doi:10.1080/01431160701352154.

Nijaguna, G. S., Manjunath, D. R., Abouhawwash, M., Askar, S. S., Basha, D. K., & Sengupta, J. (2023). Deep Learning-Based Improved WCM Technique for Soil Moisture Retrieval with Satellite Images. Remote Sensing, 15(8). doi:10.3390/rs15082005.

Döpper, V., Rocha, A. D., Berger, K., Gränzig, T., Verrelst, J., Kleinschmit, B., & Förster, M. (2022). Estimating soil moisture content under grassland with hyperspectral data using radiative transfer modelling and machine learning. International Journal of Applied Earth Observation and Geoinformation, 110, 102817. doi:10.1016/j.jag.2022.102817.

Lei, J., Yang, W., & Yang, X. (2022). Soil Moisture in a Vegetation-Covered Area Using the Improved Water Cloud Model Based on Remote Sensing. Journal of the Indian Society of Remote Sensing, 50(1), 1–11. doi:10.1007/s12524-021-01450-2.

Yahia, O., Karoui, M. S., & Guida, R. (2022). An Inversion of a Modified Water Cloud Model for Soil Moisture Content Estimation Through Sentinel-1 and Landsat-8 Remote Sensing Data. IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium. doi:10.1109/igarss46834.2022.9884192.

Luo, D., Wen, X., & He, P. (2023). Surface Soil Moisture Estimation Using a Neural Network Model in Bare Land and Vegetated Areas. Journal of Spectroscopy, 2023, 1–10. doi:10.1155/2023/5887177.

Neusch, T., & Sties, M. (1999). Application of the Dubois-model using experimental synthetic aperture radar data for the determination of soil moisture and surface roughness. ISPRS Journal of Photogrammetry and Remote Sensing, 54(4), 273–278. doi:10.1016/S0924-2716(99)00019-2.

Pari, P., Thirumaraiselvan, P., Ramalingam, M., & Jayalakshmi, S. (2020). A statistical model for estimation of soil moisture in paddy field using microwave satellite data. Progress In Electromagnetics Research M, 94, 155–166. doi:10.2528/PIERM20051401.

Polychronaki, A., Gitas, I. Z., Veraverbeke, S., & Debien, A. (2013). Evaluation of ALOS PALSAR imagery for burned area mapping in greece using object-based classification. Remote Sensing, 5(11), 5680–5701. doi:10.3390/rs5115680.

Wang, H., Méric, S., Allain, S., & Pottier, E. (2014). Adaptation of Oh Model for soil parameters retrieval using multi-angular RADARSAT-2 datasets. Journal of Surveying and Mapping Engineering, 2(4), 65-74.

Han, D., Liu, S., Du, Y., Xie, X., Fan, L., Lei, L., Li, Z., Yang, H., & Yang, G. (2019). Crop water content of winter wheat revealed with sentinel-1 and sentinel-2 imagery. Sensors (Switzerland), 19(18), 4013. doi:10.3390/s19184013.

Clementini, C., Pomente, A., Latini, D., Kanamaru, H., Vuolo, M. R., Heureux, A., Fujisawa, M., Schiavon, G., & Frate, F. Del. (2020). Long-term grass biomass estimation of pastures from satellite data. Remote Sensing, 12(13), 2160. doi:10.3390/rs12132160.

Ferencz, C., Bognár, P., Lichtenberger, J., Hamar, D., Tarcsai, G., Timár, G., Molnár, G., Pásztor, S., Steinbach, P., Székely, B., Ferencz, O. E., & Ferencz-Árkos, I. (2004). Crop yield estimation by satellite remote sensing. International Journal of Remote Sensing, 25(20), 4113–4149. doi:10.1080/01431160410001698870.

Kumar, K., Arora, M. K., & Hariprasad, K. S. (2016). Geostatistical analysis of soil moisture distribution in a part of Solani River catchment. Applied Water Science, 6(1), 25–34. doi:10.1007/s13201-014-0202-x.

Bai, X., & He, B. (2015). Potential of Dubois model for soil moisture retrieval in prairie areas using SAR and optical data. International Journal of Remote Sensing, 36(22), 5737–5753. doi:10.1080/01431161.2015.1103920.

Cho, S., Jeong, J., Baik, J., & Choi, M. (2020). Soil moisture estimation based on water cloud model at the mountainous area. 40th Asian Conference on Remote Sensing, ACRS 2019: 14-18 October, 2019, Daejeon, South Korea.

Bao, Y., Lin, L., Wu, S., Kwal Deng, K. A., & Petropoulos, G. P. (2018). Surface soil moisture retrievals over partially vegetated areas from the synergy of Sentinel-1 and Landsat 8 data using a modified water-cloud model. International Journal of Applied Earth Observation and Geoinformation, 72, 76–85. doi:10.1016/j.jag.2018.05.026.


Full Text: PDF

DOI: 10.28991/CEJ-2023-09-06-08

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Copyright (c) 2023 Vasanthi Padmanabhan

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