2_Kishan_et al..indd 17Rawat, K.S. et al. Hungarian Geographical Bulletin 69 (2020) (1) 17–26.DOI: 10.15201/hungeobull.69.1.2 Hungarian Geographical Bulletin 69 2020 (1) 17–26. Introduction Soil moisture is an important and key factor that influences the meteorological param- eters directly or indirectly; therefore it is im- portant to understand its patterns and cause of variations at the region level (Hornberger, G.M. 1998). The dynamics of soil moisture plays a critical role in the analysis of agri- cultural drought, weather forecast, flood forecasting, crop yield prediction and cli- matology (Berthet, L. et al. 2009; Beck, H.E. et al. 2009; Hegedűs, P. et al. 2013, 2015; Dezső, J. et al. 2019). The sensitivity of microwave signals is directly reciprocal to soil dielec- tric constant, which reflect the soil moisture; microwave signal can penetrate vegetation canopy and provide soil moisture states (Bindlish, R. et al. 2006). Soil moisture at a regional scale can be observed by the syn- thetic aperture radar (SAR) with fine spatial and temporal resolutions (Shi, J.C. et al. 1997; Pathe, C. et al. 2009; Paloscia, S. et al. 2013; Pasolli, L. et al. 2015). Nowadays, many models are available for the quantification of soil moisture at a regional scale, but model complexity and exhaustive data input requirement limit their applications. However, the water cloud model (WCM) requires a lower num- ber of input data (Attema, E. and Ulaby, F.T. 1978). The WCM has four empirical coefficients, namely canopy descriptor pa- rameters (A and B) and soil parameters (C and D). At local scale to regional analysis, the vegetation/crop coefficients of the WCM 1 Civil Engineering Department, Graphic Era (Deemed to be University), Dehradun – 24 8002 Uttrakhand, India. E-mails: ksr.kishan@gmail.com, er.sanjeevkr@gmail.com 2 K. Banerjee Centre of Atmospheric & Ocean Studies, IIDS, Nehru Science Centre, University of Allahabad, Prayagraj – 21 1002, Uttar Pradesh, India. Corresponding author’s e-mail: sudhirinjnu@gmail.com 3 College of Agriculture and Human Sciences, Prairie View A&M University, P.O. Box 519, MS 2008 Prairie View, TX 77446, USA. E-mail: ram.ray36@gmail.com 4 Department of Physical Geography and Geoinformation Systems, University of Debrecen, 4032 Debrecen, Egyetem tér 1. Hungary, E-mail: szaboszilard.geo@gmail.com Parameterizing the modified water cloud model to improve soil moisture data retrieval using vegetation models Kishan Singh R AWAT 1, Sudhir Kumar S I N G H 2, Ram L. Ray3, Szilárd SZABÓ4 and Sanjeev KUMAR1 Abstract The objective was to parameterize a modified water cloud model using crop coefficients (A and B). These crop coefficients were derived from Landsat-8 and Sentinel-2 data. Whereas coefficients C and D are of soil parameters. The water cloud model was modified using crop coefficients by minimizing the RMSE between observed VVσ0 and Sentinel-1 based simulated VVσ0. The comparison with observed and simulated VV polarized σ0 showed low RMSE (0.81 dB) and strong R2 of 0.98 for NDVI-EVI combination. However, based on other possible combinations of vegetation indices VVσ0 and simulated VVσ0 do not show a good statistical agreement. It was observed that the errors in crop coefficients (A and B) are sensitive to errors in initial vegetation/canopy descriptor parameters. Keywords: NDVI, EVI, SAR, Sentinel, WCM Rawat, K.S. et al. Hungarian Geographical Bulletin 69 (2020) (1) 17–26.18 are usually calculated by statistical analysis using in-situ datasets (Prevot, L. et al. 1993; Magagi, R. and Kerr, Y. 1997; Frison, P. et al. 1998). Kumar, K. et al. (2012) used a genetic algorithm (GA) to calculate vegetation coeffi- cients (A and B) at the local scale using an em- pirical relationship between surface scattering and vegetation/crop biophysical parameters (using ENVISAT ASAR VV-pol data). Since these in-situ datasets are generally collected from specific observation sites, hence it is im- portant to understand the usefulness of these observations in other regions. Consequently, in most of the crop models, crop coefficients vary from one location to another. It is impor- tant to develop a new approach or use exist- ing approaches for the identification of crop coefficients, which does not require the in-situ observation of the biological and physical pa- rameters of crops. Currently, WCM requires V1 and V2 vegetation parameters, which are associated with A, B, C, and D coefficients. Therefore, V1 and V2 must be precise, and eas- ily available otherwise spatial variability of A, B, C, and D will be high. In the past, these two (V1 and V2) vegetation parameters were estimated using extensive fieldwork within the study area; therefore, parameterization of WCM was easy at a local scale. Rawat, K.S. et al. (2017, 2018) successfully estimated soil moisture using modified WCM (MWCM) by replacing V1 and V2 with NDVI value. The study objective was to parameterize the MWCM using different combinations of vegeta- tion indices. The combinations are categorized into Cases (I-IV) of combinations of vegetation indices such as Case I (V1-V2 = NDVI-EVI), Case II (V1-V2 = NDVI-NDVI), Case III (V1-V2 = EVI- EVI) and Case IV (V1-V2 = EVI-NDVI); where NDVI is normalized vegetation index, and EVI is the enhanced vegetation index). Materials Study area and ground data The Bathinda district study located in the state of Punjab India and is a region with wheat being the dominant crop (from 30°4’30” N to 30°21’20” N latitude and from 74°47’50” E to 75°10’00” E longitude) with average elevation of 210 m from sea level (Figure 1). The district of Bathinda lies in the extreme southwestern part of Punjab and far away from the Shivalik ranges in the North of the state. The normal annual rainfall of this region is about 408 mm, 80 per cent of which is received during the southwestern monsoon season (First week of July to mid- September) and remaining during the winter season. Dust storms are a regular feature in summer season when the temperature reach- es to 47.0 °C in the peak summer in May- Fig. 1. Location map of the study area 19Rawat, K.S. et al. Hungarian Geographical Bulletin 69 (2020) (1) 17–26. June, however, in winter during December and January, the minimum temperature at night could reach 0.0 °C. The soil in the area is mostly loamy sand and sandy loam and contained 68–70 per cent sand, 12–15 per cent silt and 18–21 per cent clay. Due to light texture, the water holding capacity of soil in the district varies from 25– 30 per cent depends on the clay content and organic matter present in the soil. The arid brown soils are calcareous in nature; these soils are imperfectly to moderately drained and siezoram soils the accumulation of cal- cium carbonate (CGWB, 2017). Yadav, B.K. et al. (2018) determined that the soils were low in available nitrogen (N) low to medium in available phosphorous (P) and medium to high in available potassium (K) content. They also found that there was wide variation in soil fertility status has developed on various landforms in Bathinda District, but the soils were low in available N, low to medium in available P and medium to high in available K content. The measurements of soil moisture and vegetation parameters were carried out during the Sentinel-1 overpasses (dates are given in Table 1). The Sentinel-1 mission provides active mi- crowave data of C-band with 10 m resolution and has potential for soil moisture mapping. Further, European Space Agency (ESA) con- stellation of one more identical Sentinel-1A satellite on 25 April 2016 named Sentinel- 1B which has two microwave Synthetic Aperture Radar (SAR) sensors for improve- ment in temporal resolution. Total of thirty imageries were acquired during the winter wheat crop growing period (details are provided in Table 1). During each overpass of the satellite, in- situ soil moisture measurements were per- formed using a time-domain reflectometer instrument (TDR, Field ScoutTM TDR 300, Spectrum Technologies, Aurora, IL, United States). From sampling sites, soil moisture was measured using a TDR at a soil depth of 0–5 cm. Calibration of the TDR instrument was performed as suggested by Rawat, K.S. et al. (2017, 2018). The ancillary data, namely surface roughness, leaf area index (LAI), crop height, crop coverage and crop physiological states data were also collected. Landsat-8 and Sentinel-2 data A total of sixteen Sentinel-2 and Landsat-8 data- sets have been downloaded to estimate veg- etation greenness in 2018 (Table 1). The spatial resolutions of Landsat-8 and Sentinel-2 were 30 m and 10 m, respectively. After pre-process- ing, vegetation greenness was calculated using the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI). Since the date of data acquisition differ- ence was very small, hence a limited or no change was observed in NDVI and EVI value. Therefore, we did not performed any interpolation of NDVI and EVI value along with SAR images. Sentinel-1 data The Sentinel-1 operates at 5.4 GHz frequen- cy, and has four imaging modes, namely Stripmap model, Interferometric wide swath, extra-wide swath, and wave mode. In pre- Table 1. Satellite data with a different date Sensor Dates in 2018 Spatial resolution Sentinel-1A/B Sentinel-2A/B Landsat-8 January (20, 24), February (1, 5, 13, 17, 25), March (1, 9, 13, 21, 25), April (6, 14) January (1, 27), February (1, 6, 12, 18), March (1, 6), April (7, 15) January (8, 17), February (14, 22, 26), March (30) 10 m 10 m 30 m Rawat, K.S. et al. Hungarian Geographical Bulletin 69 (2020) (1) 17–26.20 sent research work, S1 TOPS-model SLC data of an interferometric wide swath mode has been used for soil moisture estimation. The pre-processing (radiometric and ortho- rectification) was performed according to the Sentinel-1 user data handbook. The Sentinel-1 data sets were processed in the SNAP plat- form (http://step.esa.int/main/toolboxes/ snap). The Sentinel-1 imageries were acquired in VV and VH polarization with an incidence angle near to 38°. We processed only VV polarization in our study because past stud- ies (Rawat, K.S. et al. 2019a, b) showed that VH polarization does not provide relevant crop/soil information with WCM for our study area. Time Domain Reflectometer (TDR) instrument The instrument TDR with a 7.5 cm probe length was used to collect in-situ soil mois- ture. TDR has wide spectrum frequency; it also works in C-band frequency as the Sentinel-1. TDR is a lightweight, portable instrument, it was used for in-situ observa- tion. Besides, TDR may be used to get a large number of measurements over a short period of time (within satellite overpass of the study area) (Rawat, K.S. et al. 2017, 2018, 2019a, b). Methods The Sentinel-1 data sets were processed for only VV polarization and generated backscat- tering coefficient (σ°) which is known as total σ0 (or σ0total) because two σ 0 contribute in σ0total, backscattering from soil σ0 (σ0soil) and backscat- tering from vegetation (σ0veg). The σ 0 total of in-si- tu observation sites were derived using SNAP software. Similarly, NDVI and EVI value of each in-situ measurement sites were derived from the Sentinel-2 and Landsat-8 data. There was no need for re-sampling of Landsat-8 into the Sentinel-2 or Sentinel-2 into Landsat-8 be- cause our study area was homogenous and the size of the in-situ measurement plots was of dimensions more than 30 m × 30 m. The MWCM was used to develop a semi- empirical model for soil moisture estimation using microwave data. In MWCM, vegetation descriptors (or V1 and V2) were replaced by a couple of vegetation indices as Cases (I-IV) (as V1-V2 = NDVI-EVI, V1-V2 = NDVI-NDVI, V1-V2 = EVI-EVI; V1-V2 = EVI-NDVI; where NDVI = normalized vegetation index [Bala, A. et al. 2015; Rawat, K.S. et al. 2017, 2019a, b] and EVI = enhances vegetation index). Modified Water Cloud Model (MWCM) The WCM has a great possibility to diminish the effect of vegetation by computing the σ0veg. It can be expressed by the following equation (1): σ0 total (dB) = σ 0 veg + σ 0 veg+soil + τ 2 σ0 soil , For a given radar signal, σ0 from the bare soil has a linear function of the soil moisture with depth (0.0–7.5 cm) (Attema, E. and Ulaby, F.T. 1978) and σ0veg+soil ≈ 0; therefore, eq. 1 can be modified as: σ0 total (dB) = σ 0 veg + τ 2 σ0 soil , where: σ0 veg (dB) = AV1 cos(1 τ 2), τ2 = exp(2BV2 / cosθ), σ0 soil (dB) = C + D . SM, where, θ is incident angle; A and B are veg- etation coefficients that depend on the type of canopy, while coefficients C and D are soil dependent, SM is soil moisture. V1 and V2 are canopy parameters and WCM was modified by changing these parameters (V1 and V2) by NDVI or EVI. Magagi, R. and Kerr, Y. (1997) investi- gated that due to change in vegetation states, canopy properties (bio and physical) change temporally. Therefore, a vegetation index is capable of explaining vegetation growth states. Model parametrization Based on Magagi, R. and Kerr, Y. (1997), we have replaced V1 and V2 by a pair of vegetation eq. 1 eq. 2 eq. 3 eq. 4 eq. 5 21Rawat, K.S. et al. Hungarian Geographical Bulletin 69 (2020) (1) 17–26. indices (by applying in eq. 3 and 4), namely NDVI and EVI, since no previous work was found on replacing particular vegetation pa- rameter (e.g. V1 or V2) by specific vegetation index (e.g. NDVI or EVI) to obtain optimal value of MWCM coefficients. However, NDVI and EVI can describe the winter wheat crop canopy as canopy descriptors. We tested dif- ferent possible pairs of NDVI and EVI for the parametrization of the MWCM. We assumed that different possible combinations of NDVI and EVI would be better to incorporate the spatial water content/status, its spatial distri- bution within a confined volume, and would stand out an accurate simulation of total σ0. The inverse distance method was applied for interpolation to generate the spatial maps of soil moisture (Mishra, A. et al. 2009). Vegetation index (NDVI/EVI) Crop mapping and environmental research commonly use NDVI (Garroutte, E. et al. 2016). Huete, A.R. (1988) investigated that NDVI responses were high for canopy back- ground variations and showed saturated sig- nals for high biomass conditions. EVI, suggest- ed by Qi, J. et al. (1994), improves sensitivity over dense vegetation conditions without the effect of the canopy background by minimiz- ing canopy-soil variations (Huete, A.R. et al. 2002). We had selected two vegetation indices, and these vegetation indices were developed using equations (eq. 6, 7, 8 and 9). The particu- lar band (blue, red, and near-infrared bands of Landsat-8 and Sentinel-2A/B) after atmospher- ic correction and conversion of digital number into reflectance of particular bands. NDVI and EVI were used for the para- metrization of vegetation and soil coefficients of MWCM (eq. 6 and eq. 7 for Landsat 8; eq. 8 and eq. 9 for Sentinel-2): NDVI = (Band5 – Band4) (Band5 + Band4) where, the value of 2.5 in eq. 6 and 9 is a gain factor while 7.5 and 2.4 in eq. 6 and 9 (https://webapps.itc.utwente.nl/librarywww/ papers_2017/msc/nrm/adan.pdf ) are coef- ficients, used to reduce aerosol effects and value 1 is the soil adjustment factor. Model coefficients (A, B, C, and D) estimation Images of the study period were downloaded of 20/01/2018 to14/04/2018. This study assumed that the roughness over the crop was constant during the study period because of the sin- gle wheat crop, a slight change in roughness. Hence the number of unknown variables reduc- es into four for MWCM: coefficients of A, B, C, and D estimation errors (for coefficients) due to different factors (e.g. SAR sensor measurement error, optical sensor measurement error and NDVI estimation error) were also reduced. This process diminishes the effects over coefficients. The following two steps were used to prepare data for the optimal value of coefficients: – Measured σ0 at soil moisture sampling (us- ing TDR) point from each corresponding to the Sentinel-1 data. – The NDVI was calculated at each sampling point in the series of data. The iterative optimization method was ap- plied in SigmaPlot-12.0 to estimate the model coefficients (A, B, C and D). These model co- efficients are important in predicting in σ0 using the MWCM. Evaluation of observed and estimated σ0 In this study, the estimated VV polarized σ0 with the help of possible combinations of NDVI and EVI as canopy descriptor and gen- erated MWCM coefficients were tested with observed VVσ0 of Sentinel-1 using statistical EVI = 2.5 . (Band5 – Band4) (Band5 + 6 . Band4 – 7.5 . Band2 + 1) eq. 6 eq. 7 NDVI = (Band8 – Band4) (Band8 + Band4) eq. 8 EVI = 2.5 . (Band8 – Band4) (Band8 + 2.4 . Band4 +1) eq. 9 Rawat, K.S. et al. Hungarian Geographical Bulletin 69 (2020) (1) 17–26.22 tests and method as explained in Rawat, K.S. et al. (2017, 2018). Statistical tests are the way to evaluate the accuracy of predicted data compared to observed data. However, there must be a sufficient number of datasets to draw a conclusion using statistical tests. Results and discussion For the simulation of VV σ0, the vegetation and soil coefficients were estimated for four combinations of NDVI and EVI for V1 and V2 (Table 2). The C and D soil coefficients have almost fixed values for each of the four combinations of vegetation indices (VIs) for VVσ0; therefore, C and D are free from cano- py properties in the MWCM. Also, this type of interpretation can only be driven when V1 and V2 replaced by different vegetation indi- ces combinations. If V1 and V2 are replaced by same VI (may be NDVI or EVI) then it cannot be concluded that C and D are independent of canopy because same NDVI (V1)-NDVI (V2) or EVI (V1)-EVI (V2) combinations of VI gives us only one value of C and D. The value of A and B are different from different pos- sible combinations of VIs (see Table 2). The values of A and B are completely governed by canopy properties (an orientation of leaf, the water content in leaf, and chlorophyll). It is further clarification of why more than one VI should be used for parameterization of MWCM. Table 2, non-zero values of A and B indicate that we cannot ignore the contribu- tion of a canopy in microwave analysis. MWCM parameterization using VIs was conducted by minimizing the RMSE with the best R2 value between observed and predict- ed VV σ0 to optimize the effective unknown coefficients (A, B, C, and D). The comparative results of RMSE and R2 between observed and predicted VV σ0 from different vegeta- tion parameters in combination are present- ed in Table 2 and shown in Figure 2. MWCM Parameterization I: Estimation of A, B, and C, D coefficients using NDVI (V1)-NDVI (V2) NDVI is a commonly used index to monitor crop canopy, health and spatial distribution of vegetation during the growing season in agriculture. Therefore, NDVI-NDVI combi- nation (case I) was used instead of V1-V2 veg- etation parameters in MWCM. The graphi- cal simulation of generated VV σ0 to VV σ0 from microwave data over the wheat crop (Figure 2, a). A total of 82 and observed data (NDVI and soil moisture) points (as input for σ0 simulation) were used for NDVI-ND- VI performance for predicting VV σ0 from MWCM. The model simulated VV σ0 with a good R2 value of 0.61, while the RMSE was highest in this combination (see Table 2). The NDVI represents the crop canopy solely in terms of its biophysical properties, and can- opy background incorporates the dielectric properties. MWCM Parameterization II: Estimation of A, B, and C, D coefficients using NDVI (V1)-EVI (V2) NDVI and EVI combination (case II) was used as the canopy descriptors in MWCM. The R2 Table 2. Vegetation and soil coefficients with different possible combinations of vegetation parameters Canopy parameters of MWCM MWCM coefficients RMSE R2Canopy coefficients Soil coefficients V1 V2 A B C D NDVI NDVI EVI EVI NDVI EVI NDVI EVI 3.99 -2.89 -1.25 -5.65 8.38 0.418 0.018 0.171 11.33 11.21 11.32 11.30 0.03 0.02 0.02 0.15 0.89 0.73 0.87 0.79 0.61 0.68 0.59 0.63 23Rawat, K.S. et al. Hungarian Geographical Bulletin 69 (2020) (1) 17–26. and RMSE (0.68 and 0.73, respectively, see Table 2) show good correlation (Figure 2, b). The MWCM retrieved VV σ0 with a good accuracy based on statistical tests. It may be due to both conditions of crop/vegeta- tion canopy information with a background (NDVI) and canopy information without the background (EVI). This possible combination also indicates that V1 slightly dependent on canopy background while V2 is independent of canopy background information (because EVI is free from canopy background informa- tion (Qi, J. et al. 1994; Huete, A.R. et al. 2002). Therefore, both VIs combinations make a good prediction of VV σ0 from MWCM and moderately improves the performance. MWCM Parameterization III: Estimation of A, B, and C, D coefficients using EVI (V1)-NDVI (V2) The case III of VIs was EVI (V1)-NDVI (V2). A significant decrement was witnessed when the combination of EVI and NDVI was observed. The MWCM shows below the average R2 (0.62) value in four possible combinations of two VIs (see Table 2). Based on statistical tests, MWCM works with low efficiency because this combination has a low R2 value of 0.59 (see Table 2; Figure 2, c). The third combination of VIs was just opposite of case II, and from this condition, we have also concluded that V1 slightly supports the canopy background in MWCM. Fig. 2. Observed v/s simulated VV backscatter from the case I-IV. MWCM Parameterization I-IV: Estimation of A, B, and C, D coefficients using a = NDVI (V1)-NDVI (V2); b = NDVI (V1)-EVI (V2); c = EVI (V1)-NDVI (V2); d = EVI (V1)-EVI (V2). Rawat, K.S. et al. Hungarian Geographical Bulletin 69 (2020) (1) 17–26.24 MWCM Parameterization IV: Estimation of A, B, and C, D coefficients using EVI (V1)-EVI (V2) The case IV was EVI (V1)-EVI (V2). This com- bination gives a marginal improvement in the performance of MCM with the R2 of 0.63 for VV σ0 (see Table 2). There was a second highest preferable value of RMSE and R2 for VV σ0 in comparison to MWCM with different combinations of VIs (Figure 2, d). Therefore, we can infer that EVI depicts the wheat crop canopy in a better way for the V2 parameter in the MWCM. Evaluation of observed and estimated σ0 In this study, statistical tests showed that NDVI-EVI combination had the potential to provide good results with the agreement of nine statistical tests. The combination of NDVI-EVI as a combination of vegetation pa- rameters or canopy descriptor (V1-V2) showed the least RMSE of 0.81 dB between observed and predicted VV σ0 while highest R2 value of 0.98 among other VIs combination for ob- served and predicted VV σ0 (Table 3). The accuracy of retrieval of VVσ0 could be different when different combinations of VIs were chosen. Wang, L. et al. (2019) also found the accuracy of retrieval depends on the selection of VIs. The accuracy of retrieval of VVσ0 was high when the V1 replaced by NDVI and V2 by EVI (see Table 3). Also, when the V1 replaced by EVI and the V2 by NDVI, retrieval accuracy decreases, which means that canopy background influences the V1 parameter in MWCM while V2 may be cano- py background free. Because EVI is canopy background free while NDVI showed canopy as well as litter bit canopy background (soil) information, this analysis also revealed that any combination of VIs does not have much effect on soil coefficients C and D, because C and D depend entirely on soil properties (e.g., bulk density, soil texture, etc.) rather than vegetation properties. The C and D pa- rameters were fixed by using linear equation eq. 5. Therefore, for any combination of VIs, the value of C and D does not change much. It was found that the errors in vegetation or canopy descriptors were sensitive to errors in the retrieval of VV σ0 (Liu, C. and Shi, J. 2016). Conclusion In this study, WCM was modified using vegetation/canopy descriptor to simulate VVσ0. The current research focused on the parameterization of MWCM. In the current research work, a combination of vegetation indices and backscattering (VV) simulated Table 3. Statistical evaluation of estimated with respect observed VV backscatter (σ0), based on NDVI as crop canopy descriptor and on investigated coefficients Observed ENDVI-NDVI ENDVI-EVI EEVI-NDVI EEVI-EVI -27.24 -29.28 -26.86 -28.28 -25.28 -31.39 -29.93 -30.95 -30.93 -25.99 -25.24 -24.11 -25.21 -25.11 -24.70 -29.59 -27.49 -29.2 -28.49 -27.96 -31.88 -35.16 -31.91 -34.73 -33.66 -30.98 -33.98 -31.03 -32.98 -31.09 -36.88 -39.45 -38.89 -37.45 -36.22 Statistical performance measures NDVI-NDVI NDVI-EVI EVI-NDVI EVI-EVI R2 RMSE R-RMSE MAE NRMSE MAE SEE RMSE, % IR 0.83 2.34 0.08 0.89 -0.08 0.89 2.53 -1.10 1.03 0.98 0.81 0.02 0.12 -0.03 0.12 0.87 -0.38 1.00 0.90 1.46 0.05 0.68 -0.05 0.68 1.58 -0.69 1.02 0.75 2.38 0.08 1.19 -0.08 -1.19 2.57 -1.12 0.96 25Rawat, K.S. et al. Hungarian Geographical Bulletin 69 (2020) (1) 17–26. from Sentinel-1 was studied. The assumed hypothesis was that surface roughness dur- ing wheat crop period was constant. A to- tal of four combinations were tested for the comparison of VVσ0 to the observed VVσ0. Our results revealed that MWCM could be parameterized with NDVI and EVI as cano- py descriptors. The basis of optimization of A, B, C and D by reducing RMSE between MWCM predicted and Sentinel-1 observed VVσ0. The retrieval of VVσ0 converges to the correct (with good accuracy or free from er- rors) values of the vegetation or canopy de- scriptors. Acknowledgement: Authors KSR and SKS express thanks to Ministry of Earth Sciences (MoES), Govern- ment of India, India for providing the financial sup- port (MoES/16/13/2016-RDEAS) to carry out this research work. The author SS was supported by the Higher Education Institutional Excellence Programme (NKFIH-1150-6/2019) of the Ministry of Innovation and Technology in Hungary, within the framework of the 4th thematic programme of the University of Debrecen. R E F E R E N C E S Attema, E. and Ulaby, F.T. 1978. Vegetation modelled as a water cloud. 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