Double Bounce Research Articles

Hybrid Naïve Bayes Gaussian mixture models and SAR polarimetry based automatic flooded vegetation studies using PALSAR-2 data

Flood mapping using Synthetic Aperture Radar (SAR) data impose limitations in fully distinguishing flood under vegetation due to false double bounce returns from inundated tree trunks along with seasonal heterogeneities devised from changing land cover settings. In addition, rapid mapping of flooded vegetation is challenging during near real time applications. In this paper a fully automatic novel supervised classification approach called polarimetric Naïve Bayes is proposed that combines polarimetric information with series of Gaussian mixture models in Naïve Bayes framework to detect various flooded vegetation classes. It also allows the user to choose class configuration and eliminates creation of Region of Interest (ROI) for supervised training. The proposed approach uses scattering information from pre monsoon PolSAR dataset in training step to create ROIs for buildings and other features. In the next step series of Gaussian Mixtures are used for density estimation for different features in Bayesian multiclass problem. The newly developed classifier applied on 2016 Assam flood event resulted in precise mapping of at least three different vegetation classes under flood such as submerged vegetation, wetlands and floating vegetation. Under optimal class configuration, the approach showed better performance compared to other supervised techniques applied on the same data set such as MLE, Mahalanobis, Minimum Euclidean distance, and SVM classifications in delineating flood, submerged vegetation, wetlands and floating vegetation with Producer’s Accuracy of 98.6%, 81.1%, 94% and 51.5% respectively and combined Overall accuracy of 95.5% for flooded vegetation class. This method also detected multiple vegetation classes with better accuracy compared to similar methods.

The influence of surface canopy water on L-band backscatter from corn: A study combining detailed In situ data and the Tor Vergata radiative transfer model

The presence, duration, and amount of surface canopy water (SCW) is important in microwave remote sensing for agricultural applications. Our current understanding of the effect of SCW on total backscatter and the underlying mechanisms is limited. The aim of this study is to investigate the effect of SCW on backscatter as a function of frequency and polarization, and to understand the underlying mechanisms. For this purpose, the radiative transfer model developed at the Tor Vergata University was used to simulate the total backscatter at L-, C-, and X-band. First, simulations from the standard Tor Vergata model were compared to L-band observations. Then, two additional implementations of the model were developed to account for the effect of SCW and the presence of water on the soil surface on radar backscatter. Representing SCW by the inclusion of additional water in the vegetation leads to an increase in vegetation volume scattering and a reduction in the contribution from double bounce and direct scattering from the ground. This increases total backscatter, particularly at lower frequencies. Results suggest that the difference between backscatter in the presence and absence of SCW can be up to around 2.5 dB in L-band and likely less at higher frequencies. The effect of water on the canopy (SCW) reaches its maximum during the mid and late season as the crop reached its maximum biomass. The influence of dew on the reflectivity of the soil surface resulted in a difference of up to 3.8 dB between backscatter in the presence and absence of SCW. In particular, at low frequencies and low vegetation cover, the presence of water on the soil surface needs to be taken into account to correctly capture the sub-daily dynamics in backscatter. The findings of this study are relevant for current and future SAR missions including Sentinel-1, ROSE-L, NISAR, SAOCOM, ALOS, CosmoSkyMed, TerraSAR-X, TanDEM-X and constellations such as those of ICEYE, and Capella which have dawn/dusk overpasses or multiple overpasses per day.

Extraction and Analysis of Radar Scatterer Attributes for PAZ SAR by Combining Time Series InSAR, PolSAR, and Land Use Measurements

Extracting meaningful attributes of radar scatterers from SAR images, PAZ in our case, facilitates a better understanding of SAR data and physical interpretation of deformation processes. The attribute categories and attribute extraction method are not yet thoroughly investigated. Therefore, this study recognizes three attribute categories: geometric, physical, and land-use attributes, and aims to design a new scheme to extract these attributes of every coherent radar scatterer. Specifically, we propose to obtain geometric information and its dynamics over time of the radar scatterers using time series InSAR (interferometric SAR) techniques, with SAR images in HH and VV separately. As all InSAR observations are relative in time and space, we convert the radar scatterers in HH and VV to a common reference system by applying a spatial reference alignment method. Regarding the physical attributes of the radar scatterers, we first employ a Random Forest classification method to categorize scatterers in terms of scattering mechanisms (including surface, low-, high-volume, and double bounce scattering), and then assign the scattering mechanism to every radar scatterer. We propose using a land-use product (i.e., TOP10NL data for our case) to create reliable labeled samples for training and validation. In addition, the radar scatterers can inherit land-use attributes from the TOP10NL data. We demonstrate this new scheme with 30 Spanish PAZ SAR images in HH and VV acquired between 2019 and 2021, covering an area in the province of Friesland, the Netherlands, and analyze the extracted attributes for data and deformation interpretation.

General Five-Component Scattering Power Decomposition with Unitary Transformation (G5U) of Coherency Matrix

The polarimetric synthetic aperture radar (PolSAR) provides us with a two-by-two scattering matrix data set. The ensemble averaged coherency matrix in an imaging window derived using a scattering matrix has all non-zero elements in its three-by-three matrix. It is a full 3 × 3 matrix that bears nine real-valued and independent polarimetric parameters inside. In the proposed decomposition method, G5U, we preprocess observed coherency matrix [T] by using two consecutive unitary transformations to become an ideal form for five-component decomposition. The transformation reduces nine parameters to seven, which is the best fit for five-component scattering model expansion. We can retrieve five powers corresponding to surface scattering, double bounce scattering, volume scattering, oriented dipole scattering, and compound dipole scattering, directly. These powers can be calculated easily and used to display superb polarimetric RBG images as never before, and are further applicable for polarimetric calibration, classification, validation, etc.

Backscattering Statistics of Indoor Full-Polarization Scatterometric and Synthetic Aperture Radar Measurements of a Rice Field

The backscattering coefficient σ0 of a rice field is closely related to the amplitude, power, and phase of its radar backscattered signals. An investigation of the statistics of indoor full-polarization scatterometric and synthetic aperture radar (SAR) measurements on rice fields in the Laboratory of Target Microwave Properties (LAMP) is implemented in terms of the amplitude, power, and phase difference of backscattered signals. The validity and accuracy of LAMP measured data are studied and confirmed for the first time. The Rayleigh fading model and phase difference statistical model are both validated by the experimental data. Continuous microwave spectrum is obtained after spatial and frequency averaging over N independent scatterometric samples and full-polarization images are generated by applying a focusing algorithm to the SAR data. Comparisons between scatterometric results and SAR images with three resolutions of rice field scene are conducted with respect to amplitude and co-pol phase difference (CPD) statistics, as well as backscattering coefficients. The results show that the measured statistics of a rice field scene are in good agreement with those calculated by theoretical formulas. Spatial and frequency averaging of scatterometric data can increase N and thus improve the estimation accuracy of the backscattering coefficients. SAR images show a shift to the near range due to the intrinsic height of the rice plants and the probable existence of the double bounce scattering between vertical rice stems and the water surface considering the measurement geometry. The measured amplitude statistics of the SAR images approach a Rayleigh distribution with reduction of the resolution cell size while the size has little effect on the CPD statistics. The differences between backscattering coefficients extracted from the scatterometric data and SAR images confirm a 1-dB calibration accuracy in power of the LAMP measurement system.

Potential of L- and C- Bands Polarimetric SAR Data for Monitoring Soil Moisture over Forested Sites

This study investigates the potential of L- and C- bands Polarimetric Synthetic Aperture Radar (PolSAR) data to monitor soil moisture over the forested sites of SMAP Validation Experiment 2012 (SMAPVEX12). The optimal backscattering coefficients and polarimetric parameters to characterize the soil moisture were determined based on L-band Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR), C-band RADARSAT-2, and ground measurements composed of soil and vegetation parameters collected during SMAPVEX12. Linear and circular backscattering coefficients (σ0) and polarimetric parameters such as correlation coefficients (ρHHVV) and phase difference (φHHVV) between HH and VV, pedestal height (PH), entropy (H), anisotropy (A), α angle, surface (Ps), and double bounce (Pd) powers were used to develop the relationships with soil moisture. The analysis of these relationships shows that over the forested sites of SMAPVEX12: (a) at L-band several optimal backscattering coefficients and polarimetric parameters allow the monitoring of soil moisture, particularly the linear and circular σ0 (r = 0.60–0.96), Ps (r = 0.59–0.84), Pd (r = 0.60–0.82), ρHHHV_30°, ρVVHV_30°, φHHHV_30° and φHHVV_30° (r = 0.56–0.81). However, compared to the results obtained with σ0, there is no added value of the polarimetric parameters for soil moisture retrievals. (b) at C-band, only a few polarimetric parameters φHHHV, φVVHV, and φHHVV are correlated with soil moisture (r = ~0.90). They can contribute to soil moisture retrievals over forested sites when L-band data are not available.

Combining SAR images with land cover products for rapid urban flood mapping

Synthetic Aperture Radar (SAR) is an indispensable source of data for mapping and monitoring flood hazards, thanks to its ability to image the Earth’s surface in all weather conditions and at all times. Through cloud computing platforms such as Google Earth Engine (GEE), SAR imagery can be used in near-real time for rapid flood mapping. This has facilitated the disaster response community to make informed decisions in flood hazard interventions and management plans. However, rapid urban flood mapping using SAR is challenging, due to the complex land cover configuration in urban environments, coupled with complicated backscattering mechanisms. Here, we propose a novel method to utilise SAR imagery and land use-land cover (LULC) products for rapid urban flood mapping. Our approach uses a Land Cover Product to segment the study area into LULC types and differentiate each type with respect to whether double bounce is expected to occur during the flooding events. The normalised difference index was derived using a multi-temporal SAR image stack, and the threshold segmentation method was adopted for flood mapping. In addition, DEM and Surface Water datasets were employed to refine the flood extraction results using a morphological correction approach. We assessed the method quantitatively using two use cases: the 2017 Houston and 2022 Coraki flood events. Based on fine spatial resolution optical imagery, the proposed method achieved an accuracy of 92.7% for the August 2017 Houston flood mapping task and 89% for the March 2022 Coraki flood mapping task, which not only represents at least 13% in accuracy compared to non-LCP based flood extraction method, but also provides strong capability for rapid flood mapping in urban settings.

Monitoring Crops Using Compact Polarimetry and the RADARSAT Constellation Mission

The RADARSAT Constellation Mission (RCM) can acquire imagery in Compact Polarimetric (CP) mode. With this new mode, and the increased revisit with three satellites, RCM can contribute to operational crop monitoring at national scales. The four Stokes (S0, S1, S2 and S3) and three m-chi decomposition (surface, double bounce, volume) parameters were used to identify crops (pasture/forage, barley, wheat, canola, flaxseed, peas, lentils) with a Random Forest classifier. The Stokes and m-chi parameters delivered maps of similar accuracies (95% overall accuracy) and were only slightly less accurate than a classification using optical satellite imagery (97%). To understand why Stokes parameters worked well in classifying crops, scattering responses for wheat, canola, lentils and peas were plotted on the Poincaré sphere. These responses were interpreted in the context of the degree of polarization and were related to crop phenology. These plots revealed that early and late in the season the polarized component of the scattered wave remained circular. However, in the active season when crop structure was changing, scattered waves became more elliptically polarized. Although the amount of polarized scattering was lower mid-season, the change in ellipticity was helpful in separating crop types.

Scattering power decomposition-based analysis for target identification

Reflection and non reflection symmetry conditions can be fitted in the scattering models to optimize the coherence power estimation. The SAR formed from received wave is in the form of images which captures the physical feature of the target under consideration. To cohersely extract the target characters prominently the co polarization parameters are considered in the design model. In this investigation a new modified approach with the co polarization parameters is developed. The scattering component and the associated power components computed from the design model illustrate quantitatively a better confidence in evaluating the target. The components namely surface power component, volume power components and double bounce power components are obtained from the scattering parameters. The formulated algorithms model used in decomposition technique gives positive scattering powers. The technique is applied on C band RADARSAT2 images and simulated using a system level mathematical model.

Investigation into the Trampoline Dynamic Characteristics and Analysis of Double Bounce Vibrations.

Double bounce is an unusual and potentially very hazardous phenomenon that most trampoline users may have experienced, yet few would have really understood how and why it occurs. This paper provides an in-depth investigation into the double bounce. Firstly, the static and dynamic characteristics of a recreational trampoline are analysed theoretically and verified through experiments. Then, based on the developed trampoline dynamic model, double bounce simulation is conducted with two medicine balls released with different time delays. Through simulation, the process of double bounce is presented in detail, which comprehensively reveals how energy is transferred between users during double bounce. Furthermore, the effect of release time delay on double bounce is also presented. Finally, we conducted an experiment which produced similar results to the simulation and validated the reliability of the trampoline dynamic model and double bounce theoretical analysis.

Spatial heterogeneity for urban built-up footprint and its characterization using microwave remote sensing

The study focuses on the spatial heterogeneity and footprint analysis of Greater Hyderabad Metropolitan Corporation (GHMC), India using local indicators of spatial association (LISA) and grey level co-occurrence matrix (GLCM) based on Sentinel 1A/B Synthetic Aperture Radar (SAR) satellite observations. The study demonstrated the dominance of homogeneous patches (>30% of GHMC; 21.9% growth) in central parts, while high growth of heterogeneous patches (75.3% growth) was observed in peri-urban regions during 2015–2020 elucidating the implications of zoning regulations inside the municipal corporation. The Getis-Ord index-based hotspot analysis accentuated the hotspots of urban structures and observed its moderately high growth (19.6%) over the 5 years (2015-2020). A latitudinal spatial pattern of the built-up density was observed with the dominance of very high density in the southern parts, while low density in the northern parts. The coupling of LISA and GLCM based built-up footprint analysis represents the increasing influence of built-up areas primarily in GHMC (287.9 km2 to 343.7 km2) compared to peri-urban regions (103.7 km2 to 193.9 km2) and characterized by corner reflection and double bounce scattering of microwave signals. The study highlights the structural components of an urban environment with the type and growth of homogeneous and heterogeneous urban surfaces over the years. The finding of the study underlines the potential of LISA and GLCM for the evaluation of urban heterogeneity and built-up extraction using SAR and thus effectively supports in regulating the landscape management of urban and peri-urban regions.

A Detection Method for Collapsed Buildings Combining Post-Earthquake High-Resolution Optical and Synthetic Aperture Radar Images

The detection of collapsed buildings based on post-earthquake remote sensing images is conducive to eliminating the dependence on pre-earthquake data, which is of great significance to carry out emergency response in time. The difficulties in obtaining or lack of elevation information, as strong evidence to determine whether buildings collapse or not, is the main challenge in the practical application of this method. On the one hand, the introduction of double bounce features in synthetic aperture radar (SAR) images are helpful to judge whether buildings collapse or not. On the other hand, because SAR images are limited by imaging mechanisms, it is necessary to introduce spatial details in optical images as supplements in the detection of collapsed buildings. Therefore, a detection method for collapsed buildings combining post-earthquake high-resolution optical and SAR images was proposed by mining complementary information between traditional visual features and double bounce features from multi-source data. In this method, a strategy of optical and SAR object set extraction based on an inscribed center (OpticalandSAR-ObjectsExtraction) was first put forward to extract a unified optical-SAR object set. Based on this, a quantitative representation of collapse semantic knowledge in double bounce (DoubleBounceCollapseSemantic) was designed to bridge a semantic gap between double bounce and collapse features of buildings. Ultimately, the final detection results were obtained based on the improved active learning support vector machines (SVMs). The multi-group experimental results of post-earthquake multi-source images show that the overall accuracy (OA) and the detection accuracy for collapsed buildings (Pcb) of the proposed method can reach more than 82.39% and 75.47%. Therefore, the proposed method is significantly superior to many advanced methods for comparison.

Ray-optics study of gentle non-conformal texture morphologies for perovskite/silicon tandems.

We investigate gentle front side textures for perovskite/silicon tandem solar cells. These textures enhance the absorption of sunlight, yet are sufficiently gentle to allow deposition of an efficient perovskite top cell. We present a tandem solar cell with such gentle texture, fabricated by Kaneka corporation, with an efficiency as high as 28.6%. We perform an extensive ray-optics study, exploring non-conformal textures at the front and rear side of the perovskite layer. Our results reveal that a gentle texture with steepness of only 23° can be more optically efficient than conventional textures with more than double that steepness. We also show that the observed anti-reflective effect of such gentle textures is not based a double bounce, but on light trapping by total internal reflection. As a result, the optical effects of the encapsulation layers play an important role, and have to be accounted for when evaluating the texture design for perovskite/silicon tandems.

ACE-OT: Polarimetric SAR Data-Based Amplitude Contrast Enhancement Algorithm for Offset Tracking Applications

The use of polarimetric SAR data can improve the performance of persistent scatterer interferometry (PSI). However, its huge potential remains locked for the amplitude information based offset tracking (OT) technology. For example, to the best knowledge of the authors, there is no single example of a polarization based image optimization method that has been developed for OT processing. In this paper, an amplitude contrast enhancement algorithm (ACE) is introduced, which demonstrates the potential of the polarimetric SAR data on the improvement of OT performance. Its core idea is finding the optimal combination of the different scattering mechanisms for each pixel to improve the contrast. Firstly, the orientation of the reflected polarization ellipse is removed, to avoid the influence of the geometric relationship between the antenna and the target, and the properties of the target. Then three similarity parameters are defined to represent the three basic reflection types of the single bounce, the double bounce, and the random reflection. After that, the optimizing equation is constructed with two optimizing vectors. Finally, the optimizing vectors are calculated to obtain the enhanced amplitude image. Three examples of the enhancement are presented with different PolSAR images sets of both full- (Radarsat-2) and dual-polarization (TerraSAR-X and Sentinel-1). The performance of ACE-OT has been compared with another method, the Adaptive Histogram Enhancement (AHE). The impact of the number of polarization channels available on ACE-OT performance has also been studied.

Using the Pauli Scattering Mechanisms for Analysis of Polarimetric SAR Images from RADARSAT Constellation and TanDEM-X Missions

This work describes techniques for extraction of information in polarimetric images from RADARSAT constellation mission (RCM) and TanDEM-X (TDX) and TerraSAR-X (TSX) satellites. Using the boundary conditions of Maxwell's equations it is shown how the single bounce and double bounce scattering can be extracted from quad-pol and compact polarimetric (CP) SAR. We develop new equations to calculate the Pauli decomposition from simulated CP data using RCM quad-pol data. The RCM CP option is circular transmit, linear receive (CTLR). We find that using quad-pol data performs better than using CP data for Pauli decomposition, especially extraction of the HV component. Estimation of single bounce and double bounce scattering from RCM CP data is feasible and yields more information about scattering properties than the CP components alone. We show how more information can be extracted from bistatic TSX/TDX quad-pol data by estimation of the coherence of the different Pauli components and optimization of the interferometric coherence using ships as detection objects. This is a unique opportunity to use optimized coherence on moving ships for characterizing their structure.

Performance Analysis of Ocean Eddy Detection and Identification by L-Band Compact Polarimetric Synthetic Aperture Radar

The automatic detection and analysis of ocean eddies has become a popular research topic in physical oceanography during the last few decades. Compact polarimetric synthetic aperture radar (CP SAR), an emerging polarimetric SAR system, can simultaneously acquire richer polarization information of the target and achieve large bandwidth observations. It has inherent advantages in ocean observation and is bound to become an ideal data source for ocean eddy observation and research. In this study, we simulated the CP data with L-band ALOS PALSAR fully polarimetric data. We assessed the detection and classification potential of ocean eddies from CP SAR by analyzing 50 CP features for 2 types of ocean eddies (“black”and “white”) based on the Euclidean distance and further carried out eddy detection and eddy information extraction experiments. The results showed that among the 50 CP features, the dihedral component power (Pd), shannon entropy (SEI), double bounce (Dbl), Stokes parameters (g0 and g3), eigenvalue (l1), lambda, RVoG parameter (ms), shannon entropy (SE), surface scattering component (Ps), and σHH all performed better for detecting “white” eddies. Moreover, the H-A combination parameter (1mHA), entropy, shannon entropy (SEP, SEI, and SE), probability (p2), polarization degree (m), anisotropy, probability (p1), double bounce (Dbl), H-A combination parameter (H1mA), circular polarization ratio (CPR), and σVV were better CP features for detecting “black” eddies.

Robust retrieval of soil moisture at field scale across wide-ranging SAR incidence angles for soybean, wheat, forage, oat and grass

Surface soil moisture is estimated by inverting physical scattering models for low- crops using L-band airborne SAR data over the incidence angle range from 30 to 50°. The forward simulation is accurate with 1.7, 1.8, and 2.3 dB rmse for grass, wheat (including oat and forage), and soybean respectively assessed over 38 fields and during the full growth period for soybean. Dominant scattering mechanisms revealed by the model are found consistent across the incidence angle range: surface (VV) and double bounce (HH) for grass, double bounce for both polarization of wheat, and surface (young plant) and volume (mature plant) for both polarization of soybean. The model fidelity is robust across incidence angles between 30 and 50°, benefiting from rigorously simulating the three scattering mechanisms and successfully simulating the effect of vegetation and roughness.The soil moisture estimates are accurate to unbiased rmse of 0.041, 0.059 and 0.060 m3/m3 and correlation of 0.71, 0.83, and 0.69 for grass, wheat, and soybean fields, respectively over the soil moisture dynamic range up to 0.5 m3/m3, and soybean's full growth cycle. Vegetation water content and surface roughness are retrieved simultaneously to the soil moisture solutions, thus constraining the inversion of soil moisture. The retrieval performance is fairly uniform across the incidence angle, partly because of the reliable forward modeling across the incidence angle and also thanks to the reliable retrieval strategy. The robustness against the incidence angle supports frequent global mapping.The use of both HH and VV offers superior soil moisture retrievals than single-channel VV or HH input perfoms, by 0.034 m3/m3 unbiased rmse for grass and by 0.01 m3/m3 for wheat and soybean. The VV-based retrieval performed better than HH for grass and soybean, where surface scattering was important, but either choice resulted in similar retrieval accuracy for double-bounce dominanted wheat. No need for quad-pol and multi-angular observations would allow the retrieval approach to be applicable for spaceborne global mapping.

Evaluation of different machine learning algorithms for pearl millet discrimination using multi-sensor SAR data

This study aims to assess the potential of multi-temporal Sentinel-1 and RADARSAT-2 data for pearl millet discrimination using various classifiers—machine learning and knowledge-based decision tree (m-DT and k-DT), Support Vector Machine (SVM), and Random Forest (RF). Results show that the RF classifier outperformed the other classifiers in terms of overall accuracy (OA) and kappa coefficient. The highest Overall Accuracy (OA) of 89.1% and kappa coefficients of 0.822 and 0.814 were achieved by the RF classifier for both the Sentinel-1 and RADARSAT-2 data, respectively. The Variable Importance (VI) of the RF classifier revealed that the polarimetric parameters—volume scattering, double bounce, entropy and alpha angle were crucial for pearl millet discrimination. Polarimetric parameters, when ingested with the RF machine learning classifier, achieved better classification accuracies.

Eigen vector-based classification of pearl millet crop in presence of other similar structured (sorghum and maize) crops using fully polarimetric Radarsat-2 SAR data

The effect of structure and phenology of dryland crops on SAR scattering mechanisms is under explored and under reported. This study aims to understand the complex scattering mechanisms occurring in similar structured crops and to discriminate pearl-millet crop using single date Radarsat-2 SAR data. The study arrived at few significant findings that the panicle stage isthe critical stage for discriminating pearl-millet from similar structured sorghum and maize crops. Alpha angle extracted from eigen-vector based decomposition was identified as the discriminating factor. Average alpha value almost reached 55° due to dihedral scattering occurring in the prominent panicles which emerge early in pearl millet compared to maize and sorghum. Wishart H-A-α technique discriminated pearl millet, maize and sorghum crops with a producer's accuracy of about 87.8%, 71.6% and 71.4%, respectively. Paddy was classified with highest accuracy of 95.8% due to occurrence of dominant double bounce scattering in most of the fields.

Lake water level variability determination from SAR backscatter of discrete objects, GNSS levelling and satellite altimetry

In the absence of enough gauging stations for monitoring lake water level, three (3) methods are presented in this research: (1) Synthetic Aperture Radar (SAR) backscatter from discrete object; (2) Global Navigation Satellite System (GNSS) levelling; and (3) satellite altimetry. The results showed that the Laguna Lake water level is not homogeneous but instead varies at different locations. The GNSS leveling showed that water level varies from −0.64 to 1.26 m from water gauge data. Using the fish pen guard house as discrete object for double bounce scattering a correlation of 0.86 was computed between the gauge data and sigma 0 the equation derived through linear regression was used to predict water level from SAR images at different periods. The satellite altimeter data correlated with water level from graduated staff at 0.97 with a difference of about 0.29 m. It also showed an increasing water level trend from north to south.