Bathymetry retrieval from optical images with spatially distributed support vector machines

This paper presents a spatially distributed support vector machine (SVM) system for estimating shallow water bathymetry from optical satellite images. Unlike the traditional global models that make predictions from a unified global model for the entire study area, our system uses locally trained SVMs and spatially weighted votes to make predictions. By using IKONOS-2 multi-spectral image and airborne bathymetric LiDAR water depth samples, we developed a spatially distributed SVM system for bathymetry estimates. The distributed model outperformed the global SVM model in predicting bathymetry from optical satellite images, and it worked well at the scenarios with a low number of training data samples. The experiments showed the localized model reduced the bathymetry estimation error by 60% from RMSE of 1.23 m to 0.48 m. Different from the traditional global model that underestimates water depth near shore and overestimates water depth offshore, the spatially distributed SVM system did not produce regional prediction bias and its prediction residual exhibited a random pattern. Our model worked well even if the sample density was much lower: The model trained with 10% of the samples was still able to obtain similar prediction accuracy as the global SVM model with the full training set.     

The influence of wind speed on infrared temperature in impervious surface areas based on in situ measurement data

Wind perturbations can cause a relatively rapid decay in infrared temperature, thus resulting in abnormal spatial patterns of infrared temperature in urban areas and the subsequent reduction in the reliability of infrared temperature measurements. To increase the reliability of such measurements, the effects of wind speed must be evaluated and removed. However, studies on the quantitative estimation of wind speed effects on infrared temperature are limited. In this study, in situ infrared temperature measurements and synchronous meteorological data were used to evaluate the influence of wind speed on in situ infrared temperature measurements of impervious surfaces. Five different impervious surfaces were selected in this study. The technical schemes are proposed for quantitative estimation of wind speed effects: (1) the residual-based method from the diurnal temperature cycle model was proposed to estimate the infrared temperature decay (ITD) due to wind fluctuations; (2) quantile regression method was introduced to define the relationship between wind speed fluctuations and the ITD; and (3) An improved probabilistic prediction interval as well as a ratio method were developed to estimate the magnitude and duration of the ITD. The results indicated that relative extreme wind speed (EWS) was significantly correlated with the range of ITD over 5-min intervals; the hourly decay rate and impact duration of ITD varied with changes in relative wind speed and impervious surface type; and the impact duration of ITD increased with an increase in the relative EWS and lasted more than 1.3 h for the studied impervious surfaces. The above findlings provide us a guidance for in situ measurement of infrared temperature and could be utilized for correcting thermal infrared images.     

Developing water quality retrieval models with in situ hyperspectral data in Poyang Lake,China

Applying remote sensing techniques to develop the retrieval models and further to obtain the spatiotemporal information of water quality parameters is necessary for understanding, managing, and protecting lake ecosystems. This study aimed to calibrate and validate the retrieval models for estimating the concentrations of chlorophyll a (C_CHL), suspended particulate matter (C_SPM), and dissolved organic carbon (C_DOC) with the in situ hyperspectral measurements in Poyang Lake, China in 2010 and 2011. The model calibration and validation results indicated that: (1) for C_CHL retrieval, significantly strong and moderate correlations existed between the measured and estimated values (with the correlation coefficient r = 0.92 and r = 0.76) using the exponential model and the three-band model, respectively, with biased estimation observed for the exponential model; (2) for retrieving C_SPM, there was a strong correlation between the measured and estimated values (r = 0.95) using the exponential model; and (3) no significant correlation between measured and estimated C_DOC values was found with our developed models. More work is needed to allow the water quality of Poyang Lake to be accurately and steadily estimated, especially for C_CHL and C_DOC.     

Leaf to canopy upscaling approach affects the estimation of canopy traits

In remote sensing applications, leaf traits are often upscaled to canopy level using sunlit leaf samples collected from the upper canopy. The implicit assumption is that the top of canopy foliage material dominates canopy reflectance and the variability in leaf traits across the canopy is very small. However, the effect of different approaches of upscaling leaf traits to canopy level on model performance and estimation accuracy remains poorly understood. This is especially important in short or sparse canopies where foliage material from the lower canopy potentially contributes to the canopy reflectance. The principal aim of this study is to examine the effect of different approaches when upscaling leaf traits to canopy level on model performance and estimation accuracy using spectral measurements (in-situ canopy hyperspectral and simulated Sentinel-2 data) in short woody vegetation. To achieve this, we measured foliar nitrogen (N), leaf mass per area (LMA), foliar chlorophyll and carbon together with leaf area index (LAI) at three vertical canopy layers (lower, middle and upper) along the plant stem in a controlled laboratory environment. We then upscaled the leaf traits to canopy level by multiplying leaf traits by LAI based on different combinations of the three canopy layers. Concurrently, in-situ canopy reflectance was measured using an ASD FieldSpec-3 Pro FR spectrometer, and the canopy traits were related to in-situ spectral measurements using partial least square regression (PLSR). The PLSR models were cross-validated based on repeated k-fold, and the normalized root mean square errors (nRMSE_cv) obtained from each upscaling approach were compared using one-way analysis of variance (ANOVA) followed by Tukey’s post hoc test. Results of the study showed that leaf-to-canopy upscaling approaches that consider the contribution of leaf traits from the exposed upper canopy layer together with the shaded middle canopy layer yield significantly (p < 0.05) lower error (nRMSE_cv < 0.2 for canopy N, LMA and carbon) as well as high explained variance (R² > 0.71) for both in-situ hyperspectral and simulated Sentinel-2 data. The widely-used upscaling approach that considers only leaf traits from the upper illuminated canopy layer yielded a relatively high error (nRMSE_cv>0.2) and lower explained variance (R² < 0.71) for canopy N, LMA and carbon. In contrast, canopy chlorophyll upscaled based on leaf samples collected from the upper canopy and total canopy LAI exhibited a more accurate relationship with spectral measurements compared with other upscaling approaches. Results of this study demonstrate that leaf to canopy upscaling approaches have a profound effect on canopy traits estimation for both in-situ hyperspectral measurements and simulated Sentinel-2 data in short woody vegetation. These findings have implications for field sampling protocols of leaf traits measurement as well as upscaling leaf traits to canopy level especially in short and less foliated vegetation where leaves from the lower canopy contribute to the canopy reflectance.     

Assessment and validation of evapotranspiration using SEBAL algorithm and Lysimeter data of IARI agricultural farm,India

Evapotranspiration (ET) is a vital process in land surface atmosphere research. In this study, Surface Energy Balance Algorithm for Land (SEBAL) for the assessment of ET (for 23 December 2010, 8 January 2011, 24 January 2011, 9 February 2011, 25 February 2011, 29 March 2011 and 14 April 2011) from LANDSAT7-ETM+ and validation with Lysimeter data set is illustrated. It is based on the evaporative fraction concept, and it has been applied to LANDSAT7-ETM + (30 m resolution) data acquired over the Indian Agricultural Research Institute’s agricultural farm land. The ET from SEBAL was compared with Lysimeter ET using four statistical tests (root-mean-square error (RMSE), relative root-mean-square error (R-RMSE), mean absolute error (MAE), and normalized root-mean square error (NRMSE)), and each test showed a good correlation between the predicted and observed ET values. Results from this study revealed that the RMSE of crop-growing period was 0.51 mm d^?1 for ET_SEBAL, i.e. ET_SEBAL having good accuracy with respect to observed ET_Lysimeter. Results were also validated using R-RMSE test, which also proved that ET_SEBAL data are having good accuracy with respect to observed ET_Lysimeter as R-RMSE of crop-growing period is 0.19 mm d^?1. MAE (0.19), NRMSE (0.21) and r² (0.91) tests indicated that model prediction is significant, and model can be effectively used for the estimation of ET from SEBAL as input of remote sensing data sets. Finally, the SEBAL has been useful for remote agricultural land where ground-based data (Lysimeter data) are not available for daily ET (ET_24 h) estimation. The temporal study of the ET_24 h values analysed has revealed that the highest ET_24 h values are owing to the higher development (high greenness) of crops, whereas the lower values are related to the lower development (low greenness) or null crop.     

Medium resolution imaging spectrometer data for monitoring tropical coastal waters: a case study of Berau estuary,East Kalimantan,Indonesia

This article investigates the performance of MERIS reduced resolution data to monitor water quality parameters in the Berau estuary waters, Indonesia. Total suspended matter (TSM), Chlorophyll-a (Chl-a) concentration and diffuse attenuation coefficient (K_d ) were derived from MERIS data using three different algorithms for coastal waters: standard global processor (MERIS L2), C2R and FUB. The outcomes were compared to in situ measurements collected in 2007. MERIS data processed with C2R gave the best retrieval of Chl-a, while MERIS L2 performed the best for TSM retrieval, but large deviations from in situ data were observed, pointing at inversion problems over these tropical waters for all standard processors. Nevertheless, MERIS can be of use for monitoring equatorial coastal waters like the Berau estuary and reef system. Applying a K_d (490) local algorithm to the MERIS RR data over the study area showed a sufficient good correlation to the in situ measurements (R ² = 0.77).     

Artificial Neural Network (ANN) Based Inversion of Benthic Substrate Bottom Type and Bathymetry in Optically Shallow Waters - Initial Model Results

Ocean-colour remote sensing in optically shallow waters is influenced by contribution from the water column depth as well as by the substrate type. Therefore, it is required to include the contribution from the water column and substrate bottom type for bathymetry estimation. In this report we demonstrate the use of Artificial Neural Network (ANN) based approach to spectrally distinguish various benthic bottom types and estimate depth of substrate bottom simultaneously in optically shallow waters. We have used in-water radiative transfer simulation modeling to generate simulated top-of-the-water column reflectance the four major benthic bottom types viz. sea grass, coral sand, green algae and red algae using Hydrolight simulation model. The simulated remote sensing reflectance, for the four benthic bottom types having benthic bottom depth up to 30 m were generated for moderately clear waters. A multi-layer perceptron (MLP) type neural network was trained using the simulated data. ANN based approach was used for classification of the benthic bottom type and simultaneous inversion of bathymetry. Simulated data was inverted to yield benthic bottom type classification with an accuracy of ~98% for the four benthic substrate types and the substrate depth were estimated with an error of 0% for sea grass, 1% for coral sand and 1–3% for green and red algae up to 25 m, whereas for substrate bottom deeper than 25 m depth the classification errors increased by 2–5% for three substrate bottom types except sea grass bottom type. The initial results are promising which needs validation using the in-situ measured remote sensing reflectance spectra for implementing further on satellite data.     

Assessing Performance of Tomo-SAR and Backscattering Coefficient for Hemi-Boreal Forest Aboveground Biomass Estimation

Tomo-SAR technique has been used for hemi-boreal forest height and further forest biomass estimation through allometric equation. Backscattering coefficient especially in longer wavelength (L- or P-band) is thought as a useful parameter for hemi-boreal forest biomass retrieval. The aim of this paper is to assess the performance of vertical backscattering power and backscattering coefficient for hemi-boreal forest aboveground biomass (AGB) estimation with airborne P-band data. The test site locates in southern Sweden called Remningstorp test site, and the in-situ forest AGB ranges from 14 t/ha to 245 t/ha at stand level. Multi-baseline P-band Pol-InSAR data in repeat-path mode collected during March and May in 2007 at Remningstorp test site was used. We found that the correlation coefficient (R) between backscattering coefficient of P-band HH polarization and the in-situ forest biomass reached 0.87. The R for P-band VV backscattering power at 5 m is 0.71 and 10 m is 0.72. Backscattering coefficient in HH polarization and vertical backscattering power at 5 m and 10 m were applied to construct a model for hemi-boreal forest AGB estimation by backward step-wise regression and cross-validation approach. The results showed that the estimated forest AGB ranges from 19 to 240 t/ha, and the constructed model obtained a higher R and smaller RMSE, the value of R is 0.91, RMSE is 30.43 t/ha at Remningstorp test site.     

Soil organic carbon mapping using remote sensing techniques and multivariate regression model

Soil organic carbon (SOC) is an important aspect of soil quality and plays an imperative role in soil productivity in the agriculture ecosystems. The present study was applied to estimate the SOC stock using space-borne satellite data (Landsat 4–5 Thematic Mapper [TM]) and ground verification in the Medinipur Block, Paschim Medinipur District and West Bengal in India. In total, 50 soil samples were collected randomly from the region according to field surveys using a hand-held Global Positioning System (GPS) unit to estimate the surface SOC concentrations in the laboratory. Bare soil index (BSI) and normalized difference vegetation ndex (NDVI) were explored from TM data. The satellite data-derived indices were used to estimate spatial distribution of SOC using multivariate regression model. The regression analysis was performed to determine the relationship between SOC and spectral indices (NDVI and BSI) and compared the observed SOC (field measure) to predict SOC (estimated from satellite images). Goodness fit test was performed to determine the significance of the relationship between observed and predicted SOC at p ≤ 0.05 level. The results of regression analysis between observed SOC and NDVI values showed significant relationship (R² = 0.54; p < 0.0075). A significant statistical relationship (r = ?0.72) was also observed between SOC and BSI. Finally, our model showed nearly 71% of the variance of SOC distribution could be explained by SOC and NDVI values. The information from this study has advanced our understanding of the ongoing ecological development that affects SOC dissemination and might be valuable for effective soil management.     

Bathymetrical influences on spatial and temporal characteristics of chlorophyll-a concentrations in the Southern Ocean from 2002 to 2012 (October to March) using MODIS

Phytoplankton blooms, particularly in the Southern Ocean, can have significant impact on global biogeochemistry cycling. To investigate the accuracy of chlorophyll-a distribution, and to better understand the spatial and temporal dynamics of phytoplankton biomass, we examine chlorophyll-a estimates (October–March from 2002 to 2012) derived from Moderate Resolution Imaging Spectrometer (MODIS) data following the ocean chlorophyll-a 3 model (OC3M) algorithm. Noticeable seasonality occurs in the temporal distribution of chlorophyll-a concentrations, which shows the highest value in December and January and an increasing tendency during the 2002–2012 period. The spatial distribution of chlorophyll-a varies greatly with latitude, as higher latitudes experience more phytoplankton blooms (chlorophyll-a concentration larger than 1 mg/m³) and marginal seas (Ross Sea and Amundsen Sea) show different bloom anomalies caused by two dominant algae species. Areas at higher latitudes and shallow water (<500 m) experience the shorter ice-free periods with greater seasonality. A noticeable bathymetry gradient exists at 2500-m isobaths, while water at the 500–2500-m depth experiences quite long ice-free periods with a stable water environment. Blooms generally occur near topographic features where currents have strong interactions when the water depth is more than 2500 m. Based on these findings, we can classify the Southern Ocean into two bloom subregions, 0–500 m as an enhanced bloom zone (EBZ), and 500–2500 m as a moderate bloom zone (MBZ). The EBZ has a quite high-bloom probability of about 30%, while the MBZ has only 10%.     

Bathymetric mapping over coastal Andhra Pradesh using landsat Mss data

Satellite imagery can be used to map shallow water depth using techniques which utilize only a few selected depth sounding points as input. For the present work three different models were considered for water depth estimation, which relate different parameters with the water depth. The Landsat-4 MSS data of the sounding points were used for calculation of model parameters. Goodness of fit of the above models was tested using statistical tests, which showed that the exponential relation between water body radiance and water depth gives best fit for shallow water bathymetry. Contour map of water depths over the coastal region of Andhra Pradesh near Machlipattnam have been drawn with the help of the above models using remotely sensed data of the area.     

3-D visualizations of coastal bathymetry by utilization of airborne TOPSAR polarized data

Abstract

Multi-frequency C and L bands in the TOPSAR data have been utilized to reconstruct three-dimensional (3-D) bathymetry pattern. The main objective of this study is to utilize fuzzy arithmetic to reduce the errors arising from speckle in synthetic aperture radar (SAR) data when constructing ocean bathymetry from polarized SAR data. In doing so, two 3-D surface models, the Volterra algorithm and a fuzzy B-spline (FBS) algorithm, which construct a global topological structure between the data points, were used to support an approximation to the real surface. Volterra algorithm was used to express the non-linearity of TOPSAR data intensity gradient based on the action balance equation (ABC). In this context, a first-order kernel of Volterra algorithm was used to express ABC equation. The inverse of Volterra algorithm then performed to simulate 2-D current velocities from C_VV and L_HH band. Furthermore, the 2-D continuity equation then used to estimate the water depth. In order to reconstruct 3-D bathymetry pattern, the FBS has been performed to water depth information which was estimated from 2-D continuity equation. The best reconstruction of coastal bathymetry of the test site in Kuala Terengganu, Malaysia, was obtained with polarized L and C bands SAR acquired with HH and VV polarizations, respectively. With 10 m spatial resolution of TOPSAR data, bias of –0.004 m, the standard error mean of 0.023 m, r ² value of 0.95, and 90% confidence intervals in depth determination was obtained with L_HH band.     

Evaluation of machine learning models for rice dry biomass estimation and mapping using quad-source optical imagery

ABSTRACT

Several machine learning regression models have been advanced for the estimation of crop biophysical parameters with optical satellite imagery. However, literature on the comparative performances of such models is still limited in range and scope, especially under multiple data sources, despite the potential of multi-source imagery to improving crop monitoring in cloudy areas. To fill in this knowledge gap, this study explored the synergistic use of Landsat-8, Sentinel-2A, China’s environment and disaster monitoring and forecasting satellites (HJ-1 A and B) and Gaofen-1 (GF-1) data to evaluate four machine learning regression models that include Random Forest (RF), Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), and Gradient Boosting Decision Tree (GBDT), for rice dry biomass estimation and mapping. Taking a major rice cultivation area in southeast China as case study during the 2016 and 2017 growing seasons, a cross-calibrated time series of the Enhanced Vegetation Index (EVI) was obtained from the quad-source optical imagery and on which the aforementioned models were applied, respectively. Results indicate that in the before rice heading scenario, the most accurate dry biomass estimates were obtained by the GBDT model (R² of 0.82 and RMSE of 191.8 g/m²) followed by the RF model (R² of 0.79 and RMSE of 197.8 g/m²). After heading, the k-NN model performed best (R² of 0.43 and RMSE of 452.1 g/m²) followed by the RF model (R² of 0.42 and RMSE of 464.7 g/m²). Whist the k-NN model performed least in the before heading scenario, SVM performed least in the after heading scenario. These findings may suggest that machine learning regression models based on an ensemble of decision trees (RF and GBDT) are more suitable for the estimation of rice dry biomass, at least with optical satellite imagery. Studies that would extend the evaluation of these machine learning models, to other parameters like leaf area index, and to microwave imagery, are hereby recommended.     

Comparative evaluation of horizontal accuracy of elevations of selected ground control points from ASTER and SRTM DEM with respect to CARTOSAT-1 DEM: a case study of Shahjahanpur district,Uttar Pradesh,India

Digital elevation model (DEM) data of Shuttle Radar Topography Mission (SRTM) are distributed at a horizontal resolution of 90 m (30 m only for US) for the world, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) DEM data provide 30 m horizontal resolution, while CARTOSAT-1 (IRS-P5) gives 2.6 m horizontal resolution for global coverage. SRTM and ASTER data are available freely but 2.6 m CARTOSAT-1 data are costly. Hence, through this study, we found out a horizontal accuracy for selected ground control points (GCPs) from SRTM and ASTER with respect to CARTOSAT-1 DEM to implement this result (observed from horizontal accuracy) for those areas where the 2.6-m horizontal resolution data are not available. In addition to this, the present study helps in providing a benchmark against which the future DEM products (with horizontal resolution less than CARTOSAT-1) with respect to CARTOSAT-1 DEM can be evaluated. The original SRTM image contained voids that were represented digitally as ?140; such voids were initially filled using the measured values of elevation for obtaining accurate DEM. Horizontal accuracy analysis between SRTM- and ASTER-derived DEMs with respect to CARTOSAT-1 (IRS-P5) DEM allowed a qualitative assessment of the horizontal component of the error, and the appropriable statistical measures were used to estimate their horizontal accuracies. The horizontal accuracy for ASTER and SRTM DEM with respect to CARTOSAT-1 were evaluated using the root mean square error (RMSE) and relative root mean square error (R-RMSE). The results from this study revealed that the average RMSE of 20 selected GCPs was 2.17 for SRTM and 2.817 for ASTER, which are also validated using R-RMSE test which proves that SRTM data have good horizontal accuracy than ASTER with respect to CARTOSAT-1 because the average R-RMSE of 20 GCPs was 3.7 × 10^?4 and 5.3 × 10^?4 for SRTM and ASTER, respectively.     

A comparison of geographic datasets and field measurements to model soil carbon using random forests and stepwise regressions (British Columbia,Canada)

We used geographic datasets and field measurements to examine the mechanisms that affect soil carbon (SC) storage for 65 grazed and non-grazed pastures in southern interior grasslands of British Columbia, Canada. Stepwise linear regression (SR) modeling was compared with random forest (RF) modeling. Models produced with SR performed better than those produced using RF models (r² = 0.56–0.77 AIC = 0.16–0.30 for SR models; r² = 0.38–0.53 and AIC = 0.18–0.30 for RF models). The factors most significant when predicting SC were elevation, precipitation, and the normalized difference vegetation index (NDVI). NDVI was evaluated at two scales using: (1) the MOD 13Q1 (250 m/16-day resolution) NDVI data product from the moderate resolution imaging spectro-radiometer (MODIS) (NDVI_MODIS), and (2) a handheld multispectral radiometer (MSR, 1 m resolution) (NDVI_MSR) in order to understand the potential for increasing model accuracy by increasing the spatial resolution of the gridded geographic datasets. When NDVI_MSR data were used to predict SC, the percentage of the variance explained by the model was greater than for models that relied on NDVI_MODIS data (r² = 0.68 for SC for non-grazed systems, modeled with SR based on NDVI_MODIS data; r² = 0.77 for SC for non-grazed systems, modeled with SR based on NDVI_MSR data). The outcomes of this study provide the groundwork for effective monitoring of SC using geographic datasets to enable a carbon offset program for the ranching industry.     

CatBoost模型在水深反演中的应用

在多光谱遥感水深反演研究中,由于影响反演精度的因素较多,传统的水深反演模型具有一定局限性。机器学习算法在解决非线性高复杂问题上较有优势,将其应用在某些特定区域水深反演可提高反演精度。本文利用Sentinel-2多光谱遥感影像和LiDAR测深数据,以瓦胡岛为研究区域,构建CatBoost水深反演模型,与传统水深反演模型及Boosting中的XGBoost和LightGBM模型的反演精度进行比较。试验结果表明,经过参数优化后的CatBoost水深反演模型的决定系数、均方根误差、平均绝对误差和平均相对误差分别为96.19%、1.09 m、0.77 m和9.61%,准确性最高,效果更佳。     

Biomass estimation of Sonneratia caseolaris (l.) Engler at a coastal area of Hai Phong city (Vietnam) using ALOS-2 PALSAR imagery and GIS-based multi-layer perceptron neural networks

This study tested the use of machine learning techniques for the estimation of above-ground biomass (AGB) of Sonneratia caseolaris in a coastal area of Hai Phong city, Vietnam. We employed a GIS database and multi-layer perceptron neural networks (MLPNN) to build and verify an AGB model, drawing upon data from a survey of 1508 mangrove trees in 18 sampling plots and ALOS-2 PALSAR imagery. We assessed the model’s performance using root-mean-square error, mean absolute error, coefficient of determination (R²), and leave-one-out cross-validation. We also compared the model’s usability with four machine learning techniques: support vector regression, radial basis function neural networks, Gaussian process, and random forest. The MLPNN model performed well and outperformed the machine learning techniques. The MLPNN model-estimated AGB ranged between 2.78 and 298.95 Mg ha^?1 (average = 55.8 Mg ha^?1); below-ground biomass ranged between 4.06 and 436.47 Mg ha^?1 (average = 81.47 Mg ha^?1), and total carbon stock ranged between 3.22 and 345.65 Mg C ha^?1 (average = 64.52 Mg C ha^?1). We conclude that ALOS-2 PALSAR data can be accurately used with MLPNN models for estimating mangrove forest biomass in tropical areas.     

Understanding satellite-derived bathymetry using Sentinel 2 imagery and spatial prediction models

ABSTRACT

Optical satellite data is an efficient and complementary method to hydrographic surveys for deriving bathymetry in shallow coastal waters. Empirical approaches (in particular, the models of Stumpf and Lyzenga) provide a practical methodology to derive bathymetric information from remote sensing. Recent studies, however, have focused on enhancing the performance of such empirical approaches by extending them via spatial information. In this study, the relationship between multibeam depth and Sentinel-2 image bands was analyzed in an optically complex environment using the spatial predictor of kriging with an external drift (KED), where its external drift component was estimated: a) by a ratio of log-transformed bands based on Stumpf’s model (KED_S) and b) by a log-linear transform based on Lyzenga’s model (KED_L). Through the calibration of KED models, the study objectives were: 1) to better understand the empirical relationship between Sentinel-2 multispectral satellite reflectance and depth, 2) to test the robustness of KED to derive bathymetry in a multitemporal series of Sentinel-2 images and multibeam data, and 3) to compare the performance of KED against the existing non-spatial models described by Stumpf et al. and Lyzenga. Results showed that KED could improve prediction accuracy with a decrease in RMSE of 89% and 88%, and an increase in R² of 27% and 14%, over the Stumpf and Lyzenga models, respectively. The decrease in RMSE provides a worthwhile improvement in accuracy, where results showed effective prediction of depth up to 6 m. However, the presence of higher concentrations of suspended materials, especially river plumes, can reduce this threshold to 4 m. As would be expected, prediction accuracy could be improved through the removal of outliers, which were mainly located in the channel of the river, areas influenced by the river plume, abrupt topography, but also very shallow areas close to the shoreline. These areas have been identified as conflictive zones where satellite-derived bathymetry can be compromised.     

Comparing the spectral settings of the new generation broad and narrow band sensors in estimating biomass of native grasses grown under different management practices

The challenge of assessing and monitoring the influence of rangeland management practices on grassland productivity has been hampered in southern Africa, due to the lack of cheap earth observation facilities. This study, therefore, sought to evaluate the capability of the newly launched Sentinel 2 multispectral imager (MSI) data, in relation to Hyperspectral infrared imager (HyspIRI) data in estimating grass biomass subjected to different management practices, namely, burning, mowing and fertilizer application. Using sparse partial least squares regression (SPLSR), results showed that HyspIRI data exhibited slightly higher grass biomass estimation accuracies (RMSE = 6.65 g/m², R² = 0.69) than Sentinel 2 MSI (RMSE = 6.79 g/m², R² = 0.58) across all rangeland management practices. Student t-test results then showed that Sentinel 2 MSI exhibited a comparable performance to HyspIRI in estimating the biomass of grasslands under burning, mowing and fertilizer application. In comparing the RMSEs derived using wave bands and vegetation indices of HyspIRI and Sentinel, no statistically significant differences were exhibited (α = 0.05). Sentinel (Bands 5, 6 and 7) and HyspIRI (Bands 730 nm, 740 nm, 750 nm, 710 nm), as well as their derived vegetation indices, yielded the highest predictive accuracies. These findings illustrate that the accuracy of Sentinel 2 MSI data in estimating grass biomass is acceptable when compared with HyspIRI. The findings of this work provide an insight into the prospects of large-scale grass biomass modeling and prediction, using cheap and readily available multispectral data.     

Development of digital bathymetry maps for Lakes Azuei and Enriquillo using sonar and remote sensing techniques

This article presents an improved algorithm for optimization and development of a digital bathymetric model (DBM) for Lake Azuei (LA) (Haiti) and Lake Enriquillo (LE) (Dominican Republic) using the ANUDEM method. Both sonar data and contour lines of the lakes’ layout extracted using Landsat imagery were compiled for bathymetry development. We show that the performance of the ANUDEM method was strongly dependent on the density and irregularity of the spatial distribution of the data. Changing the resolution of the output grids and deriving auxiliary topographically corrected contours improved the ANUDEM performance and minimized the systematic errors of the method. Statistical analysis showed no significant difference between measured and interpolated depths, characterized by a root mean square error of less than 0.71 m (LE) and 0.96 m (LA), and a median difference of ?0.009 m (LE) and 0.012 m (LA) on average. The shape reliability analysis revealed a relationship between accuracy and the depth and slope values, increasing in the center of the lakes where they are deep and the slope is gentle.