Comparison of Landsat 8 OLI and Landsat 7 ETM+ for estimating grassland LAI using model inversion and spectral indices: case study of Mpumalanga,South Africa

The leaf area index (LAI) is the key biophysical indicator used to assess the condition of rangeland. In this study, we investigated the implications of narrow spectral response, high radiometric resolution (12 bits), and higher signal-to-noise ratio of the Landsat 8 Operational Land Imager (OLI) sensor for the estimation of LAI. The Landsat 8 LAI estimates were compared to that of its predecessors, namely Landsat 7 Enhanced Thematic Mapper Plus (ETM+) (8 bits). Furthermore, we compared the radiative transfer model (RTM) and spectral indices approaches for estimating LAI on rangeland systems in South Africa. The RTM was inverted using artificial neural network (ANN) and lookup table (LUT) algorithms. The accuracy of the models was higher for Landsat 8 OLI, where ANN (root mean squared error, RMSE = 0. 13; R² = 0. 89), LUT (RMSE = 0. 25; R² = 0. 50), compared to Landsat 7 ETM+, where ANN (RMSE = 0. 35; R² = 0. 60), LUT (RMSE = 0. 38; R² = 0. 50). Compared to an empirical approach, the RTM provided higher accuracy. In conclusion, Landsat 8 OLI provides an improvement for the estimation of LAI over Landsat 7 ETM+. This is useful for rangeland monitoring.     

Assessing the efficacy of Landsat-8 OLI imagery derived models for remotely estimating chlorophyll-a concentration in the Upper Ganga River,India

ABSTRACT

Chlorophyll-a (chl-a) serves as an indicator of productivity in surface water. Estimating chl-a concentration is pivotal for monitoring and subsequent conservation of surface water quality. Artificial neural network (ANN) based models were validated and tested for their efficacy against various regression models to determine the chl-a concentration in the Upper Ganga river. Landsat-8 Operational Land Imager (OLI) surface reflectance (SR) imagery for May and October along with in-situ data over a period of 2 years (2016–2017) was used to develop and validated models. Regression model performance was acceptable with a coefficient of determination (R²) of 0.57, 0.63, 0.66 and 0.68 for linear, exponential, logarithmic and power model, respectively. However, there was a significant improvement in the efficacy of chl-a determination using ANN model performance having a root mean square error (RMSE) of 1.52 µg l^–1 and R² = 0.97 in comparison to the best-performing regression model (power) with RMSE = 9.86 µg l^–1 and R² = 0.68. ANN exhibited comparatively more precise spatial and seasonal variability with mean absolute error (MAE) of 1.26 µg l^–1 as compared to the best regression model (power) MAE = 7.98 µg l^–1 suggesting the applicability of ANN for large-scale spatial and temporal monitoring river stretches using Landsat-8 OLI SR images.     

Comparing Sentinel-2 MSI and Landsat 8 OLI in soil salinity detection: a case study of agricultural lands in coastal North Carolina

This study presents the first comparison of Landsat 8 Operational Land Imager (OLI) and Sentinel-2 Multispectral Instrument (MSI) in identifying soil salinity using soil physiochemical, spectral, statistical, and image analysis techniques. By the end of the century, intermediate sea level rise scenarios project approximately 1.3 meters of sea level rise along the coast of the southeastern United States. One of the most vulnerable areas is Hyde County, North Carolina, where 1140 km² of agricultural lands are being salinized, endangering 4,200 people and $40 million USD of property. To determine the best multispectral sensor to map the extent of salinization, this study compared the feasibility of OLI and MSI to estimate electrical conductivity (EC). The EC of field samples were correlated with handheld spectrometer spectra resampled into multispectral sensor bands. Using an iterative ordinary least squares regression, it was found that EC was sensitive to OLI bands 2 (452 nm – 512 nm) and 4 (636 nm – 673 nm) and MSI bands 2 (457.5 nm – 522.5 nm) and 4 (650 nm – 680 nm). Respectively, the R²_Adj and Root Mean Square Error (RMSE) of 0.04–0.54 and 1.15 for OLI, and 0.05–0.67 and 1.17 for MSI, suggests that the two sensors have similar salinity modelling skill. The extracted saline soils make up approximately 1,703 hectares for OLI and 118 hectares for MSI, indicating overestimation from the OLI image due to its coarser spatial resolution. Additionally, field samples indicate that nearby vegetated land is saline, indicating an underestimation of total impacted land. As sea levels rise, accurately monitoring soil salinization will be critical to protecting coastal agricultural lands. MSI’s spatial and temporal resolution makes it superior to OLI for salinity tracking though they have roughly equivalent spectral resolutions. This study demonstrates that visible spectral bands are sensitive to soil salinity with the Blue and Red spectral ranges producing the highest model accuracy; however, the low accuracies for both sensors indicate the need of narrowband sensors. The HyspIRI to be launched in the early 2020s by NASA may provide ideal data source in soil salinity studies.     

Bio‐optical properties and estimation of the optically active substances in Lake Tianmuhu in summer

Bio‐optical properties in an optically complex and biologically productive region of Lake Tianmuhu were determined in three cruises from June to August 2006. The concentrations of three optically active substances, tripton C _Tripton (calculated from total suspended matter and chlorophyll‐a (Chla) and phaeophytin‐a (Pa)), phytoplankton pigment C _Chla+Pa , and chromophoric dissolved organic matter (CDOM) a _CDOM(440), were predicted from the estimated irradiance reflectance based on in situ measurements and laboratory analyses. The total relative contributions of phytoplankton, tripton, CDOM and pure water over the range of photosynthetically active radiation (PAR) (400–700 nm) were 36.1%, 24.2%, 15.9% and 23.8%, respectively. The dominant contribution of phytoplankton to the total absorption was due to high phytoplankton pigment concentration. The range and variation in irradiance reflectance and diffuse attenuation coefficient derived from a bio‐optical model, based on inherent optical properties, compared well with the measured variability. A reasonably strong relationship (R² = 0.92) was observed between irradiance reflectance at 780 nm R(780) and C _Tripton. For our data set, the best algorithm for C _Chla+Pa used the three‐band reflectance model [R ^?1(688)?R ^?1(717)]×R(747). The a _CDOM(440) could be estimated using the ratio of irradiance reflectance R(682)/R(555). The retrieval accuracy (R²) of tripton, phytoplankton pigment and CDOM was 0.92, 0.87 and 0.91, respectively, while the rms. error was 0.90 mg l^?1 (18.2%), 3.27 µg l^?1 (14.8%) and 0.073 m^?1 (15.3%), respectively. Estimation of the concentrations of the three optically active substances was reasonably accurate based on inherent optical properties measurement.     

Monitoring algal blooms in drinking water reservoirs using the Landsat-8 Operational Land Imager

In this study, we demonstrated that the Landsat-8 Operational Land Imager (OLI) sensor is a powerful tool that can provide periodic and system-wide information on the condition of drinking water reservoirs. The OLI is a multispectral radiometer (30 m spatial resolution) that allows ecosystem observations at spatial and temporal scales that allow the environmental community and water managers another means to monitor changes in water quality not feasible with field-based monitoring. Using the provisional Land Surface Reflectance product and field-collected chlorophyll-a (chl-a) concentrations from drinking water monitoring programs in North Carolina and Rhode Island, we compared five established approaches for estimating chl-a concentrations using spectral data. We found that using the three band reflectance approach with a combination of OLI spectral bands 1, 3, and 5 produced the most promising results for accurately estimating chl-a concentrations in lakes (R² value of 0.66; root mean square error value of 8.9 µg l^?1). Using this model, we forecast the spatial and temporal variability of chl-a for Jordan Lake, a recreational and drinking water source in piedmont North Carolina and several small ponds that supply drinking water in southeastern Rhode Island.     

Chlorophyll content estimation in arid grasslands from Landsat-8 OLI data

In this study, an arid grassland was selected, and the chlorophyll content of the leaf and canopy level was estimated based on Landsat-8 Operational Land Imager (OLI) data using the PROSAIL radiative transfer (RT) model. Two vegetation indices (green chlorophyll index, CI_green, and greenness index, G) were selected to estimate the leaf and canopy chlorophyll content (LCC and CCC). By analysing the effect of soil background on the two indices, the LCC was divided into low and moderate-to-high levels. A different combination of the two indices was adopted at each level to improve the chlorophyll content estimation accuracy. The results suggested that the chlorophyll content estimated using the proposed method yielded a higher accuracy with coefficient of determination, R² = 0.84, root-mean-square error, RMSE = 9.67 μg cm^?2 for LCC and R² = 0.85, RMSE = 0.43 g m^?2 for CCC than that using CI_green alone with R² = 0.62, RMSE = 20.04 μg cm^?2 for LCC and R² = 0.85, RMSE = 0.71 g m^?2 for CCC. The results also confirmed the validity of this approach to estimate the chlorophyll content in arid areas.     

Stability of linear infinite-dimensional systems revisited

The paper is devoted to a study of stability questions for linear infinite-dimensional discrete-time and continuous-time systems. The concepts of power stability and l ^p Instability for a linear discrete-time system x _k+1 = Ax _k (where x _k ε X, X is a Banach space, A is linear and bounded) are introduced and studied. Relationships between these concepts and the inequality r(A) < 1, where r(A) denotes the spectral radius of A, are also given. The discrete-time results are used for a simple derivation of some well-known properties of exponentially stable and L^p-stable linear continuous-time systems described by [xdot](t) = Ax(t) (A generates here a strongly continuous semigroup of linear and bounded operators on X). Some remarks on norms related to stable systems are also included.     

Lake-area mapping in the Tibetan Plateau: an evaluation of data and methods

Lake area derived from remote-sensing data is a primary data source, because changes in lake number and area are sensitive indicators of climate change. These indicators are especially useful when the climate change is not convoluted with a signal from direct anthropogenic activities. The data used for lake-area mapping is important, to avoid introducing unnecessary uncertainty into long-term trends of lake-area estimates. The methods for identifying waterbodies from satellite data are closely linked to the quality and efficiency of surface-water differentiation. However, few studies have comprehensively considered the factors affecting the selection of data and methods for mapping lake area in the Tibetan Plateau (TP), nor of evaluating their consequences. This study tests the dominant data sets (Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) data) and the methods for automated waterbody mapping on 14 large lakes (>500 km²) distributed across different climate zones of the TP. Seasonal changes in lake area and data availability from Landsat imagery are evaluated. Data obtained in October is optimal because in this month the lake area is relatively stable. The data window can be extended to September and November if insufficient data is available in October. Grouping data into three-year bins decreases the effects of year-to-year seasonal variability and provides a long-term trend that is suitable for time series analysis. The Landsat data (Multispectral Scanner, MSS; Thematic Mapper, TM; Enhanced Thematic Mapper Plus, ETM+; and Operational Land Imager, OLI) and MODIS data (MOD09A1) showed good performance for lake-area mapping. The Otsu method is used to determine the optimal threshold for distinguishing water from non-water features. Several water extraction indices, namely NDWI_McFeeters, NDWI_Xu, and AWEI_non-shadow, yielded high overall classification accuracy (92%), kappa coefficient (0.83), and user’s accuracy (~90%) for lake-water classification using Landsat data. The MODIS data using NDWI_McFeeters and NDWI_Xu showed consistent lake area (r² = 0.99) compared with Landsat data on the corresponding date with root mean square error (RMSE) values of 86.87 and 103.33 km² and mean absolute error (MAE) values of 25.7 and 29.04 km², respectively. The MODIS data is suitable for great lake mapping, which is the case for the large lakes in the TP. Although automated water extraction indices exhibited high accuracy in separating water from non-water, visual examination and manual editing are still necessary. Combined with recent Chinese high-resolution satellites, these remotely sensed imageries will provide a wealth of data for studies of lake dynamics and long-term lake evolution in the TP.     

Mapping pasture biomass in Mongolia using Partial Least Squares,Random Forest regression and Landsat 8 imagery

The aim of this study was to develop a robust methodology to estimate pasture biomass across the huge land surface of Mongolia (1.56 × 10⁶ km²) using high-resolution Landsat 8 satellite data calibrated against field-measured biomass samples. Two widely used regression models were compared and adopted for this study: Partial Least Squares (PLS) and Random Forest (RF). Both methods were trained to predict pasture biomass using a total of 17 spectral indices derived from Landsat 8 multi-temporal satellite imagery as predictor variables. For training, reference biomass data from a field survey of 553 sites were available. PLS results showed a satisfactory correlation between field measured and estimated biomass with coefficient of determination (R²) = 0.750 and Root Mean Square Error (RMSE) = 101.10 kg ha^?1. The RF regression gave similar results with R² = 0.764, RMSE = 98.00 kg ha^?1. An examination of feature importance found the following vegetation indices to be the most relevant: Green Chlorophyll Index (CL_green), Simple Ratio (SR), Wide Dynamic Range Vegetation Index (WDRVI), Enhanced Vegetation Index EVI₁ and Normalized Difference Vegetation Index (NDVI) indices. With respect to the spectral reflectances, Red and Short Wavelength Infra-Red2 (SWIR₂) bands showed the strongest correlation with biomass. Using the developed PLS models, a spatial map of pasture biomass covering Mongolia at a spatial resolution of 30 m was generated. Our study confirms the high potential of RF and PLS regression (PLSR) models to predict pasture biomass. The computationally simpler PLSR model is preferred for applications involving large regions. This method can be implemented easily, provided that sufficient reference data and cloud-free observations are available.     

Volume estimation in a Eucalyptus plantation using multi-source remote sensing and digital terrain data: a case study in Minas Gerais State,Brazil

In this study, we tested the effectiveness of stand age, multispectral optical imagery obtained from the Landsat 8 Operational Land Imager (OLI), synthetic aperture radar (SAR) data acquired by the Sentinel-1B satellite, and digital terrain attributes extracted from a digital elevation model (DEM), in estimating forest volume in 351 plots in a 1,498 ha Eucalyptus plantation in northern Minas Gerais state, Brazil. A Random Forest (RF) machine learning algorithm was used following the Principal Component Analysis (PCA) of various data combinations, including multispectr al and SAR texture variables and DEM-based geomorphometric derivatives. Using multispectral, SAR or DEM variables alone (i.e. Experiments (ii)–(iv)) did not provide accurate estimates of volume (RMSE (Root Mean Square Error) > 32.00 m³ ha^?1) compared to predictions based on age since planting of Eucalyptus stands (Experiment (i)). However, when these datasets were individually combined with stand age (i.e. Experiments (v)–(vii)), the RF models resulted in better volume estimates than those obtained when using the individual multispectral, SAR and DEM datasets (RMSE < 28.00 m³ ha^?1). Furthermore, a model that integrated the selected variables of these data with stand age (Experiment (viii)) improved volume estimation significantly (RMSE = 22.33 m³ ha^?1). The large and increasing area of Eucalyptus forest plantations in Brazil and elsewhere suggests that this new approach to volume estimation has the potential to support Eucalyptus plantation monitoring and forest management practices.     

Hyperspectral field estimation and remote-sensing inversion of salt content in coastal saline soils of the Yellow River Delta

This study explored hyperspectral field and satellite-based remote sensing of soil salt content. Using Kenli County in the Yellow River Delta as the study area, in situ soil field spectra and satellite-based remote-sensing images were integrated with laboratory measurements of soil sample salinity to improve remote sensing-based soil salt estimation and inversion procedures. First, the narrow-band hyperspectral reflectance field data were used to model the wide-band reflectance data from Landsat 7. Second, the bands and spectral features sensitive to soil salt content were identified through correlation analysis and band combination. Stepwise multiple linear regression was used to find a best model, which was then inverted to predict soil salt content using remote-sensing images from Landsat 7 and Landsat 8. The applicability of the model was verified by ground-checking the inversion results. The results show that the bands sensitive to soil salinity are mainly in the visible and near-infrared (NIR) regions. Combining information from these bands can eliminate some background effects and significantly improve the correlation with salinity. The best model of soil salinity is y = 1.345 ? 25.898 × g_SWIR1 ? 245.440 × g_Red × (g_Red ? g_NIR) ? 0.252 × (g_Red + g_NIR)/(g_Red ? g_NIR) ? 19.563 × (g_Red ? g_SWIR1). This model has a coefficient of determination (R²) of 0.896, a verification R² of 0.867, a relative prediction deviation (RPD) of 2.135, and a root mean square error (RMSE) of 0.264. The model fits well and is highly stable. The inversion results based on Landsat 7 and Landsat 8 images are consistent with the actual situation of soil salinity in the study area. This study provides an effective and feasible method for the estimation of soil salt content in coastal regions based on field spectral measurements and remote-sensing inversion.     

Characterizing the spatiotemporal evolution of soil salinization in Hetao Irrigation District (China) using a remote sensing approach

ABSTRACT

Soil salinization is a major problem of land degradation in arid and semiarid irrigation districts. This study aims to characterize the spatiotemporal evolution of soil salinization in Hetao Irrigation District (HID) in Inner Mongolia, China, using Landsat Thematic Mapper/Enhanced Thematic Mapper Plus/Operational Land Imager datasets. Salty barren land and farmland are extracted using supervised classification. Then, we develop four integrated soil salinity models (ISSMs) to quantify the intensity of saline farmland. ISSMs are generated through deriving the parameters (E_VI-SIs), which integrate enhanced vegetation index (EVI) and Salinity Index-1 (SI1), EVI and Salinity Index-3 (SI3), Modified Soil Adjusted Vegetation Index (MSAVI) and SI1, and MSAVI and SI3, respectively, from the scatter plots of farmland soils with different salinity in four spectral feature spaces (SFSs). Exponential regression analyses reveal that the E_VI-SI from MSAVI-SI3 SFS has the best fit with in situ soil electrical conductivity measurements (R² = 0.74, root mean square error = 0.31 dS m^–1). Salty barren land clustered in the central and northeast of HID, while the area of salty barren land decreased during 1986–2016. After employing water-saving irrigation since 2000, saline farmland decreased and then remained relatively stable. This study indicates that the SFS integrating MSAVI and SI3 contains effective information for quantifying the saline farmland. Employing water-saving irrigation had a positive effect on controlling salinization.     

Retrieval of total suspended matter concentration from Gaofen-1 Wide Field Imager (WFI) multispectral imagery with the assistance of Terra MODIS in turbid water – case in Deep Bay

This study proposed a method for developing high spatial resolution Gaofen-1 satellite (GF-1) Wide Field Imager (WFI)-based total suspended matter concentration (C_TSM) retrieval model with the assistance of Moderate Resolution Imaging Spectroradiometer (MODIS) data, using the Deep Bay in China as a case. Based on long-term calibrated C_TSM measurements of optical backscatter (OBS) 3A turbidity and temperature monitoring system of two stationary stations from January 2007 through November 2008, 33 match-ups were selected to build an exponential retrieval model for MODIS atmospherically corrected remote-sensing reflectance (R_rs) ratio (R_rs,645/R_rs,555). Validation of the MODIS model showed well agreement with the seven in situ C_TSM measurements with a root mean squared error (RMSE) of 5.06 mg l^?1 and a coefficient of determination R² of 0.80. Aided with six MODIS retrieved C_TSM products, different band combinations (single band (R_rc,660), band subtraction (R_rc,660–R_rc,560), band ratio (R_rc,660/R_rc,560), and total suspended matter index at 660 nm band (TSMI₆₆₀) were evaluated for simultaneous GF-1 WFI Rayleigh-corrected reflectance (R_rc). The results showed that the exponential model based on the Rayleigh-corrected reflectance ratio (R_rc,660/R_rc,560) could achieve acceptable accuracy, with RMSE of 14.80 mg l^?1 and R² of 0.62. The proposed method would be helpful for dynamic monitoring in the Deep Bay, and more important could also provide an alternative approach for studies when in situ measurements are unreachable.     

Mapping crop cover using multi-temporal Landsat 8 OLI imagery

Crop classification maps are useful for estimating amounts of crops harvested, which could help address challenges in food security. Remote-sensing techniques are useful tools for generating crop maps. Optical remote sensing is one of the most attractive options because it offers vegetation indices (VIs) with frequent revisits and has adequate spatial and spectral resolution and some data has been distributed free of charge. However, sufficient consideration has not been given to the potential of VIs calculated from Landsat 8 Operational Land Imager (OLI) data. This article describes the use of Landsat 8 OLI data for the classification of crops in Hokkaido, Japan. In addition to reflectance, VIs calculated from simple formulas that consisted of combinations of two or more reflectance wavebands were evaluated, as well as the six components of the Kauth–Thomas transform. The VIs based on shortwave infrared bands (bands 6 or 7) improved classification accuracy, and using a combination of all derived data from Landsat 8 OLI data resulted in an overall accuracy of 94.5% (allocation disagreement = 4.492 and quantity disagreement = 1.017).     

Towards a practical remote-sensing model of suspended sediment concentrations in turbid waters using MERIS measurements

A new empirical index, termed the normalized suspended sediment index (NSSI), is proposed to predict total suspended sediment (TSS) concentrations in inland turbid waters using Medium Resolution Imaging Spectrometer (MERIS) full-resolution (FR) 300 m data. The algorithm is based on the normalized difference between two MERIS spectral bands, 560 and 760 nm. NSSI shows its potential in application to our study region – Poyang Lake – the largest freshwater lake in China. An exponential function (R² = 0.90, p < 0.01) accurately explained the variance in the in situ data and showed better performance for the TSS range 10–524 mg l^?1. The algorithm was then validated with TSS estimates using an atmospheric-corrected MERIS FR image. The validation showed that the NSSI algorithm was a more robust TSS algorithm than the band-ratio algorithms. Findings of this research imply that NSSI can be successfully used on MERIS images to obtain TSS in Poyang Lake. This work provided a practical remote-sensing approach to estimate TSS in the optically and hydrologically complex Poyang Lake and the method can be easily extended to other similar waters.     

Analysis of natural background and dredging-induced changes in TSM concentration from MERIS images near commercial harbours in the Estonian coastal sea

Medium Resolution Imaging Spectrometer (MERIS) products with 300 m resolution from 2006 to 2011 were used to evaluate the local background of total suspended matter (TSM) in the vicinity of commercial harbours located along the Estonian coastline in the Baltic Sea. The difference between background TSM maps (mainly influenced by spring bloom, cyanobacterial bloom, resuspension, and river inflow) and dredging period mean maps was used for the estimation of dredging-induced turbidity at the time of dredging operations. Validation of Case II Regional (C2 R) and Free University of Berlin (FUB) MERIS processors with point measurements showed that both processors represent the changes in TSM concentration adequately. C2 R processors showed better statistics (R² = 0.61, root mean square error = 0.82 mg l^–1, SD = 0.77 mg l^–1, mean bias = –0.28 mg l^–1) compared to the FUB processor. Analysis of monthly mean TSM maps revealed that the variability of TSM concentration, showing the resilience level of the local ecosystem, is very different along the Estonian coastline – varying between 0.75 and 2.60 mg l^–1 near the Port of Tallinn, located in the Gulf of Finland, and between 10.04 and 24.23 mg l^–1 near the Port of Pärnu, located in the Gulf of Riga. The viability of the method for dredging impact detection was tested by evaluating the dredging-induced turbidity on monthly mean TSM maps for the dredging period in autumn 2008 in Pakri Bay, which is an environmentally sensitive area. A threshold TSM concentration value of >2.26 mg l^–1 difference from background TSM was defined as a criterion for dredging impact detection for Pakri Bay. The area of dredging-induced turbidity was between 0.56 and 1.25 km² and did not reach the environmentally sensitive NATURA 2000 region adjoining Paldiski South Harbour.     

The potential for integrating Sentinel 2 MSI with SPOT 5 HRG and Landsat 8 OLI imagery for monitoring semi-arid savannah woody cover

Multitemporal archived imagery enables the monitoring of savannah woody cover, for ecological purposes. Compatibility in multitemporal, multiple sensor image data would facilitate the monitoring. The decommissioning of SPOT 5 (Système Pour l’Observation de la Terre 5) left a void in multispectral imagery at the 10 m spatial resolution of its high-resolution geometric (HRG) sensor. The subsequent launch of Sentinel 2 presented an opportunity for data continuity to monitor the savannah woody cover, using equivalent 10 m resolution multispectral instrument (MSI) bands. This study examined the integration potential of Sentinel 2 MSI with the longer archive HRG and Landsat 8 (Land Satellite 8) Operational Land Imager (OLI) imagery, in assessing savannah woody cover. Images of three semi-arid savannah sites acquired on same season dates that excluded herbaceous vegetation from the spectral signature were used: November 2014 (HRG) and December 2015 (MSI, OLI). Using equivalent green (G), red (R), and near infrared (NIR) bands at 10 m (MSI, HRG) and 30 m (OLI) resolution, the woody cover was mapped through subpixel classification. The mapped woody cover was compared for statistical differences using χ² analysis at 10 m resolution (MSI, HRG) and at a degradation of the MSI and HRG images to the 30 m OLI pixel size. Conversion to top-of-atmosphere reflectance values facilitated inter-sensor correlation of G, R, and NIR reflectance for field sampling sites where woody cover was quantified. Inter-sensor regression functions in G, R, and NIR band MSI and HRG images were developed. The 10 m resolution classifications of woody cover were not statistically different. Due to spatial resolution similarity, SPOT 5 HRG multispectral imagery was established as suitable for integration with equivalent band MSI imagery in mapping the woody cover in a multitemporal analysis. For dense woody cover, Landsat 8 OLI imagery was more suitable for integration with MSI than HRG images due to higher radiometric sensitivity, which can permit monitoring physiology-related woody reflectance.     

Defining the Landsat TM/ETM+ and CHRIS/PROBA spectral regions in which turbidity can be retrieved in inland waterbodies using field spectroscopy

This article describes the results obtained by an existing campaign in which in situ spectroradiometric measurements using a GER1500 field spectroradiometer, Secchi disk depth, and turbidity measurements (using a portable turbidity meter) were acquired at Asprokremmos Reservoir in Paphos District, Cyprus. Field spectroradiometric and water quality data span 18 sampling campaigns during the period May 2010–October 2010. By applying several regression analyses between ‘In-Band’ mean reflectance values against turbidity values for all spectral bands corresponding to Landsat TM/ETM+ (Bands 1 to 4) and CHRIS/PROBA (Bands A1 to A62), the highest correlation was found for Landsat TM/ETM+ Band 3 (R² = 0.85) and for CHRIS/PROBA Bands A30 to A32 (R² = 0.90).     

Estimation of chlorophyll-a concentration in coastal waters with HJ-1A HSI data using a three-band bio-optical model and validation

Accurate assessment of phytoplankton chlorophyll-a (chl-a) concentration in turbid waters by means of remote sensing is challenging because of the optical complexity of case 2 waters. We applied a bio-optical model of the form [R^–1(λ₁) – R^–1(λ₂)](λ₃), where R(λ_i) is the remote-sensing reflectance at wavelength λ_i, to estimate chl-a concentration in coastal waters. The objectives of this article are (1) to validate the three-band bio-optical model using a data set collected in coastal waters, (2) to evaluate the extent to which the three-band bio-optical model could be applied to the spectral radiometer (SR) ISI921VF-512T data and the hyperspectral imager (HSI) data on board the Chinese HJ-1A satellite, (3) to evaluate the application prospects of HJ-1A HSI data in case 2 waters chl-a concentration mapping. The three-band model was calibrated using three SR spectral bands (λ₁ = 664.9 nm, λ₂ = 706.54 nm, and λ₃ = 737.33 nm) and three HJ-1A HSI spectral bands (λ₁ = 637.725 nm, λ₂ = 711.495 nm, and λ₃ = 753.750 nm). We assessed the accuracy of chl-a prediction with 21 in situ sample plots. Chl-a predicted by SR data was strongly correlated with observed chl-a (R² = 0.93, root mean square error (RMSE) = 0.48 mg m^–3, coefficient of variation (CV) (RMSE/mean(chl-a_mea)) = 3.72%). Chl-a predicted by HJ-1A HSI data was also closely correlated with observed chl-a (R² = 0.78, RMSE = 0.45 mg m^–3, CV (RMSE/mean(chl-a_mea)) = 7.51%). These findings demonstrate that the HJ-1A HSI data are promising for quantitative monitoring of chl-a in coastal case-2 waters.     

Estimating the leaf area index in Indian tropical forests using Landsat-8 OLI data

Leaf area index (LAI) is a key vegetation biophysical parameter and is extensively used in modelling of phenology, primary production, light interception, evapotranspiration, carbon, and nitrogen dynamics. In the present study, we attempt to spatially characterize LAI for natural forests of Western Ghats India, using ground based and Landsat-8 Operational Land Imager (OLI) sensor satellite data. For this, 41 ground-based LAI measurements were carried out across a gradient of tropical forest types, viz. dry, moist, and evergreen forests using LAI-2200 plant canopy analyser, during the month of March 2015. Initially, measured LAI values were regressed with 15 spectral variables, including nine spectral vegetation indices (SVIs) and six Landsat-8 surface reflectance (ρ) variables using univariate correlation analysis. Results showed that the red (ρ_red), near-infrared (ρ_NIR), shortwave infrared (ρ_SWIR1, ρ_SWIR2) reflectance bands (R² > 0.6), and all SVIs (R² > 0.7) except simple ratio (SR) have the highest and second highest coefficient of determination with ground-measured LAI. In the second step, to select significant (high R², low root mean square error (RMSE), and p-level < 0.05) SVIs to determine the best representative model, stepwise multiple linear regression (SMLR) was implemented. The results indicate that the SMLR model predicted LAI with better coefficient of determination (R² = 0.83, RMSE = 0.78) using normalized difference vegetation index, enhanced vegetation index, and soil-adjusted vegetation index variables compared to the univariate approach. The predicted SMLR model was used to estimate a spatial map of LAI. It is desirable to evaluate the stability and potentiality of regional LAI models in natural forest ecosystems against the operationally accepted Moderate Resolution Imaging Spectroradiometer (MODIS) global LAI product. To do this, the Landsat-8 pixel-based LAI map was resampled to 1 km resolution and compared with the MODIS derived LAI map. Results suggested that Landsat-8 OLI-based VIs provide significant LAI maps at moderate resolution (30 m) as well as coarse resolution (1 km) for regional climate models.