Remote sensing techniques to predict salinity intrusion: application for a data-poor area of the coastal Mekong Delta,Vietnam

ABSTRACT

Salinity intrusion is a complex issue in coastal and estuarine areas. Currently, remote sensing techniques have been widely used to monitor water quality changes, ranging from inland river networks to deep oceans. The Vietnamese Mekong Delta is an important rice-growing area, and intrusion of saline water into irrigated freshwater-based agriculture areas is one of the most crucial constraints for agriculture development. This study aimed at building a numerical model to realize the salinity intrusion through the relationship between reflectance from the Landsat-8 Operational Land Imager images and salinity levels measured in situ. A total of 103 observed samples were divided into 50% training and 50% test. Multiple Linear Regression, Decision Trees and Random Forest (RF) approaches were applied in the study. The result showed that the RF approach was the best model to estimate salinity along the coastal river network in the study area. However, the large samples size needed was a significant challenge to circumscribe predicting ability of the RF model. The reflectance has a good correlation with salinity when locations (latitude–longitude) of salinity measured stations were added as a parameter of the Step-wise model with R-square 77.48% in training and 74.16% in test while Root Mean Square Error was smaller than 3.     

Using hyperspectral data to quantify water-quality parameters in the Wabash River and its tributaries,Indiana

A hand-held spectrometer was used to collect above-water spectral measurements for measuring optically active water-quality characteristics of the Wabash River and its tributaries in Indiana. Water sampling was undertaken concurrent with spectral measurements to estimate concentrations of chlorophyll (chl) and total suspended solids (TSS). A method for removing sky and Sun glint from field spectra for turbid inland waters was developed and tested. Empirical models were then developed using the corrected field spectra and in situ chl and TSS data. A subset of the field measurements was used for model development and the rest for model validation. Spectral characteristics indicative of waters dominated by different inherent optical properties (IOPs) were identified and used as the basis of selecting bands for empirical model development. It was found that the ratio of the reflectance peak at the red edge (704 nm) with the local minimum caused by chl absorption at 677 nm was a strong predictor of chl concentrations (coefficient of determination (R²) = 0.95). The reflectance peak at 704 nm was also a good predictor for TSS estimation (R² = 0.75). In addition, we also found that reflectance within the near-infrared (NIR) wavelengths (700–890 nm) all showed a strong correlation (0.85–0.91) with TSS concentrations and generated robust models. Results suggest that hyperspectral information provided by field spectrometer can be used to distinguish and quantify water-quality parameters under complex IOP conditions.     

Using hyperspectral remote sensing to estimate chlorophyll‐a and phycocyanin in a mesotrophic reservoir

This research estimates phytoplankton pigment concentrations (chlorophyll‐a (chl‐a) and phycocyanin (PC)) from hyperspectral Airborne Imaging Spectrometer for Applications (AISA) imagery. AISA images were acquired for a meso‐eutrophic reservoir in Central Indiana, USA. Concurrent with the airborne image acquisition, in situ water samples and reflectances were collected. The water samples were subsequently analysed for pigment concentrations, and in situ measured reflectance spectra were used for calibrating the AISA images. Spectral indices, derived from the AISA reflectance spectra, were regressed against the measured pigment concentrations to derive algorithms for estimating chl‐a and PC. The relationship between the pigment concentrations and the spectral indices were analysed and evaluated. The results indicate that the highest correlation occurred between chl‐a and a near‐infrared to red ratio (coefficient of determination R ²?=?0.78) and between PC and the reflectance trough at 628 nm (R ²?=?0.80). The relationship between PC and the reflectance at 628 nm provides an approach to the estimation of cyanobacteria concentration from hyperspectral imagery, which facilitates water‐quality authorities or management agencies in making well‐informed management decisions.     

Assessment of grassland degradation near Lake Qinghai,West China,using Landsat TM and in situ reflectance spectra data

The severity of grassland degradation near Lake Qinghai, West China was assessed from a Landsat Thematic Mapper (TM) image in conjunction with in situ samples of per cent grass cover and proportion (by weight) of unpalatable grasses (PUG) collected over 1?m² sampling plots. Spectral reflectance at each sampling plot was measured with a spectrometer and its location determined with a Global Positioning System (GPS) receiver. After radiometric calibration, the TM image was geometrically rectified. Ten vegetation indices were derived from TM bands 3 and 4, and from the spectral reflectance data at wavelengths corresponding most closely to those of TM3 and TM4. Regression analyses showed that NDVI and SAVI are the most reliable indicators of grass cover and PUG, respectively. Significant relationships between TM bands-derived indices and in situ sampled grass parameters were established only after the former had been calibrated with in situ reflectance spectra data. Through the established regression models the TM image was converted into maps of grass cover parameters. These maps were merged to form a degradation map at an accuracy of 91.7%. It was concluded that TM imagery, in conjunction with in situ grass samples and reflectance spectra data, enabled the efficient and accurate assessment of grassland degradation inside the study area.     

Atmospheric correction of high-spatial-resolution satellite images with adjacency effects: application to EO-1 ALI data

Adjacency effects are an interesting physical phenomenon caused by multiple scattering between the atmosphere and the surface. It is necessary to remove adjacency effects in the surface reflectance retrieved from satellite data at a high spatial resolution. In this study, we propose an atmospheric correction method with adjacency effect correction to derive surface reflectance from Earth Observing-1 (EO-1) Advanced Land Imager (ALI) data. Adjacency effects are corrected using an atmospheric point spread function. An analytical expression of the atmospheric point spread function is presented based on a single scattering approximation. This method was applied to ALI imagery acquired through Watershed Airborne Telemetry Experimental Research (WATER) on 20 May 2008. Compared with the surface reflectance before the adjacency effects were corrected for, the surface reflectance after correction exhibited increased between-pixel contrast. Furthermore, the discrepancies between the surface reflectance before and after corrections decreased from the blue band to the shortwave infrared band.     

An algorithm for the retrieval of suspended sediment concentrations in the Irish Sea from SeaWiFS ocean colour satellite imagery

A study was conducted in the Irish Sea with the aim of deriving an algorithm for the retrieval of suspended sediment concentrations from ocean colour imagery obtained from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS). In situ observations of the diffuse attenuation coefficient, K_d , and irradiance reflectance, R, were obtained at wavelengths coincident with the SeaWiFS visible wavebands using a Profiling Reflectance Radiometer (PRR600, Biospherical Instruments Inc., San Diego). Results showed that surface reflectance at 665 nm (R₆₆₅ ), rather than variations in the intrinsic colour of the ocean (using colour ratios), was the most widely applicable method of obtaining suspended sediment concentrations from ocean colour imagery in this region. The derived algorithm enabled the estimation of mineral suspended sediment (MSS) concentrations from ocean colour in the Irish Sea, accurate to within 1 mg l^?1 (see equation below). Furthermore, the application of this algorithm to a SeaWiFS image of the Irish Sea accurately reproduced known regions of high turbidity with realistic MSS concentrations.

MSS=0.0441R ² ₆₆₅ + 1.1392R ₆₆₅ + 1.7459

(R ²=0.9105, n=124, RMS error=0.907)

Specific absorption and scattering coefficients were derived for all optically active in-water constituents, namely yellow substance (YS), mineral suspended sediments (MSS) and phytoplankton pigments (C). An optical model based on the empirically derived absorption and scattering coefficients reproduced the observed relationship between MSS and R ₆₆₅. Model results highlighted the relative insensitivity of reflectance at 665 nm to variations in the concentrations of other in-water constituents, suggesting that the algorithm may be applicable to the Irish Sea throughout the year.     

Mapping concentrations of surface water quality parameters using a novel remote sensing and artificial intelligence framework

The deterioration of surface water quality occurs due to the presence of various types of pollutants generated from human, agricultural, and industrial activities. Thus, mapping concentrations of different surface water quality parameters (SWQPs), such as turbidity, total suspended solids (TSS), chemical oxygen demand (COD), biological oxygen demand (BOD), and dissolved oxygen (DO), is indeed critical for providing the appropriate treatment to the affected waterbodies. Traditionally, concentrations of SWQPs have been measured through intensive field work. Additionally, quite a lot of studies have attempted to retrieve concentrations of SWQPs from satellite images using regression-based methods. However, the relationship between SWQPs and satellite data is complex to be modelled accurately by using regression-based methods. Therefore, our study attempts to develop an artificial intelligence modelling method for mapping concentrations of both optical and non-optical SWQPs. In this context, a remote-sensing framework based on the back-propagation neural network (BPNN) is developed for the first time to quantify concentrations of different SWQPs from the Landsat8 satellite imagery. Compared to other methods, such as Support Vector Machine, significant coefficients of determination (R²) between the Landsat8 surface reflectance and concentrations of SWQPs were obtained using the developed Landsat8-based-BPNN models. The resulting R² values were 0.991, 0.933, 0.937, 0.930, and 0.934 for turbidity, TSS, COD, BOD, and DO, respectively. Indeed, these findings indicate that the developed Landsat8-based-BPNN framework is capable of developing highly accurate models for retrieving concentrations of different SWQPs from the Landsat8 imagery.     

Remote sensing assessment of sediment re-suspension during Hurricane Frances in Apalachicola Bay, USA

Assessments of hurricane-induced environmental impacts are important to coastal management and risk analysis of ecosystems. In this study, a previously-developed remote sensing model for non-hurricane conditions by Wang et al. [Wang, H. Q., Hladik, C. M., Milla, K., Huang, W. R., Edmiston, L., Harwell, M. A., & Schalles, J. F. (in press). Detecting and mapping water quality indicators in Apalachicola Bay, Florida using MODIS Terra 250-m imagery. International Journal of Remote Sensing] has been substantially enhanced to investigate the impact of Hurricane Frances on total suspended solid (TSS) concentrations in Apalachicola Bay, Florida, USA. The remote sensing model uses 250-m Moderate Resolution Imaging Spectroradiometer (MODIS) to map TSS concentrations in the Bay. Eleven additional satellite imageries of MODIS were used in the model improvement and calibration. TSS concentration computation in the present model has been substantially improved by using a two-step process: firstly producing atmospheric correction intercept by an approach of in-water reflectance regression, and then building the regression model (R² = 0.8534, n = 25) between 250-m MODIS reflectance and observed TSS concentrations, which includes an extreme high TSS concentration data of 208 mg/L for severe storm or hurricane condition. Also, we carried out the validation of model (RMSE = 5.5 mg/L, n = 21). MODIS-derived TSS maps show substantial increases of TSS concentrations in the Bay during the passage of Hurricane Frances (the average TSS and maximum concentration about 54.3 mg/L and 165 mg/L in the Bay respectively) compared to under no-storm or -hurricane condition ( the average TSS and maximum concentration were approximately 24-27 mg/L and 58-64 mg/L). In comparison to those before and 5-days after the passage of the hurricane, the average TSS concentration in the Bay was twice higher while the maximum TSS concentration increased almost three times during the hurricane. This indicates that strong winds during the hurricane have caused strong sediment re-suspension. The spatial variations of TSS concentrations were analyzed by applying the hydrodynamic characteristics of wind-induced flow and tidal currents as described by Huang [Huang, W., Jones, K., & Wu, T. (2002). Modeling surface wind effects on subtidal salinity in Apalachicola Bay. Estuarine, Coastal and Shelf Science, 55(1), 33−46; Huang, W., Sun, H., Nnaji, S., & Jones, K. (2002). Tidal hydrodynamics in a multiple inlet estuary: Apalachicola Bay. International Journal of Coastal Research, 18(4), 674−684], which show westward currents in the Bay under westward wind condition. Therefore, the southwestward wind (about 50° from the north) during the hurricane induced southwestward currents and transport that resulted in the high TSS concentrations near West Pass in the Bay and the Gulf. Within the Bay, TSS concentrations were generally higher in the southern portion of the Bay, which was due mainly to transport by the combination of southwestward wind and southward residual flow from the Apalachicola River.     

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.     

Red tide detection in the Strait of Hormuz (east of the Persian Gulf) using MODIS fluorescence data

A monstrous red tide appeared on October 2008 and expanded to the west on November 2008 off the Iranian coasts in the Hormuz Strait (east of the Persian Gulf). MODIS satellite data, hydrographic and bio-optical field measurements were used to detect the red tide. MODIS fluorescence line height (FLH in w m^?2 μm^?1 sr^?1) data showed the highest correlation with near-concurrent in situ chlorophyll concentration of 0.74 (100(FLH))^1.23 (r?=?0.9, n?=?44). In contrast, the band-ratio Chlorophyll product of MODIS showed more inconsistency with in situ chlorophyll data due to the interference of other water constituents. High FLH value patches >0.18 were confirmed to be located at the medium to high (10⁴–10⁶ cells l^?1) concentrations of Cochlodinium polykrikoides, and also showed a chlorophyll anomaly >1 mg m^?3, which means the potential of red tide occurrences. The FLH imagery also showed that the bloom started in early September along Bandar-Abbas port, and developed and moved to the west along the coastal regions. The results revealed that MODIS FLH and enhanced RGB (ERGB) imagery plus in situ data are adequate tools for red tide monitoring.     

A novel technique to detect turbid water and mask clouds in Greenland fjords

The existing National Aeronautics and Space Administration (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS) MOD35_L2 cloud mask performance was assessed using imagery of Kangerlussuaq Fjord, Greenland. It was found to perform suboptimally, especially near glacially fed river mouths, due to sediment-laden water being highly reflective in near-infrared wavelengths. In situ observations of suspended sediment concentration (SSC) were compared against MODIS band 1 and 2 reflectance and show a reflectance saturation effect past which increasing SSC values fail to increase the reflectance of water. A new mask optimized for turbid waters uses the reflectance saturation effect observed in high-SSC water. This new mask and a custom adaption of individual tests within the existing MOD35_L2 cloud mask were tested. The new mask outperforms the standard MOD35_L2 mask and the combination of new and custom masks was shown to screen out clouds very well in Greenland fjords. It is thought that with local measurements of MODIS band 1 and 2 reflectance values of turbid water to serve as training data, this mask should perform equally well in other turbid coastal waters.     

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.     

Incorporating legacy soil data to minimize errors in salinity change detection: a case study of Darab Plain,Iran

The results of a 1990 soil survey of a salinized region in Darab Plain, southern Iran, were combined with soil sampling data taken in 2002 from the same locations and employed as a basis for salinity change detection in the region. New preprocessing of satellite imagery was used, along with statistical analysis of the digital number (DN)?salinity relationship, in order to determine salinization of the area. Removal of outliers on the basis of interfering land uses improved the correlations. Nonlinear regression (NLR) in the form y?=?a +?bx ^α provided a suitable predictor of salinity (y, dS m^?1) for both 1990 and 2002 based on DNs (x). Among the 12 tested methods of salinity classification in this study, the six salinity class method with intervals 0–4, 4–10, 10–32, 32–64, 64–80 and >80 dS m^–1 was selected. A series of accuracy assessments through a trial-and-error procedure was the basis of the selection of the best method and led to a final accuracy of 91%. About 42% of the lands located on ‘no saline’ and ‘low salinity’ classes in 1990 had changed to the ‘medium’, ‘very high’ and ‘new agricultural land’ classes in 2002.     

Coupling remote sensing data with in-situ optical measurements to estimate suspended particulate matter under the Evros river influence (North-East Aegean sea,Greece)

ABSTRACT

Monitoring the riverine output of Suspended Particulate Matter (SPM) distribution in marine embayment is a crucial factor for the water quality of neighbouring coastal regions. This study presents satellite-derived SPM calculations against in-situ measurements in the continental shelf of North-East Aegean surrounding the transboundary Evros river mouth. Surface SPM, Inherent Optical Properties (IOPs) and remote sensing reflectance (R_rs) data were collected in a field campaign during low river discharge period (June 2016). The relationship between the optical backscattering coefficient (b_bp) and the in-situ SPM concentrations was investigated. Subsequently, an empirical single band model was applied for estimating SPM concentrations by using the Landsat-8 Operational Land Imager (L8/OLI) red band and the model was then locally tuned within the study area. Furthermore, a multi-band SPM-retrieval algorithm was developed using the in-situ surface reflectance R_rs for calibration and it was validated using the Leave-One-Out Cross Validation technique (LOOCV). The relationship between in-situ SPM and backscattering coefficient values showed good proportionality, thus, nominating the predominance of terrestrial mineral particles. Validation against field measurements indicated that the SPM concentrations derived from the newly-developed multi-band algorithm had an improved significance correlation (96%), compared to both the single band model (not-tuned) (coefficient of determination, R² = 0.82) and its locally tuned version (R² = 0.83). Most importantly, the generated multi-band model apart from exhibiting the best performance (R² = 0.93), it revealed high SPM spots which were not detected by the locally tuned single band model, indicating additional processes originating from river outflows, coastal erosion and subaqueous thermal springs in the area. In contrast, the locally tuned single band model overestimated SPM values in offshore waters, where low concentrations are encountered under the influence of the clear Black Sea Water (BSW).     

A new regional algorithm for estimating chlorophyll-a in the Alboran Sea (Mediterranean Sea) from MODIS-Aqua satellite imagery

The utility of three different algorithms for retrieving surface chlorophyll-a values from satellite images of MODIS-Aqua is tested in the northern Alboran Sea. The available global algorithm to calculate chlorophyll-a from reflectance of MODIS-Aqua (OC3M) overestimates the surface chlorophyll-a in the study area. Another regional algorithm specifically developed for the Mediterranean Sea (MedOC3) improves the estimates although the best outcome is obtained with OC5, which was developed for Atlantic coastal waters. The three tested algorithms perform worse at in situ chlorophyll-a concentrations higher than 1 mg m^?3 and exhibit uncertainty levels higher than 35% for this range of concentrations. A new algorithm (ALBOC3) is proposed which produces a good estimation of the in situ chlorophyll-a for the whole range of concentrations normally registered in the study area (0.1–3.5 mg m^?3). We hypothesize that the particular bio-optical features of the northern Alboran Sea phytoplankton explain the poor functioning of the published algorithms that have been tested in this work.     

地球观测1号高光谱与全色图像融合的最佳方法

受制于成像原理及制造技术等因素,航天高光谱遥感图像的空间分辨率相对较低,为此提出将高光谱图像与高空间分辨率图像进行融合处理,设计最佳的增强高光谱遥感图像空间分辨率的融合算法。针对地球观测1号(EO-1)Hyperion高光谱图像和高级陆地成像仪（ALI）全色波段图像的特点,从9种具体遥感图像融合算法中选用4种融合算法开展山区与城市的数据融合实验,即Gram-Schmidt光谱锐化融合法、平滑调节滤波(SFIM)变换融合法、加权平均法（WAM）融合法和小波变换（WT）融合法,并分别从定性、定量和分类精度三方面对这些方法的融合效果进行综合评价与对比分析,从而确定适合EO-1高光谱与全色图像融合的最佳方法。实验结果显示：从图像融合效果看,在所采用的4种融合方法中,Gram-Schmidt光谱锐化融合法的效果最好;从图像分类效果看,基于融合图像的分类效果要优于基于源图像的分类效果。理论分析与实验结果均表明：Gram-Schmidt光谱锐化融合法是一种较为理想的高光谱与高空间分辨率遥感图像的融合算法,为提高高光谱遥感图像的清晰度、可靠性及图像的地物识别和分类的准确性提供有力的支持。     

Application of a generalized additive model (GAM) for estimating chlorophyll-a concentration from MODIS data in the Bohai and Yellow Seas,China

In optically complex waters, it is important to evaluate the accuracy of the standard satellite chlorophyll-a (chl-a) concentration algorithms, and to develop accurate algorithms for monitoring the dynamics of chl-a concentration. In this study, the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite remote-sensing reflectance and concurrent in situ measured chl-a (2010–2013) were used to evaluate the standard OC3M algorithm (ocean chlorophyll-a three-band algorithm for MODIS) and Graver–Siegel–Maritorena model version 1 (GSM01) algorithm for estimating chl-a concentration in the Bohai and Yellow Seas (BYS). The results showed that the chl-a algorithms of OC3M and GSM01 with global default parameters presented poor performance in the BYS (the mean absolute percentage difference (MAPD) and coefficient of determination (R²) of OC3M are 222.27% and 0.25, respectively; the MAPD and R² of GSM01 are 118.08% and 0.07, respectively). A novel statistical algorithm based on the generalized additive model (GAM) was developed, with the aim of improving the satellite-derived chl-a accuracy. The GAM algorithm was established using the in situ measured chl-a concentration as the output variable, and the MODIS above water remote-sensing reflectance (visible bands at 412, 443, 469, 488, 531, 547, 555, 645, 667, and 678 nm) and bathymetry (water depth) as input variables. The MAPD and R² calculated between the GAM and the in situ chl-a concentration are 39.96% and 0.67, respectively. The results suggest that the GAM algorithm can yield a superior performance in deriving chl-a concentrations relative to the standard OC3M and GSM01 algorithms in the BYS.     

Detecting water stress effects on fruit quality in orchards with time-series PRI airborne imagery

A methodology for the assessment of fruit quality in crops subjected to different irrigation regimes is presented. High spatial resolution multispectral and thermal airborne imagery were used to monitor crown temperature and the Photochemical Reflectance Index (PRI) over three commercial orchards comprising peach, nectarine and orange fruit trees during 2008. Irrigation regimes included sustained and regulated deficit irrigation strategies, leading to high variability of fruit quality at harvest. Stem water potential was used to monitor individual tree water status on each study site. Leaf samples were collected for destructive sampling of xanthophyll pigments to assess the relationship between the xanthophyll epoxidation state (EPS) and PRI at leaf and airborne-canopy level. At harvest, fruit size, Total Soluble Solids (TSS) and Tritatable Acidity (TA) were measured to characterize fruit quality. A statistically significant relationship between EPS and PRI was found at the leaf (r² = 0.81) and canopy level (r² = 0.41). Airborne-derived crown PRI calculated from the imagery acquired during the fruit growth was related to the ratio of the total soluble solids normalized by the tritatable acidity (TSS/TA), an indicator of fruit quality measured on the same trees, yielding a coefficient of determination of r² = 0.50. The relationship between the integral of PRI time-series and TSS/TA yielded a coefficient of determination of r² = 0.72 (peach) and r² = 0.61 (nectarines). On the contrary, the relation between TSS/TA and the time-series of crown thermal imagery was very weak (r² = 0.21 and 0.25 respectively). These results suggest that a physiological remote sensing indicator related to photosynthesis, such as PRI, is more appropriate for fruit quality assessment than crown temperature, the established method of water stress detection, which is more related to crown transpiration. A radiative transfer modelling study was conducted to assess the potential validity of this methodology for fruit quality assessment when using medium spatial resolution imagery. The analysis shows important effects of soil and shadows on the PRI vs EPS relationship used for fruit quality assessment if non-pure crown reflectance was extracted from the imagery.     

On the use of ocean color remote sensing to measure the transport of dissolved organic carbon by the Mississippi River Plume

We investigated the use of ocean color remote sensing to measure the transport of dissolved organic carbon (DOC) by the Mississippi River to the Gulf of Mexico. From 2000 to 2005 we recorded surface measurements of DOC, colored dissolved organic matter (CDOM), salinity, and water-leaving radiances during five cruises to the Mississippi River Plume. These measurements were used to develop empirical relationships to derive DOC, CDOM, and salinity from monthly composites of SeaWiFS imagery collected from 1998 through 2005. We compared our remote sensing estimates of river flow and DOC transport with data collected by the United States Geological Survey (USGS) from 1998 through 2005. Our remote sensing estimates of river flow and DOC transport correlated well (r² ∼ 0.70) with the USGS data. Our remote sensing estimates and USGS field data showed low variability in DOC concentrations in the river end-member (7-11%), and high seasonal variability in river flow (∼ 50%). Therefore, changes in river flow control the variability in DOC transport, indicating that the remote sensing estimate of river flow is the most critical element of our DOC transport measurement. We concluded that it is possible to use this method to estimate DOC transport by other large rivers if there are data on the relationship between CDOM, DOC, and salinity in the river plume.     

Applications of Radarsat‐1 synthetic aperture radar imagery to assess hurricane‐related flooding of coastal Louisiana

The Louisiana coast is subjected to hurricane impacts including flooding of human settlements, river channels and coastal marshes, and salt water intrusion. Information on the extent of flooding is often required quickly for emergency relief, repairs of infrastructure, and production of flood risk maps. This study investigates the feasibility of using Radarsat‐1 SAR imagery to detect flooded areas in coastal Louisiana after Hurricane Lili, October 2002. Arithmetic differencing and multi‐temporal enhancement techniques were employed to detect flooding and to investigate relationships between backscatter and water level changes. Strong positive correlations (R ² = 0.7–0.94) were observed between water level and SAR backscatter within marsh areas proximate to Atchafalaya Bay. Although variations in elevation and vegetation type did influence and complicate the radar signature at individual sites, multi‐date differences in backscatter largely reflected the patterns of flooding within large marsh areas. Preliminary analyses show that SAR imagery was not useful in mapping urban flooding in New Orleans after Hurricane Katrina's landfall on 29 August 2005.