Ground Spectral Measurements Research Articles

The Determination of Snow Albedo from Satellite Measurements Using Fast Atmospheric Correction Technique

We present a simplified atmospheric correction algorithm for snow/ice albedo retrievals using single view satellite measurements. The validation of the technique is performed using Ocean and Land Colour Instrument (OLCI) on board Copernicus Sentinel-3 satellite and ground spectral or broadband albedo measurements from locations on the Greenland ice sheet and in the French Alps. Through comparison with independent ground observations, the technique is shown to perform accurately in a range of conditions from a 2100 m elevation mid-latitude location in the French Alps to a network of 15 locations across a 2390 m elevation range in seven regions across the Greenland ice sheet. Retrieved broadband albedo is accurate within 5% over a wide (0.5) broadband albedo range of the (N = 4155) Greenland observations and with no apparent bias.

Hybrid Methodology for the Estimation of Crop Coefficients Based on Satellite Imagery and Ground-Based Measurements

The objective of the current study was the investigation of specific relationships between crop coefficients and vegetation indices (VI) computed at the water-limited environment of Lake Karla Watershed, Thessaly, in central Greece. A Mapping ET (evapotranspiration) at high Resolution and with Internalized Calibration (METRIC) model was used to derive crop coefficient values during the growing season of 2012. The proposed methodology was developed using medium resolution Landsat 7 ETM+ images and meteorological data from a local weather station. Cotton, sugar beets, and corn fields were utilized. During the same period, spectral signatures were obtained for each crop using the field spectroradiometer GER1500 (Spectra Vista Corporation, NY, U.S.A.). Relative spectral responses (RSR) were used for the filtering of the specific reflectance values giving the opportunity to match the spectral measurements with Landsat ETM+ bands. Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI) and Enhanced Vegetation Index 2 (EVI2) were then computed, and empirical relationships were derived using linear regression analysis. NDVI, SAVI, and EVI2 were tested separately for each crop. The resulting equations explained those relationships with a very high R2 value (>0.86). These relationships have been validated against independent data. Validation using a new image file after the experimental period gives promising results, since the modeled image file is similar in appearance to the initial one, especially when a crop mask is applied. The CROPWAT model supports those results when using the new crop coefficients to estimate the related crop water requirements. The main benefit of the new approach is that the derived relationships are better adjusted to the crops. The described approach is also less time-consuming because there is no need for atmospheric correction when working with ground spectral measurements.

Remote Ground-Based and Satellite Monitoring of Vegetation

Prospects for remote ground and satellite sensing to monitor agricultural (agrocenoses) and grass (meadows and steppes) vegetation are considered. This helps assess chlorophyll contents, crop yields, impurities of territories and identify agrocenoses. Investigation of vegetation on salinized soils identified the necessity to consider the succession of limiting factors (temperature and the degree of soil salinization). The results of studies on grassland plant communities in Khakassia based on geobotanical descriptions and ground spectral measurements are presented, allowing the refinement of methods that improve the accuracy of deciphering satellite images of medium and low resolutions.

Combining Unmanned Aerial Vehicle (UAV)-Based Multispectral Imagery and Ground-Based Hyperspectral Data for Plant Nitrogen Concentration Estimation in Rice.

Plant nitrogen concentration (PNC) is a critical indicator of N status for crops, and can be used for N nutrition diagnosis and management. This work aims to explore the potential of multispectral imagery from unmanned aerial vehicle (UAV) for PNC estimation and improve the estimation accuracy with hyperspectral data collected in the field with a hyperspectral radiometer. In this study we combined selected vegetation indices (VIs) and texture information to estimate PNC in rice. The VIs were calculated from ground and aerial platforms and the texture information was obtained from UAV-based multispectral imagery. Two consecutive years (2015 & 2016) of experiments were conducted, involving different N rates, planting densities and rice cultivars. Both UAV flights and ground spectral measurements were taken along with destructive samplings at critical growth stages of rice (Oryza sativa L.). After UAV imagery preprocessing, both VIs and texture measurements were calculated. Then the optimal normalized difference texture index (NDTI) from UAV imagery was determined for separated stage groups and the entire season. Results demonstrated that aerial VIs performed well only for pre-heading stages (R2 = 0.52–0.70), and photochemical reflectance index and blue N index from ground (PRIg and BNIg) performed consistently well across all growth stages (R2 = 0.48–0.65 and 0.39–0.68). Most texture measurements were weakly related to PNC, but the optimal NDTIs could explain 61 and 51% variability of PNC for separated stage groups and entire season, respectively. Moreover, stepwise multiple linear regression (SMLR) models combining aerial VIs and NDTIs did not significantly improve the accuracy of PNC estimation, while models composed of BNIg and optimal NDTIs exhibited significant improvement for PNC estimation across all growth stages. Therefore, the integration of ground-based narrow band spectral indices with UAV-based textural information might be a promising technique in crop growth monitoring.

Comparison of NDVIs from GOCI and MODIS Data towards Improved Assessment of Crop Temporal Dynamics in the Case of Paddy Rice

The monitoring of crop development can benefit from the increased frequency of observation provided by modern geostationary satellites. This paper describes a four-year testing period from 2010 to 2014, during which satellite images from the world's first Geostationary Ocean Color Imager (GOCI) were used for spectral analyses of paddy rice in South Korea. A vegetation index was calculated from GOCI data based on the bidirectional reflectance distribution function (BRDF)-adjusted reflectance, which was then used to visually analyze the seasonal crop dynamics. These vegetation indices were then compared with those calculated using the Moderate-resolution Imaging Spectroradiometer (MODIS)-normalized difference vegetation index (NDVI) based on Nadir BRDF-adjusted reflectance. The results show clear advantages of GOCI, which provided four times better temporal resolution than the combined MODIS sensors, interpreting subtle characteristics of the vegetation development. Particularly in the rainy season, when data acquisition under clear weather conditions was very limited, it was possible to find cloudless pixels within the study sites by compiling GOCI images obtained from eight acquisition periods per day, from which the vegetation index could be calculated. In this study, ground spectral measurements from CROPSCAN were also compared with satellite-based vegetation products, despite their different index magnitude, according to systematic discrepancy, showing a similar crop development pattern to the GOCI products. Consequently, we conclude that the very high temporal resolution of GOCI is very beneficial for monitoring crop development, and has potential for providing improved information on phenology.

Deploying four optical UAV-based sensors over grassland: challenges and limitations

Abstract. Unmanned aerial vehicles (UAVs) equipped with lightweight spectral sensors facilitate non-destructive, near-real-time vegetation analysis. In order to guarantee robust scientific analysis, data acquisition protocols and processing methodologies need to be developed and new sensors must be compared with state-of-the-art instruments. Four different types of optical UAV-based sensors (RGB camera, converted near-infrared camera, six-band multispectral camera and high spectral resolution spectrometer) were deployed and compared in order to evaluate their applicability for vegetation monitoring with a focus on precision agricultural applications. Data were collected in New Zealand over ryegrass pastures of various conditions and compared to ground spectral measurements. The UAV STS spectrometer and the multispectral camera MCA6 (Multiple Camera Array) were found to deliver spectral data that can match the spectral measurements of an ASD at ground level when compared over all waypoints (UAV STS: R2=0.98; MCA6: R2=0.92). Variability was highest in the near-infrared bands for both sensors while the band multispectral camera also overestimated the green peak reflectance. Reflectance factors derived from the RGB (R2=0.63) and converted near-infrared (R2=0.65) cameras resulted in lower accordance with reference measurements. The UAV spectrometer system is capable of providing narrow-band information for crop and pasture management. The six-band multispectral camera has the potential to be deployed to target specific broad wavebands if shortcomings in radiometric limitations can be addressed. Large-scale imaging of pasture variability can be achieved by either using a true colour or a modified near-infrared camera. Data quality from UAV-based sensors can only be assured, if field protocols are followed and environmental conditions allow for stable platform behaviour and illumination.

Evaluating the Effects of Shadow Detection on QuickBird Image Classification and Spectroradiometric Restoration

Shadows in high resolution imagery create significant problems for urban land cover classification and environmental application. We first investigated whether shadows were intrinsically different and hypothetically possible to separate from each other with ground spectral measurements. Both pixel-based and object-oriented methods were used to evaluate the effects of shadow detection on QuickBird image classification and spectroradiometric restoration. In each method, shadows were detected and separated either with or without histogram thresholding, and subsequently corrected with a k-nearest neighbor algorithm and a linear correlation correction. The results showed that shadows had distinct spectroradiometric characteristics, thus, could be detected with an optimal brightness threshold and further differentiated with a scene-based near infrared ratio. The pixel-based methods generally recognized more shadow areas and with statistically higher accuracy than the object-oriented methods. The effects of the prior shadow thresholding were not statistically significant. The accuracy of the final land cover classification, after accounting for the shadow detection and separation, was significantly higher for the pixel-based methods than for the object-oriented methods, although both achieved similar accuracy for the non-shadow classes. Both radiometric restoration algorithms significantly reduced shadow areas in the original satellite images.

Citrus greening disease detection using aerial hyperspectral and multispectral imaging techniques

Airborne multispectral and hyperspectral imaging can be used to detect potentially diseased trees rapidly over a large area using unique spectral signatures. Ground inspection and management can be focused on these detected zones, rather than an entire grove, making it less labor-intensive and time-consuming. We propose a method to detect the areas of citrus groves infected with citrus greening disease [Huanglongbing (HLB)] using airborne hyperspectral and multispectral imaging. This would prevent further spread of infection with efficient management plans of infected areas. Two sets of hyperspectral images were acquired in 2007 and 2009, from different citrus groves in Florida. Multispectral images were acquired only in 2009. A comprehensive ground truthing based on ground measurements and visual check of the citrus trees was used for validating the results using 2007 images. In 2009, a more accurate polymerase chain reaction test for selected trees from ground truthing was carried out. With a handheld spectrometer, ground spectral measurements were obtained along with their degrees of infection. A hyperspectral imaging software (ENVI, ITT VIS) was used for the analysis. HLB infected areas were identified using image-derived spectral library, mixture tuned matched filtering (MTMF), spectral angle mapping (SAM), and linear spectral unmixing. The accuracy of the MTMF method was greater than the other methods. The accuracy of SAM using multispectral images (87%) was comparable to the results of the MTMF and also yielded higher accuracy when compared to SAM analysis on hyperspectral images. A possible inaccurate ground truthing for the grove in 2007 generated more false positives.

Spectral difference analysis and airborne imaging classification for citrus greening infected trees

Citrus greening, also called Huanglongbing (HLB), became a devastating disease spread through citrus groves in Florida, since it was first found in 2005. Multispectral (MS) and hyperspectral (HS) airborne images of citrus groves in Florida were acquired to detect citrus greening infected trees in 2007 and 2010. Ground truthing including field and indoor spectral measurement, infection status along with GPS coordinates was conducted for both healthy and infected trees. Ground spectral measurements showed that healthy canopy had higher reflectance in the visible range, and lower reflectance in the near-infrared (NIR) range than HLB infected canopy. Red edge position (REP) also showed notable difference between healthy and HLB canopy. But the difference in the NIR range and REP were comparably more sensitive to the environment or the background noise. Accuracy for separating HLB and healthy samples reached more than 90% when a simple REP threshold method was implemented in the ground reflectance datasets, regardless of field or indoor measurement; but it did not work well with the HS images because of its low spatial resolution. Support vector machine (SVM) was able to provide a fast, easy and adoptable way to build a mask for tree canopy. High positioning error of the ground truth in the 2007 HS image led to validation accuracy of less than 50% for most of classification methods. In the 2010 image from Southern Gardens (SG) grove, with better ground truth records, higher classification accuracies (about 90% in training sets, more than 60% in validation sets for most of the methods) were achieved. Disease density maps were also generated from the classification results of each method; most of them were able to identify the severely infected areas. Simpler classification methods such as minimum distance (MinDist) and Mahalanobis distance (MahaDist) showed more stable and balanced detection accuracy between the training and validation sets in the 2010 images. Their similar infection trend with ground scouted maps showed a promising future to manage HLB disease with airborne spectral imaging.

Validating MODIS surface reflectance based on field spectral measurements

This paper presents a method of validating the MODIS (Moderate-Resolution Imaging Spectroradiometer) surface reflectance (MOD09) based on ground spectral measurements and high-resolution remote sensing images. Given that the spatial resolution of the MODIS image is too low to directly compare ground measurements with the MODIS pixels, registration is first made between MODIS and a high-resolution image. Afterwards, the same sizable sample area to one MODIS pixel is chosen in a high resolution image (a pixel in MODIS corresponds to many pixels in a high-resolution image), after which image classification is conducted in each sample area. Finally, reflectance is measured for each class in each area. Taking the ratio of every class area to each sample area as the weight, the weighted mean reflectance is then calculated for each sample area (a pixel for MODIS), which is treated as the measured reflectance of the corresponding pixel in the MODIS image. A contradistinctive analysis is carried out between measured reflectance and MODIS retrieval reflectance from the space in order to determine the accuracy of the MODIS land surface reflectance product and correct its error. The experiment indicates that a linear positive correlativity between the MODIS pixel retrieval reflectance from space and the measured (calculated) surface reflectance exists. From this, we can build an error correction model of the MODIS surface reflectance product by linear regression. The accuracy of the MODIS surface reflectance products after error correction is remarkably improved. Likewise, the relative error can be controlled by 10%, thereby satisfying the requirements of various remote sensing applications.

Mapping urban road infrastructure using remotely sensed images

Comprehensive and accurate information on the conditions of the road infrastructure is essential for effective management and planning of the road network, especially in cities facing serious traffic congestion problems, as in the city of Istanbul, Turkey. One of the most important services of the local authorities is the rehabilitation of road surfaces. Asphalt resurfacing is carried out to renew the road surface and restore its resistance to weathered and traffic worn pavements. Today, the determination of pavement surface conditions is usually carried out through visual inspections by the experts and interpretation of in situ photographs/videos. In this study, an investigation has been made to understand the spectral behaviour of the asphalt in terms of its contents and age, also to discover the feasibility of using widely available satellite sensors in the determination of road surface conditions. Two datasets in the study area from D‐100 road and TEM highway were used to examine asphalt road aging and deterioration. The changes to the conditions of the roads were detected using multispectral IKONOS images and ground spectral measurements for asphalt samples were obtained using an Analytical Spectral Devices full range (ASD FR) instrument in a laboratory environment. It was found that the road deformations mainly occur in areas where vehicles reduce or increase their speeds suddenly. Road sections just before toll booths and junctions are found to be more prone to deformations. IKONOS images can be effectively used to determine the condition of the asphalt pixels.

A spectral index to monitor the head-emergence of wheat in semi-arid conditions

Harvesting wheat ( Triticium aestivum L.) for forage or leaving it for grain is the main decision uncertainty growers face in semi-arid regions during mid-season. To facilitate decision-making, a decision support system (DSS) has recently been proposed that requires information about crop water and nutritional status during spike emergence. Though remote sensing has been used to provide site-specific crop status information, a spectral vegetation index is needed to ensure that the information has been acquired during spike emergence. The objective of this study was to propose a spectral index sensitive to spike emergence and validate its suitability across different commercial farm fields by using ground spectral measurements and multispectral satellite imagery. To develop the index, controlled experiments with commonly grown wheat varieties were conducted during the 2004/2005 and 2005/2006 growing season in the agricultural area of the northern Negev desert of Israel. The experiments showed that spike emergence correlated most strongly ( r = 0.7, p < 0.05) with spectral changes near the 1.2 μm water absorption feature in contrast to the band at 1.1 μm which appeared to be only weakly correlated. Thus, the spike emergence sensitive band at 1.2 μm has been combined with the insensitive band at 1.1 μm as reference to form the ratio-based normalized heading index (NHI). Experimental data were then used to establish an index threshold that helps separate data acquired before and after spike emergence. The proposed NHI was able to identify spike emergence with a classification accuracy varying between 53 and 83%. Accuracy was influenced by season, and whether narrow or broad spectral bands were used. Validation of the index in commercial farm fields in Israel and the United States showed that the classification accuracy was similar for ground spectral measurements and the advanced land imager (ALI) satellite imagery. These results suggest that the NHI is suited for identifying the onset of heading throughout wheat-growing areas without the need for characterizing seasonal trends.

Wheat and maize monitoring based on ground spectral measurements and multivariate data analysis

Improved accuracy in the retrieval of crop biophysical variables is needed to enable a greater contribution of hyperspectral remote sensing data to site-specific crop management. One season of maize and two seasons of wheat field experiments were used to explore the potential of multivariate data analysis for retrieving crop biophysical variables from spectroscopic data. Canopy spectral data at 350-2500 nm were collected during the experiments, in which various seeding densities, fertilization, and irrigation treatments were applied to generate dry biomass, water-content and nitrogen-content variability. Partial least squares (PLS) models that considered the reflectance derivatives (1 st and 2 nd ) and that used only significant wavelengths were evaluated. As the measurements were conducted throughout the whole season, a wide variability was observed, which was critical for obtaining a good calibration model. The use of derivatives and selection of the most significant wavelengths were found to be the best pre-processing methodologies to increase prediction accuracy. Significant predictive power was achieved for the validation dataset, especially for the wheat dry biomass (R 2 ~0.75), for which similar results were obtained, even with data from a different season (R 2 ~0.70). PLS-predicted wheat water content had a correlation of R 2 ~0.60 with the measured values. An advantage was found in the use of PLS models, compared to common vegetation indices. Based on ground spectral measurements, this study confirms the potential of multivariate-data-analysis procedures for the interpretation of hyperspectral remote sensing data.

Analysis of the spectral response of flourishing-withering vegetation changes based on ground spectral measurements

A structural mode was used to characterize vegetation composition at the plant leaf level and a flourishing-withering ratio was developed. The spectral responses of vegetation with different flourishing-withering ratios were analyzed, the change rates of the chlorophyll and moisture content indices of vegetation with different flourishing-withering ratios were compared, and correlations between the chlorophyll and moisture content indices were analyzed. The results reveal that leaves with an intermediate flourishing-withering ratio can increase the absorption signatures of vegetation and that band ranges of 570–700 nm and 1300–1540 nm can play a role in indicating changes in the flourishing-withering ratios of vegetation; NPQI, NPCI, R695/R420, R695/R760, R750/R700, the peak-value area of red selvedge, the red selvedge amplitude, the ratio between the red selvedge amplitude and the minimum amplitude, and the NDVI of vegetation change regularly with the change in flourishing-withering ratios, and these nine vegetation indices are highly related to the chlorophyll content. Vegetation indexes of NDWI and PRI are very sensitive to the flourishing-withering change in vegetation and are closely related to the moisture content, and the correlation coefficient is higher than 0.9. The derivative of the spectra is more effective in describing changes in the structural mode of vegetation with different flourishing-withering ratios, especially at band ranges of 552–628 nm and 630–686 nm, and it is more sensitive to the mixed flourishing-withering ratios of leaves rather than to the vegetation indices. The red selvedge position in the spectrum is highly related to the chlorophyll content and is not sensitive to changes in the structural mode of mixed flourishing-withering leaves. The red selvedge parameters are sensitive to changes in the flourishing-withering ratio at the peak-value area of the red selvedge amplitude and the ratio between the red selvedge amplitude and the minimum amplitude. The effect of a sand background on the spectrum of withering leaves is higher than that of flourishing leaves; the effect of a sand background increases with increasing proportions of withering leaves, the superposition effect of the sand background on mixed flourishing-withering leaves is about 7% at visible light bands of 400–700 nm and 1300–1540 nm, and is over 10% at the near infrared band range of 700–1300 nm.

Ground and space spectral measurements for assessing the semi-arid ecosystem phenology related to CO 2 fluxes of biological soil crusts

This paper reports on ranges of carbon dioxide (CO 2) activity in biological soil crusts (BSC) correlated with different ranges of the BSC's spectral reflectance throughout the phenological cycle of the year. Methodology is based on surface CO 2 exchange measurements, ground spectral measurements, and satellite images interpretation. Thirty-nine field campaigns, each of duration of 3 days, were conducted over the course of 2 years at a sand dunes and a loess environment of the northwestern Negev desert in Israel, in order to relate the CO 2 fluxes and the spectral signals to the seasonal phenology. The Normalized Difference Vegetation Index (NDVI) was derived from ground measurements of the BSC's reflectance and correlated with their CO 2 exchange data. A linear mixture model, incorporating the different contributions of the sites' ground features, was calculated and compared with SPOT-HRV data. From the ground measurements, fairly good correlations were found between the NDVI and the CO 2 fluxes on a seasonal scale. Hence, the NDVI successfully indicates the potential magnitude and capacity of the BSC's assimilation activity. The linear mixture model successfully describes the phenological cycles of the BSC, annual, and perennial plants and corresponds well to the satellite data. Moreover, the model enables annual changes of the phenology cycle and the growing season length to be distinguished. Both the linear mixture model and the derived NDVI values recorded the recovery of the BSC at the beginning of the wet season before annuals had germinated. Finally, it is concluded that a combination of CO 2 exchange measurements, linear mixture model, and NDVI values is suitable for monitoring BSC's productivity in arid regions.

ESTIMATION OF THE FRACTION OF CANOPY COVER FROM MULTISPECTRAL DATA TO BE USED IN A WATER SOIL EROSION PREDICTION MODEL

Using the 16 day MODIS (aboard the EOS Terra satellite) 250m NDVI and ground biophysical and spectral measurements we established simple relationships between these parameters and the canopy cover. The canopy cover is used in water erosion models to estimate the amount of soil loss under precipitation events and specific geographic conditions. Two transects, in the grassland part of the Walnut Gulch Experimental Watershed (WGEW) located near the town of Tombstone in Arizona, were established for ground data collection. Ground measurements were performed every 16 days, to coincide with the Terra Satellite overpass. Erosion, in desert environment is a contributing factor to soil degradation and subsequently desertification. Erosion is strongly related with canopy cover, soil parameters, topography, and climate variables. Although ground point estimates of canopy cover are usually used in erosion models, their temporal and spatial variability need to be accounted for. Using MODIS NDVI data, calibrated with field measurements, we were able to estimate the canopy cover using regression analysis. This technique is very simple and properly accounts for the spatial and temporal variability of the canopy cover. We tested this technique with the WEPP erosion model and we found it to be very valuable

Natural vegetation phenology assessment by ground spectral measurements in two semi-arid environments.

Natural vegetation in semi-arid regions is characterized by three ground features, in addition to bare surfaces - biological soil crusts, annuals, and perennials. These three elements have distinguishable phenological cycles that can be detected by spectral ground measurements and by calculating the weighted normalized difference vegetation index (NDVI). The latter is the product of the derived NDVI of each ground feature and its respective areal cover. Each phenological cycle has the same basic elements - oscillation from null (or low) to full photosynthetic status and back to a stage of senescence. However, they vary in phase. The biological soil crusts show the earliest and highest weighted NDVI peak during the rainy season, and their weighted NDVI signal lasts longer than that of the annuals. The annuals are dominant in late winter and early spring while the perennials predominate in late spring and during the summer.

Comparison between in situ and MODIS-derived spectral reflectances of snow and sea ice in the Amundsen Sea, Antarctica

The spectral albedo and directional reflectance of snow and sea ice were measured on sea ice of various types, including nilas, grey ice, pancake ice, multi-year pack ice, and land-fast ice in the Ross, Amundsen and Bellingshausen seas during a summer cruise in February through March 2000. Measurements were made using a spectroradiometer that has 512 channels in the visible and near-infrared (VNIR) region in which 16 of the 36 bands of the Moderate Resolution Imaging Spectroradiometer (MODIS) are covered. Directional reflectance is also retrieved from the MODIS radiometrically calibrated data (Level 1B) concurrently acquired from the first National Aeronautics and Space Administration (NASA) Earth Observing System (EOS) satellite, Terra. The locations of the ground ice stations are identified accurately on the MODIS images, and the spectral albedo and directional reflectance values at the 16 VNIR MODIS bands are extracted for those pixel locations. MODIS-derived reflectance is then corrected for the intervening atmosphere whose parameters are retrieved from the MODIS atmospheric profiles product (MOD07_L2) for the same granule. The corresponding spectral albedo and directional reflectance with the same viewing geometry as MODIS are derived from our ground-based spectroradiometer measurements. Because the footprint of the ground spectroradiometer is much smaller than the pixel sizes of MODIS images, the averaged spectral reflectance and albedo in the vicinity of each ice station are simulated for the corresponding MODIS pixel from the ground spectral measurements by weighting over different surface types (various ice types and open water). An accurate determination of ice concentration is important in deriving ground reflectance of a simulated pixel from in situ measurements. The best agreement between the in situ and MODIS measurements was found when the ground had 10/10 ice concentration (discrepancy range 0.2–11.69%, average 4.8%) or was oneice-type dominant (discrepancy range 0.8–16.9%, average 6.2%). The more homogeneous the ground surface and the less variable the ground topography, the more comparable between the in situ and satellite-derived reflectance is expected.

The relation between spectral reflectance and dissolved organic carbon in lake water: Kejimkujik National Park, Nova Scotia, Canada

The ability to predict dissolved organic carbon (DOC) concentrations based on spectral reflectance of lake water was examined in Kejimkujik National Park. Spectral reflectance from both ground and satellite remote sensing platforms were used to create regression models for the prediction of DOC with r 2 values of 0.94 and 0.72 respectively. The location of the peak wavelength of the ground spectral measurements and a cluster analysis of the satellite measurements both separated the lakes into two distinct groups with different DOC concentrations. An analysis of the potential sources of DOC identified three variables important for the prediction of DOC concentrations within the lake, flushing rate and the area of both deciduous forest and open area within the watershed ( r 2 = 0.41). As DOC concentrations are related to mercury concentrations ( r 2 = 0.86) these models could be used to assist in the identification of lakes that are sensitive to mercury pollution.

Wind and cloud cover effects on spectral measurements of flooded rice crops in red and near-infrared SPOT-HRV bands

Wind and cloud cover effects on variabilities of radiance, irradiance and reflectance were analysed using ground-based measurements acquired over flooded rice crops in the red and near-infrared SPOT-HRV bands under variable wind and cloud cover conditions. Overall, spectral measurements were more variable in the near-infrared than in the red band. Wind speed was positively correlated to variability of radiances (r = 0.186 (p>0.001)) and reflectances (r= 0.245 (p>0.001)) in near-infrared, but not in red wavelengths. The proportion of diffuse radiation in the total incident solar radiation was positively correlated to the radiance variability in red (r = 0.086 (p > 0.001)) and near-infrared (r = 0.123 (p > 0.001)) as well as to the irradiance variability in red (r = 0.248 (p > 0.001) and near-infrared (r = 0.243 (p > 0.001)), but not to the reflectance variability. Such an analysis should be done every time ground spectral measurements are performed under variable weather conditions.