Chris M. Mannaerts

Evaluating nitrogen removal by vegetation uptake using satellite image time series in riparian catchments.

Nitrogen (N) removal by vegetation uptake is one of the most important functions of riparian buffer zones in preventing non-point source pollution (NSP), and many studies about N uptake at the river reach scale have proven the effectiveness of plants in controlling nutrient pollution. However, at the watershed level, the riparian zones form dendritic networks and, as such, may be the predominant spatially structured feature in catchments and landscapes. Thus, assessing the functions of riparian system at the basin scale is important. In this study, a new method coupling remote sensing and ecological models was used to assess the N removal by riparian vegetation on a large spatial scale. The study site is located around the Guanting reservoir in Beijing, China, which was abandoned as the source water system for Beijing due to serious NSP in 1997. SPOT 5 data was used to map the land cover, and Landsat-5 TM time series images were used to retrieve land surface parameters. A modified forest nutrient cycling and biomass model (ForNBM) was used to simulate N removal, and the modified net primary productivity (NPP) module was driven by remote sensing image time series. Besides the remote sensing data, the necessary database included meteorological data, soil chemical and physical data and plant nutrient data. Pot and plot experiments were used to calibrate and validate the simulations. Our study has proven that, by coupling remote sensing data and parameters retrieval techniques to plant growth process models, catchment scale estimations of nitrogen uptake rates can be improved by spatial pixel-based modelling.

Evaluation of soil nitrogen emissions from riparian zones coupling simple process-oriented models with remote sensing data.

Riparian ecosystems have critical impacts on controlling the non-point source pollution and maintaining the health of aquatic ecosystems. In this study, a process oriented soil denitrification model was extended with algorithms from a simple nitrogen (N) cycle model and coupled to land surface remote sensing data to enhance its performance in spatial and temporal prediction of gaseous N emissions from soils in the riparian buffer zone surrounding the Guanting reservoir (China). The N emission model is based on chemical and physical relationships that govern the heat budget, soil moisture variations and nitrogen movement in soils. Besides soil water and heat processes, it includes nitrification, denitrification and ammonia (NH(3)) volatilization. SPOT-5 and Landsat-5 TM satellite data were used to derive spatial land surface information and the temporal variation in land cover parameters was also used to drive the model. A laboratory-scale anaerobic incubation experiment was used to estimate the soil denitrification model parameters for the different soil types. An in situ field-scale experiment was conducted to calibrate and validate the soil temperature, moisture and nitrogen sub-models. An indirect method was used to verify simulated N emissions, resulting in a coefficient of determination of R(2)=0.83 between simulated and observed values. Then the model was applied to the whole riparian buffer zone catchment, using the spatial resolution (10m) of the SPOT-5 image. Model sensitivity analysis showed that soil moisture was the most sensitive parameter for gaseous N emissions and soil denitrification was the main process affecting N losses to the atmosphere in the riparian area. From the aspect of land use management around the Guanting reservoir, the spatial structure and distribution of land cover and land use types in the riparian area should be adapted, to enhance faster ecological restoration of the wetland ecological system surrounding this strategically important water resource.

Monitoring variation of water turbidity and related environmental factors in Poyang lake national nature reserve, China

There are pronounced spatial-temporal patterns in water turbidity in Poyang Lake National Nature Reserve (NNR), China. A most suitable empirical model validated by the field data between Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance and Secchi Disk Depth (SDD) selected as the indicator of water turbidity is used to map the spatio-temporal dynamics. High water transparency values are observed during the summer season, while the most turbid situations always occur in winter. In different years, the trend is similar while the occurrence of detailed peaks is a little different in the same lake. Comparing the situation in different seasons, the most turbid places show in different directions. Different lakes have their specific situations. The turbidity difference in the low-water season is less than the varying in the other seasons. Statistical methods were used to quantify the influence of factors such as water level, wind speed, temperature and rainfall. Further statistic analysis is used to judge the accuracy of the model. Some ancillary environmental factors which can also play a role such as fishing, dredging, vegetation and birds influence are analyzed by theoretical deduction, supported by field investigations and historical data.

The ITC GEONETCAST Toolbox: A Geo capacity building component for education and training in Global earth observation & Geo information provision to society

In many countries throughout the world, the use of earth observation data for environmental or societal purposes still remains underexplored, in spite increasing earth observation (EO) data provision. The root cause is mainly a still inadequate generic knowledge to use remote sensing data and derive information products. The GEONETCast data dissemination system of GEOSS, the Global earth observation system of systems, is steadily working towards removing barriers for EO data access and use. Efficient processing and analysis tools, accessible by end-users, need to be urgently developed in order to exploit the full potential of this global data dissemination and information system. The ITC GEONETCast Toolbox, an open access earth observation data retrieval and application development environment is presented here. It can act as gap filler in the knowledge transfer chain from EO data providers to the local end-users in the different societal benefit areas of GEOSS.

Estimating total suspended matter concentration in tropical coastal waters of the Berau estuary, Indonesia

This study presents the application of a semi-empirical approach, based on the Kubelka–Munk (K-M) model, to retrieve the total suspended matter (TSM) concentration of water bodies from ocean colour remote sensing. This approach is validated with in situ data sets compiled from the tropical waters of Berau estuary, Indonesia. Compared to a purely empirical approach, the K-M model provides better results in the retrieval of TSM concentration on both data sets (in situ and Medium Resolution Imaging Spectrometer (MERIS)). In this study, the K-M model was calibrated with in situ measurements of remote-sensing reflectance (R rs) and TSM concentration. Next, the inverse K-M model was successfully applied to images taken by the MERIS instrument by generating regional maps of TSM concentration. MERIS top-of-atmosphere radiances were atmospherically corrected using the Moderate Spectral Resolution Atmospheric Transmittance (MODTRAN) radiative transfer model. The best correlation between R rs measured in situ and R rs MERIS was found to be at a wavelength of 620 nm. The TSM concentrations retrieved using the K-M model showed a lower root mean square error (RMSE), a higher coefficient of determination and a smaller relative error than those retrieved by the purely empirical approach.

An intercomparison of satellite-based daily evapotranspiration estimates under different eco-climatic regions in South Africa

Knowledge of evapotranspiration (ET) is essential for enhancing our understanding of the hydrological cycle, as well as for managing water resources, particularly in semi-arid regions. Remote sensing offers a comprehensive means of monitoring this phenomenon at different spatial and temporal intervals. Currently, several satellite methods exist and are used to assess ET at various spatial and temporal resolutions with various degrees of accuracy and precision. This research investigated the performance of three satellite-based ET algorithms and two global products, namely land surface temperature/vegetation index (TsVI), Penman–Monteith (PM), and the Meteosat Second Generation ET (MET) and the Global Land-surface Evaporation: the Amsterdam Methodology (GLEAM) global products, in two eco-regions of South Africa. Daily ET derived from the eddy covariance system from Skukuza, a sub-tropical, savanna biome, and large aperture boundary layer scintillometer system in Elandsberg, a Mediterranean, fynbos biome, during the dry and wet seasons, were used to evaluate the models. Low coefficients of determination (R2) of between 0 and 0.45 were recorded on both sites, during both seasons. Although PM performed best during periods of high ET at both sites, results show it was outperformed by other models during low ET times. TsVI and MET were similarly accurate in the dry season in Skukuza, as GLEAM was the most accurate in Elandsberg during the wet season. The conclusion is that none of the models performed well, as shown by low R2 and high errors in all the models. In essence, our results conclude that further investigation of the PM model is possible to improve its estimation of low ET measurements.

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

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

Remote-sensing estimation of the water stress coefficient and comparison with drought evidence

ABSTRACT Drought assessment of croplands and sylvo-pastoral areas is crucial in semi-arid regions. Satellite remote sensing offers an opportunity for such assessment. This study presents a method of spatial and temporal estimation of drought index in Medjerda basin (23,700 km2) using satellite data and its validation with in situ investigation of areas with crop damage realized by the ministry of agriculture. To estimate drought index, potential evapotranspiration (PET) is calculated using Penman–Monteith equation and modified FAO-56 crop coefficient (Kc) approach combined with remote-sensing data and actual evapotranspiration is derived from the Meteosat Second Generation platforms. The period of study is the 2010 water year. PET estimations show good accuracy with corrected pan evaporation observations up to 0.9. In comparison, the water stress coefficient (Ks) aggregated by land-cover type shows the coefficient of determination with the fraction of drought damage areas of 0.5 for the third decade of March and first decade of April in croplands areas and 0.8 for the second and third decades of May in croplands and sylvo-pastoral areas. This study showed that satellite data approaches could successfully be used to monitor drought in river basins in the Northern Africa and Mediterranean region.

Conjunctive use of in situ gas sampling and chromatography with geospa-tial analysis to estimate greenhouse gas emissions of a large Amazonian hydroelectric reservoir

Hydroelectric power reservoirs are considered potential contributors to the greenhouse effect in the atmosphere through the emittance of methane and carbon dioxide. We combined in situ sampling and gas chromatography with geostatistical and remote sensing approaches to estimate greenhouse gas (GHG) emissions of a large hydropower reservoir. We used remote sensing data to estimate the water surface and geospatial interpolation to calculate total emissions as a function of reservoir surface area. The CH4 and CO2 gas concentrations were linearly correlated to sampling time, confirming the adequacy of the in situ sampling method to measure GHG diffusive fluxes from reservoir water surfaces. The combination of high purity (99.99%) ISO-norm gas standards with a gas chromatograph, enabled us to achieve low measurement detection limits of 0.16 and 0.60 μmol mol-1, respectively, for CH4 (using a flame ionization or FID detector) and CO2 (using a thermal conductivity or TCD detector). Our results show that CO2 emissions are significantly (an order of 5.102-103) higher than those of CH4 in both the spatial and temporal domain for this reservoir. The total diffusive GHG emissions over a year (June 2011 to May 2012) of the Tucuruí hydropower reservoir being in operation, in units of tons of carbon, added up to 6.82 × 103 for CH4 and 1.19 × 106 for CO2. We show that in situ GHG sampling using small floating gas chambers and high precision gas chromatography can be combined with geospatial interpolation techniques and remote sensing data to obtain estimates of diffusive GHG emissions from large water bodies with fluctuating water surfaces such as hydropower reservoirs. We recommend that more measurements and observations on these emissions are pursued in order to support and better quantify the ongoing discussions on estimates and mitigation of GHG emissions from reservoirs in the Amazon region and elsewhere in the world.

Improving the MSGMPE Accuracy for the Northern of Tunisia by the Multispectral Analysis of the Cloud Field from MSG SEVIRI

The advanced Spinning Enhanced Visible Infra-Red Imager (SEVIRI) radiometer onboard the Meteosat Seconde Generation (MSG) series has a temporal resolution of 15 min. It has the ability to scan the Earth in 12 spectral channels from visible to thermal infrared. The spatial resolution of the SEVIRI instrument is 3 km for 11 of the 12 channels, and 1 km for the High Resolution Visible (HVR) channel.