Comparison of Field-Observed and Simulated Map Output from a Dynamic Floodplain Vegetation Model Using Remote Sensing and GIS Techniques

Three different forms of the Kappa statistic were used to compare simulated and observed maps to assess the predictive capability of a dynamic vegetation model. Kappas were used to compare vegetation model performance considering both individual vegetation classes and merged vegetation phases. The overall Kappa increased slightly from 0.18 to 0.21 and 0.23, when different vegetation phases were resampled from the original 10 m cell size to 30 m and 50 m cell sizes, respectively. Similarly, overall Kappa improved from 0.18 to 0.55 and 0.71 when the neighborhood cells within a buffer distance of 10 m and 20 m, respectively, were considered. Different forms of the Kappa statistic (K, K_loc, K_qty) are valuable indicators to study the performance of vegetation models and identify parameters (e.g., quantity or location) for improvement in the model results.     

Determination of the free core nutation period from tidal gravity observations of the GGP superconducting gravimeter network

This study is based on 25 long time-series of tidal gravity observations recorded with superconducting gravimeters at 20 stations belonging to the Global Geodynamic Project (GGP). We investigate the diurnal waves around the liquid core resonance, i.e., K ₁, ψ₁ and φ₁, to determine the free core nutation (FCN) period, and compare these experimental results with models of the Earth response to the tidal forces. For this purpose, it is necessary to compute corrected amplitude factors and phase differences by subtracting the ocean tide loading (OTL) effect. To determine this loading effect for each wave, it was thus necessary to interpolate the contribution of the smaller oceanic constituents from the four well determined diurnal waves, i.e., Q ₁, O ₁, P ₁, K ₁. It was done for 11 different ocean tide models: SCW80, CSR3.0, CSR4.0, FES95.2, FES99, FES02, TPXO2, ORI96, AG95, NAO99 and GOT00. The numerical results show that no model is decisively better than the others and that a mean tidal loading vector gives the most stable solution for a study of the liquid core resonance. We compared solutions based on the mean of the 11 ocean models to subsets of six models used in a previous study and five more recent ones. The calibration errors put a limit on the accuracy of our global results at the level of ± 0.1%, although the tidal factors of O ₁ and K ₁ are determined with an internal precision of close to 0.05%. The results for O ₁ more closely fit the DDW99 non-hydrostatic anelastic model than the elastic one. However, the observed tidal factors of K ₁ and ψ₁ correspond to a shift of the observed resonance with respect to this model. The MAT01 model better fits this resonance shape. From our tidal gravity data set, we computed the FCN eigenperiod. Our best estimation is 429.7 sidereal days (SD), with a 95% confidence interval of (427.3, 432.1).     

Analysis of Seasonal Variability of Vegetation and Methane Concentration over India using SPOT-VEGETATION and ENVISAT-SCIAMACHY Data

This paper highlights the spatial and temporal variability of atmospheric columnar methane (CH₄) concentration over India and its correlation with the terrestrial vegetation dynamics. SCanning IMaging Absorption spectrometer for Atmospheric CHartographY (SCIAMACHY) on board ENVIronmental SATellite (ENVISAT) data product (0.5° × 0.5°) was used to analyze the atmospheric CH₄ concentration. Satellite Pour l'Observation de la Terre (SPOT)-VEGETATION sensor’s Normalized Difference Vegetation Index (NDVI) product, aggregated at 0.5° × 0.5° grid level for the same period (2004 and 2005), was used to correlate the with CH₄ concentration. Analysis showed mean monthly CH₄ concentration during the Kharif season varied from 1,704 parts per billion volume (ppbv) to 1,780 ppbv with the lowest value in May and the highest value in September. Correspondingly, mean NDVI varied from 0.28 (May) to 0.53 (September). Analysis of correlation between CH₄ concentration and NDVI values over India showed positive correlation (r = 0.76; n = 6) in Kharif season. Further analysis using land cover information showed characteristic low correlation in natural vegetation region and high correlation in agricultural area. Grids, particularly falling in the Indo-Gangetic Plains showed positive correlation. This could be attributed to the rice crop which is grown as a predominant crop during this period. The CH₄ concentration pattern matched well with growth pattern of rice with the highest concentration coinciding with the peak growth period of crop in the September. Characteristically low correlation was observed (r = 0.1; n = 6) in deserts of Rajasthan and forested Himalayan ecosystem. Thus, the paper emphasizes the synergistic use of different satellite based data in understanding the variability of atmospheric CH₄ concentration in relation to vegetation.     

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

Abstract

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

Application of S-SEBI model for crop evapotranspiration using Landsat-8 data over parts of North India

Estimation and monitoring of crop evapotranspiration (ET_c) or consumptive water use over large-area holds the key to irrigation management plans and regional drought preparedness. The objective of this study was to estimate ET_c by applying the simplified-surface energy balance index (S-SEBI) model to Landsat-8 data for the 2014–2015 period in parts of North India. An average ET_c was estimated 2.72 and 2.47 in mm day^?1 with 0.22, 0.18 standard deviation and 0.11, 0.07 standard error for Kharif and Rabi crops, respectively. On validation part, a close relationship was observed between S-SEBI derived and scintillometer observed evaporative fraction with 0.85 correlation coefficient and 0.86 agreement index. The statistical analysis also endorses the results accuracy and reliability with 0.026 and 0.602, relative root-mean square errors and model efficiency for wheat crop, respectively. The study showed that normalized difference vegetation index and LST are closely related and serve as a proxy for qualitative representation of ET_c.     

Implications of ocean color in the upper water thermal structure at NINO3.4 region: a sensitivity study for optical algorithms and ocean color variabilities

Chlorophyll a (Chl-a) has been the most commonly used biomass metric in biological oceanographic processes. Although limited to two-dimensional surfaces, remote-sensing tools have been successfully providing the most recent state of marine phytoplankton biomass to better understand bottom-up processes initiating daily marine material cycles. In this exercise, ocean color products with various time-scales, derived from Sea-Viewing Wide Field-of-View Sensor (SeaWiFS), were used to investigate how their bio-optical properties affect the upper-ocean thermal structure in a global ocean modeling framework. This study used a ¼-degree Hybrid Coordinate Ocean Model forced by hourly atmospheric fluxes from the Climate Forecast System Reanalysis at National Oceanic Atmospheric Administration. Three numerical experiments were prepared by combining two ocean color products – downwelling diffuse attenuation coefficients (K_dPAR) and chlorophyll a (Chl-a) – and two shortwave radiant flux algorithms. These three runs are: (1) KparCLM, based on a 13-year long-term climatological K_dPAR derived from SeaWiFS; (2) ChlaCLM, based on a 13-year long-term Chl-a derived from SeaWiFS; and (3) ChlaID, which uses the inter-annual time-series of monthly-mean SeaWiFS Chl-a product. The KparCLM experiment uses a Jerlov-like two-band scheme; whereas, both ChlaCLM and ChlaID use a two-band scheme that considers inherent (absorption (a) and backscattering (b_b) coefficients) and apparent optical properties (downwelling attenuation coefficient (K_d) and solar zenith angle (θ, varying 0–60°)). It is found that algorithmic differences in optical parameterizations have a bigger impact on the simulated temperatures in the upper-100 m of the eastern equatorial Pacific, NINO3.4 region, than other parts of the ocean. Overall, the K_dPAR-based approach estimated relatively low surface temperatures compared to those estimated from the chlorophyll-based method. In specific, this cold bias, pronounced in the upper 20–30 m, is speculated to be due to optical characteristics of the algorithm and K_dPAR products, or due to nonlinear hydrodynamical processes involving displacement of mixed-layer depth. Comparisons between each experiment against Global Ocean Data Assimilation System (GODAS; Behringer and Xue 2004) analyses find that KparCLM-based simulations have lower mean differences and variabilities with higher cross-correlation coefficients compared to ChlaCLM- and ChlaID-based experiments.     

Zonation of coastal sediments based on the effective properties on the accumulation of heavy metals using the IDW and kriging method (case study: SW Iran)

Abstract

This study investigated the effects of some physicochemical properties of sediments on the accumulation of heavy metals in portions of the Musa creek coasts (Jafari and Petrochemical creeks). Effective properties such as pH, EC, texture, G_S, γ_d, n, CaCO₃ and OM were determined. All variables showed a normal distribution and general trends of NW–SE and NE–SW. After detrending the variables, ordinary kriging was used for modelling. The C₀/σ₂, C₀/σ_2, and search radius criteria were used to select the best semivariogram. All the variables displayed a spatial structure with different intensities. The IDW method was also used for estimation. The cross-validation showed that the results of both IDW and kriging methods are almost similar. Distribution of the sand particle, G_S, n and OM decreases with distance from the waterways, whilst clay–silt deposits. In the center of the studied area, CaCO₃ has the highest value and EC has the lowest value.     

Modelling of urban growth boundary using geoinformatics

Abstract

Urban growth boundary (UGB) is a regulatory measure of local government for delineating limits of urban growth over a period of time. Land within the UGB allows urban development, while the land outside of this boundary remains primarily non-urban. The increasing popularity of UGB demands an easy and effective method to design this boundary. This article introduces a new concept, Ideal Urban Radial Proximity (IURP), to designate a spatial UGB using geoinformatics in the digital environment. The Kolkata urban agglomeration was considered to demonstrate this model. Remotely sensed imageries of three temporal instants (years 1975, 1990 and 2005) were considered to determine the information on urban extent and growth of the city. These data were then used as inputs to model the UGB for the years 2020 and 2035. The proposed model discourages scattered development and increase in urban growth rate. It preserves urban vegetation, water bodies and any other important non-urban areas within the inner city space. The IURP concept will also be useful to make the cities circular and polycentric urban blobs into a monocentric tract. Apart from the proposed model and derived results, this research also proves the potential of geoinformatics in modelling a UGB.     

A neural network based urban growth model of an Indian city

The aim of the study reported in this paper is to demonstrate that the subjectivity in urban growth modeling and the calibration time can be reduced by using objective techniques like Artificial neural network (ANN). As a case study, the ANN-based model was applied to simulate the urban growth of Saharanpur city in India. In the proposed model, remote sensing and GIS were used to generate site attributes, while ANN was used to reveal the relationships between urban growth potential and the site attributes. Once ANN learnt the relationship, it was then used to simulate the urban growth. Different feed forward ANN architectures were evaluated in this study and finally the most optimum ANN architecture was selected for future growth simulation. The simulated urban growth maps were evaluated on a cell by cell matching using Kappa index and three spatial metrices namely, Mean Patch Fractal Dimension, Landscape Shape Index and Percentage of like Adjacencies. The most optimal architecture was then used subsequently for simulating the future urban growth. The study results thus, demonstrated that the ANN-based model can objectively simulate urban growth, besides successfully coupling GIS, remote sensing and ANN.     

Ross-Li核驱动模型热点参数化及其校正—以POLDER数据为例

半经验、核驱动二向性反射分布函数(BRDF)模型是多角度遥感领域的一个重要模型,其热点效应在应用BRDF反演其它地表参数时有重要作用。本研究针对树冠内部叶片的间隙对热点的影响,对几何光学核的重叠函数进行热点效应的改进,并利用POLDER多角度观测数据集,对改进热点效应后的不同核函数组合模型的最优热点参数进行了确定。通过最小均方根误差(RMSE)筛选出最优热点参数,进一步分析不同模型对热点参数的敏感性和RMSE随热点参数的变化情况。结果表明:(1)该热点参数化方法可用于Ross-Li核驱动模型不同核函数组合的情况,热点校正后的模型相对于原模型很好地改善了对热点反射率的拟合能力;(2)热点参数最优值在几何光学核为LSRC (LiSparseRChen)与LDRC (LiDenseRChen)组成的模型中出现明显差别,C1在LDRC模型中的值远小于LSRC模型,主要是因为LDRC核函数自身较好考虑了树冠尺度下的热点效应,所以该热点参数改进方法起到的补偿作用较小;(3)总体上,同一模型的C1参数比C2参数对热点的变化更敏感。本研究为Ross-Li核驱动模型的热点效应进一步校正及热点参数的取值范围提供了依据,对Ross-Li模型的推广有重要意义,改进热点效应后的模型可用于未来国产多角度卫星的数据处理流程中,以获取加精确的地物热点反射率信息。     

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

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

A VLBI Delay Model for Radio Sources at a Finite Distance

A relativistic delay model for Earth-based very long baseline interferometry (VLBI) observation of sources at finite distances is derived. The model directly provides the VLBI delay in the scale of terrestrial time. The effect of the curved wave front is represented by using a pseudo source vector K = (R ₁ + R ₂)/(R ₁ + R ₂), and the variation of the baseline vector due to the difference of arrival time is taken into account up to the second-order by using Halley’s method. The precision of the new VLBI delay model is 1 ps for all radio sources above 100 km altitude from the Earth’s surface in Earth-based VLBI observation. Simple correction terms (parallax effect) are obtained, which can also adopt the consensus model (e.g. International Earth Rotation and Reference Frames Service conventions) to finite-distance radio source at R > 10 pc with the same precision. The new model may enable estimation of distance to the radio source directly with VLBI delay data.     

地球引力位球谐级数展开的收敛性研究(英文)

Given a continuous boundary value on the boundary of a "simply closed surface"S that encloses the whole Earth, a regular harmonic fictitious field V*(P) in the domain outside an inner sphere K i that lies inside the Earth could be determined, and it is proved that V*(P) coincides with the Earth’s real field V(P) in the whole domain outside the Earth. Since in the domain outside the inner sphere Ki and the fictitious regular harmonic function V*(P) could be expressed as a uniformly convergent spherical harm...     

Seasonal variation of carbon fluxes in a sparse savanna in semi arid Sudan

Background

Large spatial, seasonal and annual variability of major drivers of the carbon cycle (precipitation, temperature, fire regime and nutrient availability) are common in the Sahel region. This causes large variability in net ecosystem exchange and in vegetation productivity, the subsistence basis for a major part of the rural population in Sahel. This study compares the 2005 dry and wet season fluxes of CO₂ for a grass land/sparse savanna site in semi arid Sudan and relates these fluxes to water availability and incoming photosynthetic photon flux density (PPFD). Data from this site could complement the current sparse observation network in Africa, a continent where climatic change could significantly impact the future and which constitute a weak link in our understanding of the global carbon cycle.

Results

The dry season (represented by Julian day 35–46, February 2005) was characterized by low soil moisture availability, low evapotranspiration and a high vapor pressure deficit. The mean daily NEE (net ecosystem exchange, Eq. 1) was -14.7 mmol d^-1 for the 12 day period (negative numbers denote sinks, i.e. flux from the atmosphere to the biosphere). The water use efficiency (WUE) was 1.6 mmol CO₂ mol H₂O^-1 and the light use efficiency (LUE) was 0.95 mmol CO₂ mol PPFD^-1. Photosynthesis is a weak, but linear function of PPFD. The wet season (represented by Julian day 266–273, September 2005) was, compared to the dry season, characterized by slightly higher soil moisture availability, higher evapotranspiration and a slightly lower vapor pressure deficit. The mean daily NEE was -152 mmol d^-1 for the 8 day period. The WUE was lower, 0.97 mmol CO₂ mol H₂O^-1 and the LUE was higher, 7.2 μmol CO₂ mmol PPFD^-1 during the wet season compared to the dry season. During the wet season photosynthesis increases with PPFD to about 1600 μmol m^-2s^-1 and then levels off.

Conclusion

Based on data collected during two short periods, the studied ecosystem was a sink of carbon both during the dry and wet season 2005. The small sink during the dry season is surprising and similar dry season sinks have not to our knowledge been reported from other similar savanna ecosystems and could have potential management implications for agroforestry. A strong response of NEE versus small changes in plant available soil water content was found. Collection and analysis of flux data for several consecutive years including variations in precipitation, available soil moisture and labile soil carbon are needed for understanding the year to year variation of the carbon budget of this grass land/sparse savanna site in semi arid Sudan.     

Thinking about Space‐Time Connections: Spatiotemporal Scheduling of Individual Activities

This article presents a spatiotemporal model for scheduling applications that is driven by the events and activities individuals plan and manage every day. The framework is presented using an ontological approach where ontologies at different levels of generalization, e.g. domain, application, and task ontologies, are linked together through participation and inheritance relationships. S_Events are entered into a schedule as a new S_Entry, or modifications can be made to existing entries including reschedule, postpone, change location, and delete as schedules vary over time. These schedule updates are formalized through changes to planned start and end times and the planned locations of S_Entries are expressed using SWRL, a semantic web rule language. SWRL is also used for reasoning about schedule changes and the space‐time conflicts that can occur. The sequence of entries in a schedule gives rise to S_trajectories representing the locations that individuals plan to visit in order to carry out their schedule, adding an additional spatial element to the framework. A prototype Geoscheduler application maps S_Entries against a timeline, offering a spatiotemporal visualization of scheduled activities showing the evolution of a schedule over space‐time and affecting spatiotemporal accessibility for individuals.     

Effect of cover management factor in quantification of soil loss: case study of Sungai Akah subwatershed,Baram River basin Sarawak,Malaysia

The present study evaluates the effectiveness and suitability of cover management factors (C factor) generated through different techniques like land use/land cover-based arbitrary value (C_LULC), Normalised Different Vegetation Index-based methods C_NDVI1 and C_NDVI2 and Modified Soil Adjusted Vegetation Index 2-based method (C_MSAVI2). The C factors generated using these four methods were tested in the calculation and assessment of annual average soil loss from an upland forested subwatershed in the Baram river basin using the Revised Universal Soil Loss Equation (RUSLE). The four cover management factor maps generated by this analysis show some variation among the results. The LULC method uses a single arbitrary value for each LULC type mapped in the subwatershed. The other three methods show a range of C values within each mapped LULC type. The effects of these variations were tested in the RUSLE by keeping the factors such as rainfall erosivity (R), soil erodibility (K), slope-length and steepness (LS) constant. The maximum annual average soil loss is 1191 t. ha^?1. y^?1 based on the C_LULC. Soil losses estimated with other three methods are very different compared to those estimated with the C_LULC method. The highest calculated soil loss values were 1832, 1674 and 1608 t. ha^?1. y^?1 in the study area based, respectively, on C_NDVI1, C_NDVI2 and C_MSAVI2 C factors. These maximum values represent the worst pixel scenario values of soil loss in the region. The statistical analysis performed indicates different relationship between the parameters and suggests the acceptance of the methodology based on C_NDVI2 for the study area, instead of a single value method such as C_LULC. Among the other two methods, the C_MSAVI2 was found to be more consistent than the C_NDVI1 method, but both methods lead to over-prediction of annual soil loss rate and therefore need to be reconsidered before applied in the RUSLE.     

Parameterization of air temperature in high temporal and spatial resolution from a combination of the SEVIRI and MODIS instruments

Some applications, e.g. from traffic or energy management, require air temperature data in high spatial and temporal resolution at two metres height above the ground (T_2m), sometimes in near-real-time. Thus, a parameterization based on boundary layer physical principles was developed that determines the air temperature from remote sensing data (SEVIRI data aboard the MSG and MODIS data aboard Terra and Aqua satellites). The method consists of two parts. First, a downscaling procedure from the SEVIRI pixel resolution of several kilometres to a one kilometre spatial resolution is performed using a regression analysis between the land surface temperature (LST) and the normalized differential vegetation index (NDVI) acquired by the MODIS instrument. Second, the lapse rate between the LST and T_2m is removed using an empirical parameterization that requires albedo, down-welling surface short-wave flux, relief characteristics and NDVI data. The method was successfully tested for Slovenia, the French region Franche-Comté and southern Germany for the period from May to December 2005, indicating that the parameterization is valid for Central Europe. This parameterization results in a root mean square deviation RMSD of 2.0 K during the daytime with a bias of −0.01 K and a correlation coefficient of 0.95. This is promising, especially considering the high temporal (30 min) and spatial resolution (1000 m) of the results.     

基于WOFOST模型与UAV数据的玉米生长后期地上生物量估算

地上生物量能够有效反映作物的生长状态,其信息的实时估算对产量预测和农田生产管理都有重要意义。作物生长模型因其详尽的生理生化基础和对生长过程数字化描述能力,成为生物量估算的理想模型。近年来,研究人员利用数据同化算法将时间序列遥感数据同化到作物生长模型中,实现了作物模型由基于气象站的点模拟到区域尺度面模拟的外推,使生物量模拟结果同时具备大范围和机理性两个方面的特点。这一模式下,时间序列的遥感数据质量将对生物量模拟精度产生直接影响,作物生长后期受到光谱饱和的影响,遥感数据的作物冠层信息获取能力会出现明显下降,因此有必要对该阶段遥感数据和作物模型的结合方式进行优化,提升生物量模拟精度。本文针对东北地区春玉米生物量遥感估算存在的问题,提出了利用WOFOST作物模型结合无人机（UAV）遥感数据实现作物生长后期生物量准确估算的新思路。新思路首先利用多光谱遥感数据获取WOFOST模型具备较高空间异质性的土壤速效养分参数以提升模型的空间信息模拟能力,使其能在一定程度上摆脱点尺度模拟的限制。同时,结合集合卡尔曼滤波算法将生长前期无人机（UAV）遥感数据同化到模型中,以缩短模型单独运行时间,减少模型运行过程中的参数误差累积,实现无遥感数据参与下的短期作物生长模拟,并输出生长后期相应的生物量模拟结果。最后,本文利用地面实测数据对新方法的生物量模拟精度进行了评价。结果表明,与全生育期数据同化相比,新方法的生物量估算精度有了明显的提升（全生育期同化：R² = 0.45,RMSE = 4254.30 kg/ha;新方法：R²= 0.86,RMSE = 2216.79 kg/ha）。     

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

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