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In situ soil moisture measurement networks are essential for developing, improving and validating satellite soil moisture products. In the east of the Netherlands, an area susceptible to droughts, a monitoring network for soil moisture and – temperature has been operational since 2009. Spread across an area of 45 by 40 km, twenty profile monitoring stations observe moisture and temperature in the root zone. Four field campaigns were conducted in order to calibrate the sensors and to assess the spatial representativeness of the measurements.The network has proven to be of great value for validation of satellite products (e.g. for SMAP soil moisture). In addition, continuation of the measurements will increase its value for climate studies. Currently, the network is being redesigned to better suit operational water management in the region, while preserving the value of long time series for climatological research, and taking into account its potential value for future missions. Here, we present an overview of the network and the observations since its establishment, including the adjustments that are being made to the network and research opportunities.Van der Velde, R., Benninga, H.J.F., Retsios, B., Vermunt, P.C., Salama, M.S. (2022) - Twelve years profile soil moisture and temperature measurements in Twente, the Netherlands. Earth System Science Data Discussions, 1-44.Velde, dr. ir. R van der (University of Twente) (2022): Twelve years profile soil moisture and temperature measurements in Twente, the Netherlands. DANS. https://doi.org/10.17026/dans-znj-wyg5

Many coastal waters include large areas of Optically Shallow Waters (OSWs) where the sea-bottom affects above-water observations of remote sensing reflectance (Rrs [sr−1]). If not treated, the effect of bottom reflectance will interfere with the correct retrieval of Water Constituent Concentrations (WCCs) from hyperspectral and multispectral remote sensing observations. To study this phenomenon in more detail, the existing semi-infinite 2SeaColour Radiative Transfer (RT) model was modified into a finite water layer model, bounded by a diffusely reflecting surface at the sea-bottom. From simulations with the new model, called Water - Sea Bottom (WSB) model, it was observed that a ratio of spectral bands in the Near-Infrared, bands 750 nm and 900 nm, is nearly insensitive to the WCCs and increases with the shallowness of the water, and therefore can be used as a robust index to detect OSWs. The newly established Near-Infrared Bottom Effect Index (NIBEI) was applied to a series of satellite observations over the Wadden Sea during high and low tidal phases. Images from the MEdium Resolution Imaging Spectrometer (MERIS) and the Ocean and Land Colour Instrument (OLCI) were processed to retrieve WCCs of the study area. The results indicate that the sea-bottom effect in OSWs affects the accuracy of atmospheric correction and retrievals. On the other hand, applying the NIBEI to flag OSWs improves the reliability and consistency of WCCs maps. The application of proposed NIBEI on satellite images requires only Top Of Atmosphere (TOA) radiances at 750 nm and 900 nm and does not depend on atmospheric correction and ancillary local input data (e.g., bathymetry map, bottom type, empirical coefficients, in-situ measurements). As a result, the proposed NIBEI can readily be applied to detect OSWs on various ocean colour remote sensors in various shallow coastal regions.

Recently, there have been significant efforts in the integration of in-situ and satellite observations for effective monitoring of coastal areas (e.g., the Copernicus program of the European Space Agency). In this study, a 15-year diurnal variation of Water Constituent Concentrations (WCCs) was retrieved from multi-sensor satellite images and in-situ hyperspectral measurements using Radiative Transfer (RT) modeling in the Dutch Wadden Sea. The existing RT model 2SeaColor was inverted against time series of in-situ hyperspectral measurements of water leaving reflectances (Rrs [sr−1]) for the simultaneous retrieval of WCCs (i.e., Chlorophyll-a (Chla), Suspended Particulate Matter (SPM), Dissolved Organic Matter (CDOM)) on a daily basis between 2003 and 2018 at the NIOZ jetty station (the NJS) located in the Dutch part of the Wadden Sea. At the same time, the existing coupled atmosphere-hydro-optical RT model MOD2SEA was used for the simultaneous retrieval of WCCs from time series of multi-sensor satellite images of the MEdium Resolution Imaging Spectrometer (MERIS) onboard ENVISAT, Multispectral Instrument (MSI) onboard Sentinel-2 and Ocean and Land Colour Instrument (OLCI) onboard Sentinel-3 between 2003 and 2018 over the Dutch Wadden Sea. At the NJS, a direct comparison (Taylor diagram and statistical analysis) showed strong agreement between in-situ and satellite-derived WCC values (Chla: R2 ≥ 0.70, RMSE ≤7.5 [mg m−3]; SPM: R2 ≥ 0.72, RMSE ≤5.5 [g m−3]; CDOM absorption at 440 nm: R2 ≥ 0.67, RMSE ≤1.7 [m−1]). Next, the plausibility of the spatial variation of retrieved WCCs over the study area was evaluated by generating maps of Chla [mg m−3], SPM [g m−3], and CDOM absorption at 440 nm [m−1] from MERIS and OLCI images using the MOD2SEA model. The integration of the spatio-temporal WCC data obtained from in-situ measurements and satellite images in this study finds applications for the detection of anomaly events and serves as a warning for management actions in the complex coastal waters of the Wadden Sea.

The 2SeaColor model (Salama & Verhoef, 2015) was proposed to analytically retrieve the diffuse attenuation coefficient (Kd) from remote sensing reflectance (Rrs), but its parameterization was based on approximations and subjected to large uncertainties. In this study, we present an improvement on the parameterization equations in the inverse scheme of the 2Seacolor model. The improved model is then validated with three in-situ datasets and compared with the Zhang model (Zhang & Fell, 2007) and the Lee model (Lee, Darecki et al., 2005). Validation with radiometric data shows that the 2SeaColor model provides the best estimates of Kd for the full range of observations, with the largest determination coefficient (R2 = 0.935) and the smallest root mean squared error (RMSE = 0.078 m− 1). For clear waters, where Kd(490) 0.2 m− 1, the 2SeaColor model is found to be more accurate, with an RMSE of 0.186 m− 1, compared to RMSEs of 0.279 m− 1 and 0.388 m− 1 for the Zhang model and the Lee model, respectively. The 2SeaColor model is finally applied to the GOCI (Geostationary Ocean Color Imager) level 2 product (L2P) to produce Kd maps over the Yangtze Estuary, resulting in a reasonable distribution and expected range of Kd, as for example Kd(490) was varying from 0.04 to 9.82 m− 1 for the image acquired at 02:16 UTC, on March 8th 2013. The analytical 2SeaColor model is able to provide consistently stable and fairly accurate Kd estimates in both clear and turbid waters without the need of tuning empirical coefficients from field measurements, and thus has great potential for estimating Kd over optically complex waters.

In this study, the effect of tide on the variation of concentrations of Chlorophyll-a (Chla) and Suspended Particulate Matters (SPM), retrieved from a complete dataset of diurnal close-range hyperspectral observations recorded at the NIOZ jetty station (NJS) located at the Marsdiep inlet in the Dutch part of the Wadden Sea, was evaluated. The two-stream radiative transfer model 2SeaColor was inverted to retrieve Chla and SPM concentrations per each hyperspectral observation of the quality-controlled dataset. Concurrently with these diurnal observations, tidal information was obtained from the Den Helder station located at 3.7 km from the NJS. From the performed analysis and evaluation of this study, we concluded that the tide has little observable effects on the diurnal changes of SPM concentration at the NJS located in the Dutch part of the Wadden Sea. The results of this evaluation and the favorable location of the NJS, which is not influenced by the tidal phase, will contribute to a better understanding of the seasonal variability of the retrieved Chla and SPM concentration values using diurnal optical observations at the NJS. These long-term retrievals can be used later to do the phenological analysis of Chla concentration values in this region.

A fundamental measure to characterize water quality is the attenuation coefficient for downward planar irradiance in the water column. In this paper we present an analytical forward model and develop an inversion scheme (2SeaColor) to retrieve the downwelling attenuation coefficients from remote sensing data. The forward formulation of 2SeaColor provides an analytical and stable solution of the direct and diffuse components of the radiative transfer equation. The common high turbidity of inland waters is accounted for in 2SeaColor by projecting its effect on the inherent optical properties (IOPs) using the similarity transform. The inversion scheme of 2SeaColor uses a novel approach to parameterize the spectral dependency of IOPs. In this parameterization 2SeaColor uses the curvature information of observed remote sensing spectra to deduce the spectral-shape of the total absorption coefficient. The forward model is verified using simulated data from the International Ocean Colour Coordinating Group (IOCCG data) and is compared with two existing models. Statistical analysis results of model verification show that the forward model is able to derive Kd values with 3% accuracy (relative mean absolute error). Our solution provides an improvement on the accuracy of the estimated Kd when compared to other models, and especially at larger depths. The inversion scheme is validated against three sets of data: IOCCG, field measured and satellite observed matchups. Field measurements were collected in the turbid Naivasha Lake, Kenya. The mean absolute relative error in the derived Kd in all data sets did not exceed 15%, with the satellite matchup set resulting in the largest discrepancy. The developed model, 2SeaColor, is stable to noise up to 15% and consistently produces accurate results which make it an ideal candidate model for the future Ocean and Land Colour Instrument (OLCI) on board the Sentinel-3 mission.

Earth observations (EOs) following empirical and/or analytical approaches are a feasible alternative to obtain spatial and temporal distribution of water quality variables. The limitations observed in the use of empirical approaches to estimate high concentrations of suspended particulate matter (SPM) in the estuarine water of Guadalquivir have led the authors to use a semi-analytical model, which relates the water constituents’ concentration to the water leaving reflectance. In this work, the atmospheric correction has been carried out simultaneously and the aerosol reflectance and backscattering coefficients of SPM obtained. The results are validated using in situ SPM data series provided by a monitoring network in the study area. The results show that the model allows us to successfully estimate backscattering coefficients of SPM in the estuary, differentiating clear and turbid water and using two ε(4,5) .These considerations improve the value of R2 from 0.68 (single ε(4,5)) to 0.86 (two ε( 4,5)) on 18 May 2009. This method could be used as a preliminary approach to obtain SPM concentration in the Guadalquivir estuary with the limitations that the model shows for turbid waters.

This study aimed to investigate the capability of the two-stream radiative transfer model 2SeaColor for the simultaneous retrieval of Chlorophyll-a (Chla), Suspended Particulate Matter (SPM) and Colored Dissolved Organic Matter (CDOM) concentrations from remote sensing measurements under various conditions (i.e., solar zenith angle values (SZAs) and water turbidity levels). For this evaluation, a time series of diurnal in-situ hyperspectral measurements of remote sensing reflectance (Rrs) concurrent with in-situ measured Chla and SPM concentrations between 2008 and 2010 by the NIOZ jetty station (the NJS), located in the Dutch part of the Wadden Sea, was used. Validation of the model retrievals against in-situ measurements showed an acceptable accuracy (Chla: R2= 0.80 and RMSE = 2.98 [mg m−3]; SPM: R2= 0.89 and RMSE = 2.53 [g m−3]) with good agreement between the temporal trends of measured and retrieved concentration values over multiple years. However, the model inversion results yielded less good estimates at SZA > 60° during winter. Furthermore, the effect of the tide on the variation of daily time series of Chla and SPM concentrations was analyzed. At the particular NJS location, the tidal effects on the concentrations of SPM and Chla were found to be small. The capability of the 2SeaColor model to retrieve reliable estimates, and the favorable location of the NJS, which is little influenced by tidal phase variations, contribute to a better understanding of the long-term variability of Chla and SPM concentrations. The results of this study may support the ongoing efforts on Sentinel-3 Ocean and Land Color Instrument (OLCI) calibration and validation at the Dutch Wadden Sea.

Freshwater resources are deteriorating rapidly due to human activities and climate change. Remote sensing techniques have shown potential for monitoring water quality in shallow inland lakes, especially in data-scarce areas. The purpose of this study was to determine the spectral diffuse attenuation coefficient (Kd(λ)) of the water column, in order to map the euphotic depth (Zeu) of Lake Naivasha, Kenya using the Medium Resolution Imaging Spectrometer (MERIS). Intensive in situ radiometric and limnological data collection was undertaken at Lake Naivasha. Atmospheric correction was done on the MERIS images using MERIS Neural Network algorithms, Case 2 Waters (C2R) and Eutrophic Lakes processors and the bright pixel atmospheric correction algorithm (BPAC). The Eutrophic Lakes processor gave the most accurate atmospherically corrected remote sensing reflectances at 490 nm compared to the other processors, with mean absolute percentage error (MAPE) of 47% and a root mean square error (RMSE) 43%, with BPAC giving negative reflectances in the blue spectral range. In situ Kd and Zeu models were calibrated and validated using above- and under-water radiometric measurements, and tested on the Neural Network atmospheric correction processed MERIS images. The Eutrophic Lakes estimates were the most accurate, with an RMSE of 0.26 m− 1 and MAPE of 18% for Kd(490) and RMSE of 0.17 m and MAPE of 14% for Zeu. The Zeu maps produced from MERIS images clearly show the variation of euphotic depth both in space and time. These results indicate the suitability of MERIS to monitor euphotic depth and other water quality parameters of shallow inland water bodies.

Understanding the sources of uncertainty that cause deviations between simulated and satellite-observed states can facilitate optimal usage of these products via data assimilation or calibration techniques. A method is presented for separating uncertainties following from (i) scale differences between model grid and satellite footprint, (ii) residuals inherent to imperfect model and retrieval applications, and (iii) biases in the climatologies of simulations and retrievals. The method is applied to coarse (10 km) soil moisture simulations by the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5)–Noah regional climate model and 2.5 years of high-resolution (100 m) retrievals from the Advanced Synthetic Aperture Radar (ASAR) data collected over central Tibet. Suppression of the bias is performed via cumulative distribution function (CDF) matching. The other deviations are separated by taking the variance of the ASAR soil moisture at the coarse MM5 model grid as measure for the deviations caused by scale differences. Via decomposition of the uncertainty sources it is shown that the bias and the spatial-scale difference explain the majority (>70%) of the deviations between the two products, whereas the contribution of model–observation residuals is less than 30% on a monthly basis. Consequently, this study demonstrates that accounting for uncertainties caused by bias as well as spatial-scale difference is imperative for meaningful assimilation of high-resolution soil moisture products. On the other hand, the large uncertainties following from spatial-scale differences suggests that high-resolution soil moisture products have a potential of providing observation-based input for the subgrid spatial variability parameterizations within large-scale models.

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.

The wide swath (WS) and global monitoring (GM) modes of the Advanced Synthetic Aperture Radar (ASAR) provide for monitoring purposes a good compromise between the spatial coverage, spatial resolution and revisit time. Similar imaging modes will be a part of the Sentinel-1 SAR sensing system. A consequence of operating at the wider swath is, however, the rather large view angle range (about 16–43°), which needs to be corrected for if biophysical variables are to be retrieved from these data sets. A soil moisture retrieval algorithm based on the Integral Equation Method (IEM) scattering model is presented that explicitly simulates the backscatter as a function of the incidence angle instead of normalizing. This algorithm is applied to 150 ASAR WS scenes collected from April 2005 to September 2007 over the central part of the Tibetan Plateau. We selected three of these images covering the study area at different view angles during the winter season for the IEM roughness derivation. Subsequently, soil moisture is retrieved from the entire σ o data sets via inversion of the IEM model with the obtained roughness parameters as input. The retrievals are validated against soil moisture measured from July 2006 to September 2007 at four sites representative for a 10 × 10 km 2 area. Further, the uncertainties following from assumptions made for the development of the algorithm are also quantified.

Variations of land surface parameters over the Tibetan Plateau have great importance on local energy and water cycles, the Asian monsoon, and climate change studies. In this paper, the NOAA/NASA Pathfinder Advanced Very High Resolution Radiometer (AVHRR) Land (PAL) dataset is used to retrieve the land surface temperature (LST), the normalized difference vegetation index (NDVI), and albedo, from 1982 to 2000. Simultaneously, meteorological parameters and land surface heat fluxes are acquired from the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) dataset and the Global Land Data Assimilation System (GLDAS), respectively. Results show that from 1982 to 2000 both the LST and the surface air temperature increased on the Tibetan Plateau (TP). The rate of increase of the LST was 0.26±0.16 K decade−1 and that of the surface air temperature was 0.29 ± 0.16 K decade−1, which exceeded the increase in the Northern Hemisphere (0.054 K decade−1). The plateau-wide annual mean precipitation increased at 2.54 mm decade−1, which indicates that the TP is becoming wetter. The 10-m wind speed decreased at about 0.05±0.03 m s−1 decade−1 from 1982 to 2000, which manifests a steady decline of the Asian monsoon wind. Due to the diminishing ground–air temperature gradient and subdued surface wind speed, the sensible heat flux showed a decline of 3.37 ± 2.19 W m−2 decade−1. The seasonal cycle of land surface parameters could clearly be linked to the patterns of the Asian monsoon. The spatial patterns of sensible heat flux, latent heat flux, and their variance could also be recognized.

Specific inherent optical properties (SIOP) of the Berau coastal waters were derived from in situ measurements and inversion of an ocean color model. Field measurements of water-leaving reflectance, total suspended matter (TSM), and chlorophyll a (Chl a) concentrations were carried out during the 2007 dry season. The highest values for SIOP were found in the turbid waters, decreasing in value when moving toward offshore waters. The specific backscattering coefficient of TSM varied by an order of magnitude and ranged from 0.003 m2 g−1, for clear open ocean waters, to 0.020 m2 g−1, for turbid waters. On the other hand, the specific absorption coefficient of Chl a was relatively constant over the whole study area and ranged from 0.022 m2 mg−1, for the turbid shallow estuary waters, to 0.027 m2 mg−1, for deeper shelf edge ocean waters. The spectral slope of colored dissolved organic matter light absorption was also derived with values ranging from 0.015 to 0.011 nm−1. These original derived values of SIOP in the Berau estuary form a corner stone for future estimation of TSM and Chl a concentration from remote sensing data in tropical equatorial waters.

Satellite estimation of chlorophyll-a (Chl-a) concentration in the Changjiang Estuary is challenging for ocean-colour retrieval algorithms. The Changjiang Estuary is characterized by suspended-sediment-dominated waters in the mouth and optically complex case 2 waters offshore of the mouth. Satellite ocean-colour products show that high-sediment loads of estuarine waters can cause overestimations or invalid flags of Chl-a concentration. A synthetic chlorophyll index (SCI) was designed for extracting chlorophyll information and for minimizing the influence of sediments on the remote-sensing reflectance spectrum. An SCI algorithm, a quadratic polynomial function of the SCI versus Chl-a concentration, was applied to the estimation of Chl-a concentration from Medium Resolution Imaging Spectrometer (MERIS) images. The overestimation of Chl-a concentration was corrected. The SCI algorithm has applications for MERIS estimation of Chl-a concentration in turbid waters with a moderate to high suspended-sediment concentration.

Improving the inversion of ocean color data is an ever continuing effort to increase the accuracy of derived inherent optical properties. In this paper we present a stochastic inversion algorithm to derive inherent optical properties from ocean color, ship and space borne data. The inversion algorithm is based on the cross-entropy method where sets of inherent optical properties are generated and converged to the optimal set using iterative process. The algorithm is validated against four data sets: simulated, noisy simulated in-situ measured and satellite match-up data sets. Statistical analysis of validation results is based on model-II regression using five goodness-of-fit indicators; only R 2 and root mean square of error (RMSE) are mentioned hereafter. Accurate values of total absorption coefficient are derived with R 2 > 0.91 and RMSE, of log transformed data, less than 0.55. Reliable values of the total backscattering coefficient are also obtained with R 2 > 0.7 (after removing outliers) and RMSE < 0.37. The developed algorithm has the ability to derive reliable results from noisy data with R 2 above 0.96 for the total absorption and above 0.84 for the backscattering coefficients. The algorithm is self contained and easy to implement and modify to derive the variability of chlorophyll-a absorption that may correspond to different phytoplankton species. It gives consistently accurate results and is therefore worth considering for ocean color global products.

The Tibetan Plateau is a region sensitive to climate change, due to its high altitude and large terrain. This sensitivity can be measured through the response of vegetation patterns to climate variability in this region. Time series analysis of Normalized Difference Vegetation Index (NDVI) imagery and correlation analyses are effective tools to study land cover changes and their response to climatic variations. This is especially important for regions like the Tibetan Plateau, which has a complex ecosystem but lacks a lot of detailed in-situ observation data due to its remoteness, vastness and the severity of its climatic conditions. In this research a time series of 315 SPOT VEGETATION scenes, covering the period between 1998 and 2006, has been processed with the Harmonic ANalysis of Time Series (HANTS) algorithm in order to reveal the governing spatiotemporal pattern of variability. Results show that the spatial distribution of NDVI values is in agreement with the general climate pattern in the Tibetan Plateau. The seasonal variation is greatly influenced by the Asian monsoon. Interannual analysis shows that vegetation density (recorded here by the NDVI values) in the entire Tibetan Plateau has generally increased. Using a 1 km resolution land cover map from GLC2000, seven meteorological stations, presenting monthly data on near surface air temperature and precipitation, were selected for correlation analysis between NDVI and climate conditions in this research. A time lag response has also been found between NDVI and climate variables. Except in desert grassland (Shiquanhe station), the NDVI of all selected sites showed strong correlation with air temperature and precipitation, with variations in correlation according to the different land cover types at different locations. The strongest relationship was found in alpine and subalpine plain grass, the weakest in desert grassland.

Remote sensing of water quality in inland waters requires reliable retrieval algorithms, accurate atmospheric correction and consistent method for uncertainty estimation. In this paper, the GSM semi-analytical inversion model is modified for inland waters to derive inherent optical properties (IOPs) and their spectral dependencies from air and space borne data. The modified model was validated using two data sets from the Veluwe and the Vecht Dutch lakes. For the Veluwe lakes, the model was able to derive a linear relationship between measured concentrations and estimated IOPs with R2 values above 0.7 for chlorophyll-a (Chl-a) and up to 0.9 for suspended particulate matters (SPM). In the Vecht lakes, the modified model derived accurate values of IOPs. The R2 values were 0.89 for Chl-a and up to 0.95 for SPM. The RMSE values were 0.93 mg m−3 and 0.56 g m−3 for Chl-a and SPM respectively. Finally, the IOPs of the Veluwe lakes are derived from multi-spectral, ocean color and hyperspectral airborne data. Inversion-uncertainties of the derived IOPs were also estimated using a standard nonlinear regression technique. The study shows that inversion-uncertainties of remote sensing derived IOPs are proportional to water turbidity.

Current earth observation models do not take into account the influence of water salinity on the evaporation rate, even though the salinity influences the evaporation rate by affecting the density and latent heat of vaporization. In this paper, we adapt the SEBS (Surface Energy Balance System) model for large water bodies and add the effect of water salinity to the evaporation rate. Firstly, SEBS is modified for fresh-water whereby new parameterizations of the water heat flux and sensible heat flux are suggested. This is achieved by adapting the roughness heights for momentum and heat transfer. Secondly, a salinity correction factor is integrated into the adapted model. Eddy covariance measurements over Lake IJsselmeer (The Netherlands) are carried out and used to estimate the roughness heights for momentum (~0.0002 m) and heat transfer (~0.0001 m). Application of these values over the Victoria and Tana lakes (freshwater) in Africa showed that the calculated latent heat fluxes agree well with the measurements. The root mean-square of relative-errors (rRMSE) is about 4.1% for Lake Victoria and 4.7%, for Lake Tana. Verification with ECMWF data showed that the salinity reduced the evaporation at varying levels by up to 27% in the Great Salt Lake and by 1% for open ocean. Our results show the importance of salinity to the evaporation rate and the suitability of the adapted-SEBS model (AquaSEBS) for fresh and saline waters.

This paper discusses soil moisture retrievals over the Tibetan Plateau from brightness temperature (TB's) observed by the Special Sensor Microwave Imagers (SSM/I's) during the warm seasons of the period from July 1987 to December 2008. The Fundamental Climate Data Record (FCDR) of F08, F11 and F13 SSM/I satellites by the Precipitation Research Group of Colorado State University is used for this study. A soil moisture retrieval algorithm is developed based on a radiative transfer model that simulates top-of-atmosphere TB's whereby effects of atmosphere are calculated from near-surface forcings obtained from a bias-corrected dataset. Validation of SSM/I retrievals against in situ measurements for a two-and-half year period (225 matchups) gives a Root Mean Squared Error of 0.046 m3 m−3. The agreement between retrievals and Noah simulations from the Global Land Data Assimilation System is investigated to further provide confidence in the reliability of SSM/I retrievals at the Plateau-scale. Normalised soil moisture anomalies (N) are computed on a warm seasonal (May–October) and on a monthly basis to analyse the trends present within the products available from July 1987 to December 2008. The slope of linear regression functions between N and time is used to quantify the trends. Both the warm season and monthly N indicate severe wettings of 0.8 to almost 1.6 decade−1 in the centre of the Plateau. Correlations are found by the trend with elevation for the warm season as a whole and the individual months May, September and October. The observed wetting of the Tibetan Plateau agrees with recent findings on permafrost retreat, precipitation increase and potential evapotranspiration decline.

The study is mainly intended to assess and evaluate the water budget in the eastern basin at 1 km resolution, through a comprehensive model of the eastern aquifer by estimating the evapotranspiration (evaporation and transpiration), surface runoff and groundwater recharge in the targeted aquifer for the period (1950-2000). A spatial modelling approach and geographical information systems (ArcGIS) specialist was used to evaluate and estimate the water budget for the 50 years period (1950-2000). Hydro-meteorological data for the 50 years period were used to drive precipitation, reference evapotranspiration, evapotranspiration, runoff spatial grids, and ground water recharge along with an summed minor losses were integrated by GIS spatial interpolation. The results of this study show that the average annual ground water recharge for the 50 years period in the study area is almost 200 millions of cubic metres (MCM) per year. A variation was found in the recharge values from the eastern slopes till the central highlands in the West Bank, this variation is due to the variation in temperature and precipitation in the targeted area.

The Wadden Sea has undergone many changes of which some (e.g., seagrass disappearance, dredging activities) are thought to have affected the concentrations of suspended particulate matter (SPM) in these waters. Results of previous analyses of long-term variation and trends in SPM are, however, possibly biased by the fact that the data underlying these trends were not corrected for methodological changes in time. In this paper we analyze the variability of Secchi disk measurements recorded at one location in the westernmost part of the Wadden Sea during almost four decades (from 1974 to 2010). The Secchi readings were corrected for varying environmental conditions (solar zenith angle, solar irradiance and sea surface conditions) at the time of observation and then converted to a turbidity proxy that measures the attenuation of light due to suspended and dissolved materials in the water column. We tested a series of hypotheses to describe the seasonal and long-term variations of this turbidity proxy. The best statistical model assumed one common seasonal pattern within the study period and a strong variation in turbidity over the years without any apparent long-term increase or decrease in time (n = 1361; r 2 = 0.53). In addition, we found that most of the turbidity variation in this part of the Wadden Sea can be described as a function of SPM, chlorophyll-a, salinity, water temperature, the filter type used for the SPM determinations, and a still unidentified seasonal factor (n = 401; r 2 = 0.88). Comparison with annual averaged ADCP-derived SPM concentrations as determined from a ferry sailing across the Marsdiep tidal inlet (1998–2008) showed that the variability in turbidity at the sampling station was indicative for the variation in light attenuation in the westernmost part of the Wadden Sea. Because the intensity of the underwater light-field affects primary productivity, this new and consistent information on long-term variation in turbidity is of profound importance to the assessment of long-term changes and underlying mechanisms of the carrying capacity of the Wadden Sea.

The middle reaches of Yarlung Zangbo River (YR) and its two tributaries is a region in central Tibet Autonomous Region where the industrial and agricultural economy is most concentrative and developed. Remote sensing of land surface parameters has significant meaning for crop growth monitoring, crop yield assessment and disaster monitoring. It also has great theoretical importance for understanding energy and water cycle in local area and even for surrounding regions. In this paper, four scenes data in 2003 under nearly clear weather conditions are selected as the spring, summer, autumn and winter cases. Different algorithms for AVHRR and MODIS data will be applied to retrieve land surface temperature (LST), albedo, Normalized Difference Vegetation Index (NDVI), vegetation coverage, emissivity, and water vapour content, respectively. The derived LST, albedo are validated with field measurements and MODIS products. The estimations are in good agreement with in-situ measurements, with MODIS retrievals being slightly better than those of AVHRR. This means that the adopted remote sensing retrieval algorithms are not only applicable but also simple for the study area. As called the "water vapour inflow corridor", the valleys of YR and its two tributaries are proved to have higher LST and larger atmospheric water vapour content. Spatiotemporal variations of land surface parameters in the middle reaches of YR and its two tributaries can also be clearly identified.

This paper describes a hydro-optical model for deriving water quality variables from satellite images, hereafter HydroSat. HydroSat corrects images for atmospheric interferences and simultaneously retrieves water quality variables. An application of HydroSat to Landsat Enhanced Thematic Mapper (ETM) observations over the Rosetta Branch of the Nile River demonstrates that reliable estimates of water quality are obtained. For example, the impact of the many water inlets along the Rosetta on the water quality can be very well identified. Quantitatively, the accuracy of the derived products is assessed via comparison with the output of a validated water quality process model for the Rosetta Branch. This matchup between the HydroSat and process model’s output results in determination coefficients, R 2 s, larger than 0.6 for all derived water quality variables. It should be noted that derivation of water quality variables using HydroSat does not rely on any tuning parameters. Hence, the successful application of HydroSat to Landsat-ETM data could also be seen as a demonstration of the future Sentinel-2 capabilities for mapping water quality over rivers.

We present a method to calibrate and validate observational models that interrelate remotely sensed energy fluxes to geophysical variables of land and water surfaces. Coincident sets of remote sensing observation of visible and microwave radiations and geophysical data are assembled and subdivided into calibration (Cal) and validation (Val) data sets. Each Cal/Val pair is used to derive the coefficients (from the Cal set) and the accuracy (from the Val set) of the observation model. Combining the results from all Cal/Val pairs provides probability distributions of the model coefficients and model errors. The method is generic and demonstrated using comprehensive matchup sets from two very different disciplines: soil moisture and water quality. The results demonstrate that the method provides robust model coefficients and quantitative measure of the model uncertainty. This approach can be adopted for the calibration/validation of satellite products of land and water surfaces, and the resulting uncertainty can be used as input to data assimilation schemes.

The development of an operational water quality monitoring method based on remote sensing data requires information on the apparent and inherent optical properties of water (AOP and IOP respectively). This study was performed to determine the apparent and inherent optical properties of coastal waters of the Mahakam Delta, Kalimantan, Indonesia. Inherent optical properties (IOPs) were derived from above-water radiometric measurements and ocean color model inversion. Retrieved IOPs and measured concentrations show good agreement both for total suspended matter (TSM) and chlorophyll a (Chl a) (R2 = 0.72 and 0.80 respectively). The linear relationship between the retrieved IOPs and the measured concentrations was then used to estimate the specific inherent optical properties (SIOPs) using the basic equation of the Lambert–Beer law. The specific backscattering coefficient of TSM ( b b , TSM ∗ ( 550 ) ) was found to be 0.0087 m2 g−1, and the specific absorption coefficient of Chl a ( a Chl ∗ ( 440 ) ) was found to be 0.023 m2 g−1 in the Mahakam Delta. The estimated values of SIOP for TSM and Chl a could be considered spatially constant for the Mahakam Delta, and resulted in reliable estimates of TSM and Chl a concentrations (R2 = 0.84 and 0.85 respectively). The specific backscattering coefficient of TSM found in this study is similar to that of the Barito Estuary (in the southern part of Kalimantan) but lower than that of the Berau Estuary (in the northern part of Kalimantan), whereas the specific backscattering coefficient of Chl a is similar to that found in the Berau Estuary. This study contributes to the development of an operational method based on remote sensing data to map water constituent concentrations in the Mahakam Delta, as well as to enrich the information about the optical properties of Indonesian waters.

An outdoor tank experiment is carried out to analyse the interrelationships between remote-sensing reflectance and sediment characteristics in the highly turbid waters of the Yangtze River and the Yellow River estuaries. The results show that the sensitivity of remote-sensing reflectance to water turbidity is inversely related to suspended sediment concentration (SSC). SSC estimation in the highly turbid waters (SSC > 0.15 g l-1) is best achieved by using ocean colour ratios, especially the ratio at 810 nm: 700 nm. The effect of particle size of suspended sediment matter (SSM) on the observed remote-sensing reflectance is significant and depends on wavelengths and a SSC range. The mineral composition of SSM has a weak effect on observed reflectance in comparison to that of particle size.

The Changjiang (Yangtze) estuarine and coastal waters are characterized by suspended sediments over a wide range of concentrations from 20 to 2,500 mg l−1. Suspended sediment plays important roles in the estuarine and coastal system and environment. Previous algorithms for satellite estimates of suspended sediment concentration (SSC) showed a great limitation in that only low to moderate concentrations (up to 50 mg l−1) could be reliably estimated. In this study, we developed a semi-empirical radiative transfer (SERT) model with physically based empirical coefficients to estimate SSC from MERIS data over turbid waters with a much wider range of SSC. The model was based on the Kubelka–Munk two-stream approximation of radiative transfer theory and calibrated using datasets from in situ measurements and outdoor controlled tank experiments. The results show that the sensitivity and saturation level of remote-sensing reflectance to SSC are dependent on wavelengths and SSC levels. Therefore, the SERT model, coupled with a multi-conditional algorithm scheme adapted to satellite retrieval of wide-range SSC, was proposed. Results suggest that this method is more effective and accurate in the estimation of SSC over turbid waters.

Estimation of large-scale land surface temperature from satellite images is of great importance for the study of climate change. This is especially true for the most challenging areas, such as the Tibetan Plateau (TP). In this paper, two split window algorithms (SWAs), one for the NOAA’s Advanced Very High Resolution Radiometer (AVHRR), and the other for the Moderate Resolution Imaging Spectroradiometer (MODIS), were applied to retrieve land surface temperature (LST) over the TP simultaneously. AVHRR and MODIS data from 17 January, 14 April, 23 July, and 16 October 2003 were selected as the cases for winter, spring, summer, and autumn, respectively. Firstly, two key parameters (emissivity and water vapor content) were calculated at the pixel scale. Then, the derived LST was compared with in situ measurements from the Coordinated Enhanced Observing Period (CEOP) Asia-Australia Monsoon Project (CAMP) on the TP (CAMP/Tibet) area. They were in good accordance with each other, with an average percentage error (PE) of 10.5% for AVHRR data and 8.3% for MODIS data, meaning the adopted SWAs were applicable in the TP area. The derived LST also showed a wide range and a clear seasonal difference. The results from AVHRR were also in agreement with MODIS, with the latter usually displaying a higher level of accuracy.

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).