Novel hyperspectral reflectance models for estimating black-soil organic matter in Northeast China

This paper presents a novel method for estimating black-soil organic matter (SOM) in the black-soil zone of northeast China from hyperspectral reflectance models. Traditional black-soil property measurements are relatively slow, but the pressures of agricultural production and environmental protection require a quick method to collect black-soil organic matter content. SOM estimation using soil hyperspectral reflectance models can meet this requirement, based on the spectral characteristics of black-soil in Northeast China. On the basis of the spectral reflectance and its derivatives, hyperspectral models can be built using correlation analysis and multivariable statistical methods. The concepts of curvature and ratio indices are also applied to compare and test the stability and accuracy of data modeling. The results show that the response of black-soil spectral reflectance from 400-1,100 nm to organic matter content is more marked than that from 1,100-2,500 nm. Specifically, the main response range of black-soil organic matter is between 620-810 nm, with a maximal spectral response at 710 nm. By comparing different models, we found that the normalized first derivate model is optimal for estimating SOM content, with a determination coefficient of 0.93 and root mean squared errors (RMSE) of 0.18%.     

Predicting field capacity,wilting point,and the other physical properties of soils using hyperspectral reflectance spectroscopy: two different statistical approaches

In this study, we examined the ability of reflectance spectroscopy to predict some of the most important soil parameters for irrigation such as field capacity (FC), wilting point (WP), clay, sand, and silt content. FC and WP were determined for 305 soil samples. In addition to these soil analyses, clay, silt, and sand contents of 145 soil samples were detected. Raw spectral reflectance (raw) of these soil samples, between 350 and 2,500-nm wavelengths, was measured. In addition, first order derivatives of the reflectance (first) were calculated. Two different statistical approaches were used in detecting soil properties from hyperspectral data. Models were evaluated using the correlation of coefficient (r), coefficient of determination (R ²), root mean square error (RMSE), and residual prediction deviation (RPD). In the first method, two appropriate wavelengths were selected for raw reflectance and first derivative separately for each soil property. Selection of wavelengths was carried out based on the highest positive and negative correlations between soil property and raw reflectance or first order derivatives. By means of detected wavelengths, new combinations for each soil property were calculated using rationing, differencing, normalized differencing, and multiple regression techniques. Of these techniques, multiple regression provided the best correlation (P?<?0.01) for selected wavelengths and all soil properties. To estimate FC, WP, clay, sand, and silt, multiple regression equations based on first(2,310)-first(2,360), first(2,310)-first(2,360), first(2,240)-first(1,320), first(2,240)-first(1,330), and raw(2,260)-raw(360) were used. Partial least square regression (PLSR) was performed as the second method. Raw reflectance was a better predictor of WP and FC, whereas first order derivative was a better predictor of clay, sand, and silt content. According to RPD values, statistically excellent predictions were obtained for FC (2.18), and estimations for WP (2.0), clay (1.8), and silt (1.63) were acceptable. However, sand values were poorly predicted (RDP?=?0.63). In conclusion, both of the methods examined here offer quick and inexpensive means of predicting soil properties using spectral reflectance data.     

Conversion of grazing land into Grevillea robusta plantation and exclosure: impacts on soil nutrients and soil organic carbon

Different studies have shown that the effect of land use conversion on soil nutrients and soil organic carbon (SOC) is variable, which indicates that more investigations that focus on different specific geographical locations and land use types are required. The objectives of this study were (1) to evaluate the effect of grazing land (GL) conversion into Grevillea robusta plantation and exclosure (EX) on soil nutrients and soil organic carbon (SOC) and (2) to examine the impact of soil organic matter (SOM) on soil nutrients. To achieve these objectives, soil samples were taken from a soil depth of 20 cm (n?=?4) in each of the studied land areas. Each soil sample was analysed in a soil laboratory following a standard procedure. Analysis of variance (ANOVA) and Pearson’s correlation coefficient were used for the data analysis. The result indicated that conversion of GL into EX improved the soil electrical conductivity (EC), exchangeable K, cation exchange capacity (CEC), total N and available P (p?<?0.05), while the exchangeable Mg, SOC, available K and SOM were decreased (p?<?0.05). Conversion of GL into G. robusta improved the soil EC, exchangeable (K, Ca, Mg), CEC, SOC, total N, available K and SOM (p?<?0.05). There was a significant relationship between SOM and available P, total N, SOC and EC. There were no significant relationships between SOM and pH, available K and CEC. Finally, the results indicate that both land uses, established in acidic Nitosols, have variable impacts on soil chemical properties and that G. robusta plantation improved most of the soil nutrients and SOC much better than the EX land use.     

Comparison of different semi-empirical algorithms to estimate chlorophyll-a concentration in inland lake water

Based on in situ water sampling and field spectral measurement from June to September 2004 in Lake Chagan, a comparison of several existing semi-empirical algorithms to determine chlorophyll-a (Chl-a) content was made by applying them to the field spectra and in situ chlorophyll measurements. Results indicated that the first derivative of reflectance was well correlated with Chl-a. The highest correlation between the first derivative and Chl-a was at 680 nm. The two-band model, NIR/red ratio of R_710/670, was also an effective predictor of Chl-a concentration. Since the two-band ratios model is a special case of the three-band model developed recently, three-band model in Lake Chagan showed a higher resolution. The new algorithm named reverse continuum removal relies on the reflectance peak at 700 nm whose shape and position depend strongly upon chlorophyll concentration: The depth and area of the peak above a baseline showed a linear relationship to Chl-a concentration. All of the algorithms mentioned proved to be of value and can be used to predict Chl-a concentration. Best results were obtained by using the algorithms of the first derivative, which yielded R ² around 0.74 and RMSE around 6.39 μg/l. The two-band and three-band algorithms were further applied to MERIS when filed spectral were resampled with regard to their center wavelengths. Both algorithms showed an adequate precision, and the differences on the outcome were small with R ² = 0.70 and 0.71.     

Dynamics of Organic Carbon and Microbial Biomass in Alluvial Soil With Tillage and Amendments in Rice-wheat Systems

Rice-wheat cropping systems of the Indo-Gangetic plains (IGP) occupying 12 million ha of productive land are important for the food security of South Asia. There are, however, concerns that yield and factor productivity trends in these systems are declining/stagnating in recent years. Decrease in soil organic carbon is often suggested as a reason for such trends. A field experiment was conducted to study the soil organic carbon (SOC) and soil microbial biomass carbon (MBC) dynamics in the rice-wheat systems. Use of organic amendments and puddling of soil before rice transplanting increased SOC and MBC contents. Microbial biomass carbon showed a seasonal pattern. It was low initially, reached its peak during the flowering stages in both rice and wheat and declined thereafter. Microbial biomass carbon was linearly related to SOC in both rice and wheat indicating that SOC could be used as a proxy for MBC.     

Monitoring changes in soil organic carbon pools, nitrogen, phosphorus, and sulfur under different agricultural management practices in the tropics

Soil organic matter not only affects sustainability of agricultural ecosystems, but also extremely important in maintaining overall quality of environment as soil contains a significant part of global carbon stock. Hence, we attempted to assess the influence of different tillage and nutrient management practices on various stabilized and active soil organic carbon pools, and their contribution to the extractable nitrogen phosphorus and sulfur. Our study confined to the assessment of impact of agricultural management practices on the soil organic carbon pools and extractable nutrients under three important cropping systems, viz. soybean–wheat, maize–wheat, and rice–wheat. Results indicated that there was marginal improvement in Walkley and Black content in soil under integrated and organic nutrient management treatments in soybean–wheat, maize–wheat, and rice–wheat after completion of four cropping cycles. Improvement in stabilized pools of soil organic carbon (SOC) was not proportional to the applied amount of organic manures. While, labile pools of SOC were increased with the increase in amount of added manures. Apparently, green manure (Sesbania) was more effective in enhancing the lability of SOC as compared to farmyard manure and crop residues. The KMnO₄-oxidizable SOC proved to be more sensitive and consistent as an index of labile pool of SOC compared to microbial biomass carbon. Under different cropping sequences, labile fractions of soil organic carbon exerted consistent positive effect on the extractable nitrogen, phosphorus, and sulfur in soil.     

Assessment of organic pollution of an industrial river by synchronous fluorescence and UV–vis spectroscopy: the Fensch River (NE France)

To rapidly monitor the surface water quality in terms of organic pollution of an industrial river undergoing restoration, optical methods (UV–visible spectrometry and fluorescence) were applied in parallel to classical physical–chemical analyses. UV–visible spectra were analyzed using the maximum of the second derivative at 225 nm (related to nitrates), specific absorbance at 254 nm (SUVA₂₅₄), and the spectral slope between 275 and 295 nm (S _275–295) (related to the aromaticity and molecular weight of dissolved organic carbon). The synchronous fluorescence spectra (wavelength difference = 50 nm) exhibited a high variability in the composition of dissolved organic material between the upstream and downstream sections and also versus time. The principal components analysis of the entire set of synchronous fluorescence spectra helped to define three river sections with different pollution characteristics. Spectral decomposition was applied to the two most upstream sections: five fluorophores, classical in rivers impacted by domestic sewage and related to protein-like (λ _ex = 280 nm) and humic-like fluorescence (M-type with λ _ex ≈ 305–310 nm and C-type with λ _ex ≥ 335 nm), were identified. The irregular shape of the synchronous fluorescence spectra in the most downstream section is likely due to organic pollutants of industrial origin; however, their variability and the complexity of the spectra did not allow the further elucidation of their nature.     

Carbon sequestration in reclaimed manganese mine land at Gumgaon, India

Carbon emission is supposed to be the strongest factor for global warming. Removing atmospheric carbon and storing it in the terrestrial biosphere is one of the cost-effective options, to compensate greenhouse gas emission. Millions of acres of abandoned mine land throughout the world, if restored and converted into vegetative land, would solve two major problems of global warming and generation of degraded wasteland. In this study, a manganese spoil dump at Gumgaon, Nagpur in India was reclaimed, using an integrated biotechnological approach (IBA). The physicochemical and microbiological status of the mine land improved after reclamation. Soil organic carbon (SOC) pool increased from 0.104% to 0.69% after 20 years of reclamation in 0–15 cm spoil depth. Soil organic carbon level of reclaimed site was also compared with a native forestland and agricultural land. Forest soil showed highest SOC level of 1.11% followed by reclaimed land and agriculture land of 0.70% and 0.40%, respectively. Soil profile studies of all three sites showed that SOC pool decreased from 0–15, 15–30, and 30–45 cm depths. Although reclaimed land showed less carbon than forestland, it showed better SOC accumulation rate. Reclamation of mine lands by using IBA is an effective method for mitigating CO₂ emissions.     

Research on the relationship between the fractional coverage of the submerged plant Vallisneria spiralis and observed spectral parameters

The present paper discusses the relationship between the coverage fraction of submerged plants and the observed spectral characteristics. The purpose of this paper is to validate a remote sensing technology to monitor the change in the plant composition of a water body. In the current study, the reflectance spectra of the submerged plant Vallisneria spiralis at different fraction coverages of the wetland in Hangzhou Bay were measured. The relationships between the fraction coverage of V. spiralis and simulated Quickbird normalized difference vegetation index (NDVI), red edge, and other spectral characteristic parameters were established. The results showed that the spectral reflectance characteristics of submerged plant V. spiralis were mainly in the visible light (490–650 nm) and near infrared (700–900 nm). The rate of change of the blue band curve and simulated Quickbird NDVI showed a higher correlation with the V. spiralis coverage, so estimation models of the fraction coverage were constructed using these parameters. The estimated fraction coverage of V. spiralis with different models were validated with ground data, and the accuracy of estimation models was assessed. The most suitable estimated fraction coverage of V. spiralis was obtained using the rate of change of the blue band curve and simulated Quickbird NDVI. The present work demonstrated a method to monitor the distribution and dynamical variation of submerged plants at the large scale.     

Spatial 3D distribution of soil organic carbon under different land use types

Soil organic carbon (SOC) has been assessed in three dimension (3D) in several studies, but little is known about the combined effects of land use and soil depth on SOC stocks in semi-arid areas. This paper investigates the 3D distribution of SOC to a depth of 1 m in a 4600-ha area in southeastern Iran with different land uses under the irrigated farming (IF), dry farming (DF), orchards (Or), range plants on the Gachsaran formation (RaG), and range plants on a quaternary formation (RaQ). Predictions were made using the artificial neural networks (ANNs), regression trees (RTs), and spline functions with auxiliary covariates derived from a digital elevation model (DEM), the Landsat 8 imagery, and land use types. Correlation analysis showed that the main predictors for SOC in the topsoil were covariates derived from the imagery; however, for the lower depths, covariates derived from both the DEM and imagery were important. ANNs showed more efficiency than did RTs in predicting SOC. The results showed that 3D distribution of SOC was significantly affected by land use types. SOC stocks of soils under Or and IF were significantly higher than those under DF, RaG, and RaQ. The SOC below 30 cm accounted for about 59% of the total soil stock. Results showed that depth functions combined with digital soil mapping techniques provide a promising approach to evaluate 3D SOC distribution under different land uses in semi-arid regions and could be used to assess changes in time to determine appropriate management strategies.     

Spatial Variability of Soil Organic Carbon in Relation to Environmental Factors of a Typical Small Watershed in the Black Soil Region, Northeast China

A total of 292 soil samples were taken from surface soil (0–20 cm) of a typical small watershed–Tongshuang in the black soil region of Heilongjiang province, northeast China in June 2005 for examining the concentration of soil organic carbon (SOC). Spatial variability of SOC in relation to topography and land use was evaluated using classical statistics, geostatistics and geographic information system (GIS) analyses. The objective of this study was to provide a scientific basis for land management targeting at improving soil quality in this region. Classical statistical analysis results indicated that the variability of SOC was moderate (C _V = 0.30). Slope position and land use types were discriminating factors for its spatial variability. Geostatistics analyses showed that SOC had a strong spatial autocorrelation, which was mainly induced by structural factors. Mean concentration of SOC in surface soil was 2.27% in this watershed, which was a very low level in the northern black soil region of northeast China. In this small watershed, present soil and water conservation measures played an important role in controlling soil loss. But SOC's restoration was unsatisfactory. Nearly three-quarters of the area had worrisome productivity. How to improve SOC concentration targeting at soil fertility is a pressing need in the future.     

Estimation of chlorophyll a content in inland turbidity waters using WorldView-2 imagery: a case study of the Guanting Reservoir,Beijing, China

Complex optical properties, such as non-pigment suspension and colored dissolved organic matter (CDOM), make it difficult to achieve accurate estimations of remotely sensed chlorophyll a (Chla) content of inland turbidity. Recent attempts have been made to estimate Chla based on red and near-infrared regions where non-pigment suspension and CDOM have little effect on water reflectance. The objective of this study is to validate the applicability of WV-2 imagery with existing effective estimation methods from MERIS when estimating Chla content in inland turbidity waters. The correlation analysis of measured Chla content and WV-2 imagery bands shows that the Chla sensitive bands of WV-2 are red edge, NIR 1, and NIR 2. The coastal band is designed for seawater Chla detection. However, the high correlation with turbidity data and low correlation with Chla made coastal band unsuitable for estimating Chla in inland waters. The high-resolution water body images were extracted by combining the spectral products (NDWI) with the spatial morphological products (sobel edge detection). The estimation results show that the accuracy of the single band and NDCI is not as good as the two-band method, three-band method, stepwise regression algorithm (SRA) and support vector machines (SVM). The SVM estimation accuracy was the highest with an R², RMSE, and URMSE of 0.8387, 0.4714, and 19.11%, respectively. This study demonstrates that the two-band and three-band methods are effective for estimating Chla in inland water for WV-2 imagery. As a high-precision estimation method, SVM has great potential for inland turbidity water Chla estimation.     

Land use impact on soil quality in eastern Himalayan region of India

Quantitative assessment of soil quality is required to determine the sustainability of land uses in terms of environmental quality and plant productivity. Our objective was to identify the most appropriate soil quality indicators and to evaluate the impact of six most prevalent land use types (natural forestland, cultivated lowland, cultivated upland terrace, shifting cultivation, plantation land, and grassland) on soil quality in eastern Himalayan region of India. We collected 120 soil samples (20 cm depth) and analyzed them for 29 physical, chemical, and biological soil attributes. For selection of soil quality indicators, principal component analysis (PCA) was performed on the measured attributes, which provided four principal components (PC) with eigenvalues >1 and explaining at least 5 % of the variance in dataset. The four PCs together explained 92.6 % of the total variance. Based on rotated factor loadings of soil attributes, selected indicators were: soil organic carbon (SOC) from PC-1, exchangeable Al from PC-2, silt content from PC-3, and available P and Mn from PC-4. Indicators were transformed into scores (linear scoring method) and soil quality index (SQI) was determined, on a scale of 0–1, using the weighting factors obtained from PCA. SQI rating was the highest for the least-disturbed sites, i.e., natural forestland (0.93) and grassland (0.87), and the lowest for the most intensively cultivated site, i.e., cultivated upland terrace (0.44). Ratings for the other land uses were shifting cultivation (0.60)?>?cultivated low land (0.57)?>?plantation land (0.54). Overall contribution (in percent) of the indicators in determination of SQI was in the order: SOC (58 %)?>?exch. Al (17.1 %)?>?available P (8.9 %)?>?available Mn (8.2 %)?>?silt content (7.8 %). Results of this study suggest SOC and exch. Al as the two most powerful indicators of soil quality in study area. Thus, organic C and soil acidity management holds the key to improve soil quality under many exploitatively cultivated land use systems in eastern Himalayan region of India.     

基于实测光谱的海河悬浮物浓度反演研究

悬浮物浓度是评价水质优劣的重要指标之一。以天津滨海新区海河为研究区域,进行光谱数据测量和水体样本采集,并在实验室进行水质参数提取,得到归一化光谱反射率和光谱一阶微分数据。然后对光谱测量数据和悬浮物浓度实测值进行相关性分析,发现896 nm归一化光谱反射率和光谱反射率比值(R_(896)/R_(546))与悬浮物浓度相关性较好。最后,分别建立单波段、波段比值和一阶微分函数拟合模型,进行对比分析。结果表明,基于R_(896)/R_(546)的二次多项式模型拟合效果最好,方差齐性检验(F)值也是最高的,可用于该地区水体的悬浮物浓度反演和预测。     

Spatial and temporal variation of soil organic carbon in the North China Plain

The organic carbon, permeability test, grain size, chemical composition, and mineral composition were analyzed for 147 samples collected from the Luan River catchment, Hebei province, China, to quantitatively characterize the effects of land use, climate change, sedimentary environment, mineral composition, and chemical composition on the spatial and temporal variation of soil organic carbon (SOC). The results indicate that there was higher SOC content and stronger variation in the south plain than in the northern low mountain. The effects of land use, climate change, and sedimentary environment on SOC distribution were greater than the effects of mineral composition and chemical composition. The cropping systems in the Luan River catchment resulted in significant difference in SOC concentration between the south plain and north mountain. The precipitation mainly transmitted its effects through the sedimentary environment to SOC, which caused the stronger temporal variation in SOC from June to October in the south plain. The north mountain did not have significant temporal variation because of the lower hydraulic conductivity of the sedimentary sequence. The spatial variation of SOC was correlated with land use, and their temporal variation was attributed to climate change and sedimentary environment. Apart from land use, the decision maker can also affect the organic carbon mineral and sequence through the sedimentary environment.     

Spatial distribution characteristics of soil organic carbon in subtropical forests of mountain Lushan,China

The study on the spatial distribution of forest soil organic carbon (SOC) is of great significance for accurate assessment of carbon storage in forest ecosystems. In the present study, by taking eight kinds of forest soils of Mountain Lushan in the subtropical area as the research object, we studied the spatial distribution characteristics of SOC in this mountainous area. The results showed that the SOC content and SOC density (SOCD) of main forest types in the Mountain Lushan were lower than the national and the world average. The soil layer of Lushan forest was thinner, and the SOC and active SOC (ASOC) contents of different forest types and SOCDs are the highest in the surface soil. SOCD of the topsoil accounts for 32.64–54.03% of the total SOCD in the whole soil profile. Surface litter is an important source of SOC, and the different vegetation types are the important reason for the different spatial distribution of SOC in this area. Soil SOC contents in the high-altitude forest (bamboo forest, deciduous broadleaf forest, Pinus taiwanensis forest, evergreen-deciduous forest, and coniferous-broadleaved mixed forest) were higher than those in the low-altitude forest (evergreen broadleaf forest, shrub, and Pinus massoniana forest). However, the difference in SOC content exhibited at the altitude gradient is significantly lower than that in SOC in the soil profile. This indicates that both soil depth and elevation are the important factors that affected SOC distribution. However, the influence of soil depth on spatial distribution of SOC may be more complex than that of altitude. Vegetation types and soil properties are the main reasons for the large differences of reduction rate in the contents of SOC and ASOC.     

Predicting climate change effects on surface soil organic carbon of Louisiana,USA

This study aimed to assess the degree of potential temperature and precipitation change as predicted by the HadCM3 (Hadley Centre Coupled Model, version 3) climate model for Louisiana, and to investigate the effects of potential climate change on surface soil organic carbon (SOC) across Louisiana using the Rothamsted Carbon Model (RothC) and GIS techniques at the watershed scale. Climate data sets at a grid cell of 0.5°?×?0.5° for the entire state of Louisiana were collected from the HadCM3 model output for three climate change scenarios: B2, A2, and A1F1, that represent low, higher, and even higher greenhouse gas emissions, respectively. Geo-referenced datasets including USDA-NRCS Soil Geographic Database (STATSGO), USGS Land Cover Dataset (NLCD), and the Louisiana watershed boundary data were gathered for SOC calculation at the watershed scale. A soil carbon turnover model, RothC, was used to simulate monthly changes in SOC from 2001 to 2100 under the projected temperature and precipitation changes. The simulated SOC changes in 253 watersheds from three time periods, 2001–2010, 2041–2050, and 2091–2100, were tested for the influence of the land covers and emissions scenarios using SAS PROC GLIMMIX and PDMIX800 macro to separate Tukey-Kramer (p?p?p?p?相似文献   

Canopy assessment of biochemical features by ground-based hyperspectral data for an invasive species, giant reed (Arundo donax)

This study explored the potential use of hyperspectral data in the non-destructive assessment of chlorophyll, carbon, and nitrogen content of giant reed at the canopy level. We found that pseudoabsorption and derivatives of original hyperspectral data were able to describe the relationship between spectral data and measured biochemical characteristics. Based on correlogram analyses of ground-based hyperspectral data, we found that derivatives of pseudoabsorption were the best predictors of chlorophyll, carbon, and nitrogen content of giant reed canopies. Within the visible region, spectral data significantly correlated with chlorophyll content at both 461 nm and 693 nm wavelengths. Within the near-infrared region, carbon levels correlated with hyperspectral data at five causal wavelengths: 1038 nm, 1945 nm, 1132 nm, 1525 nm, and 1704 nm. The best spectral wavelength for estimating nitrogen content was 1542 nm. Such relationships between nutrient content and spectral data were best represented by exponential functions in most situations.     

Effects of land conversion from native shrub to pistachio orchard on soil erodibility in an arid region

Land-use change through degrading natural vegetation for agricultural production adversely affects many of soil properties particularly organic carbon content of soils. The native shrub land and grassland of Gaziantep-Adiyaman plateau that is an important pistachio growing eco-region have been cleared to convert into pistachio orchard for the last 50 to 60 years. In this study, the effects of conversion of natural vegetation into agricultural uses on soil erodibility have been investigated. Soil samples were collected from surface of agricultural fields and adjacent natural vegetation areas, and samples were analyzed for some soil erodibility indices such as dispersion ratio (DR), erosion ratio (ER), structural stability index (SSI), Henin’s instability index (I _s ), and aggregate size distribution after wet sieving (AggSD). According to the statistical evaluation, these two areas were found as different from each other in terms of erosion indices except for I _s index (P < 0.001 for DR and ER or P < 0.01 for SSI). In addition, native shrub land and converted land to agriculture were found different in terms of AggSD in all aggregate size groups. As a contrary to expectations, correlation tests showed that there were no any interaction between soil organic carbon and measured erodibility indices in two areas. In addition, significant relationships were determined between measured variables and soil textural fractions as statistical. These obtaining findings were attributed to changing of textural component distribution and initial aggregate size distribution results from land-use change in the study area. Study results were explained about hierarchical aggregate formation mechanism.     

Total suspended matter observation in the Pearl River estuary from in situ and MERIS data

Based on the cruise data collected in the Pearl River estuary (PRE) in May 2008, an empirical two-band model by using the ratio of R _rs at 629 and 671 nm was established to retrieve total suspended matter (TSM) concentration with the determination coefficient (R²) of 0.854, mean relative error (MRE) of 7.483%, and root-mean-square error (RMSE) of 1.295 mg L^???1. To match with medium resolution imaging spectrometer (MERIS) bands, in situ remote sensing reflectance was re-sampled to the bandwidth of 10 nm. The relationship between TSM and re-sampled R _rs at 620 nm (MERIS band 6) and 665 nm (MERIS band 7) are obtained (R² = 0.748, RMSE = 1.697 mg L^???1, MRE = 8.785%, n = 13). Additionally, to map the spatial distribution of TSM in the PRE, MERIS level_1B data were calibrated using a multiple linear regression model based on in situ R _rs. Another dataset collected in the PRE in January 2004 was used to validate the two-band model and also applied to map TSM distribution from MERIS image. The comparison between measured TSM values and modeled ones showed satisfactory results (R² = 0.753, MRE = 22.199%, and RMSE = 2.603 mg L^???1).