Dianwei Liu

Retrieval of total suspended matter (TSM) and chlorophyll-a (Chl-a) concentration from remote-sensing data for drinking water resources

The concentrations of chlorophyll-a (Chl-a) and total suspended matter (TSM) are major water quality parameters that can be retrieved using remotely sensed data. Water sampling works were conducted on 15 July 2007 and 13 September 2008 concurrent with the Indian Remote-Sensing Satellite (IRS-P6) overpass of the Shitoukoumen Reservoir. Both empirical regression and back-propagation artificial neural network (ANN) models were established to estimate Chl-a and TSM concentration with both in situ and satellite-received radiances signals. It was found that empirical models performed well on the TSM concentration estimation with better accuracy (R2 = 0.94, 0.91) than their performance on Chl-a concentration (R2 = 0.62, 0.75) with IRS-P6 imagery data, and the models accuracy marginally improved with in situ spectra data. Our results indicated that the ANN model performed better for both Chl-a (R2 = 0.91, 0.82) and TSM (R2 = 0.98, 0.94) concentration estimation through in situ collected spectra; the same trend followed for IRS-P6 imagery data (R2 = 0.75 and 0.90 for Chl-a; R2 = 0.97 and 0.95 for TSM). The relative root mean square errors (RMSEs) from the empirical model for TSM (Chl-a) were less than 15% (respectively 27.2%) with both in situ and IRS-P6 imagery data, while the RMSEs were less than 7.5% (respectively 18.4%) from the ANN model. Future work still needs to be undertaken to derive the dynamic characteristic of Shitoukoumen Reservoir water quality with remotely sensed IRS-P6 or Landsat-TM data. The algorithms developed in this study will also need to be tested and refined with more imagery data acquisitions combined with in situ spectra data.

Spatial variability of soil organic carbon under maize monoculture in the Song-Nen Plain, Northeast China.

Abstract Soil organic carbon (SOC) and its relationship with landscape attributes are important for evaluating current regional, continental, and global carbon stores. Data of SOC in surface soils (0–20 cm) of four main soils, Cambisol, Arenosol, Phaeozem, and Chernozem, were collected at 451 locations in Nongan County under maize monoculture in the Song-Nen Plain, Northeast China. The spatial characteristics of soil organic carbon were studied, using geographic information systems (GIS) and geostatistics. Effects of other soil physical and chemical properties, elevation, slope, and soil type on SOC were explored. SOC concentrations followed a normal distribution, with an arithmetic mean of 14.91 g kg −1 . The experimental variogram of SOC was fitted with a spherical model. There were significant correlations between soil organic carbon and bulk density ( r = −0.374**), pH ( r = 0.549**), total nitrogen ( r = 0.781**), extractable phosphorus ( r = −0.109*), exchangeable potassium ( r = 0.565**), and cation exchange capacity ( r = 0.313**). Generally, lower SOC concentrations were significantly associated with high elevation ( r = −0.429**). Soil organic carbon was significantly negatively correlated with slope gradient ( r = −0.195**). Samples of the Cambisol statistically had the highest SOC concentrations, and samples of the Arenosol had the lowest SOC value.

Using CropSyst to Simulate Spring Wheat Growth in Black Soil Zone of Northeast China

ABSTRACT Available water and fertilizer have been the main limiting factors for yields of spring wheat, which occupies a large area of the black soil zone in northeast China; thus, the need to set up appropriate models for scenario analysis of cropping system models has been increasing. The capability of CropSyst, a cropping system simulation model, to simulate spring wheat growth of a widely grown spring cultivar, ‘Longmai 19’, in the black soil zone in northeast China under different water and nitrogen regimes was evaluated. Field data collected from a rotation experiment of three growing seasons (1992–1994) were used to calibrate and validate the model. The model was run for 3 years by providing initial conditions at the beginning of the rotation without reinitializing the model in later years in the rotation sequence. Crop input parameters were set based on measured data or taken from CropSyst manual. A few cultivar-specific parameters were adjusted within a reasonable range of fluctuation. The results demonstrated the robustness of CropSyst for simulating evapotranspiration, aboveground biomass, and grain yield of ‘Longmai 19’ spring wheat with the root mean square errors being 7%, 13% and 13% of the observed means for evapotranspiration (ET), grain yield and aboveground biomass, respectively. Although CropSyst was able to simulate spring production reasonably well, further evaluation and improvement of the model with a more detailed field database was desirable for agricultural systems in northeast China.

Evapotranspiration estimation based on MODIS products and surface energy balance algorithms for land (SEBAL) model in Sanjiang Plain, Northeast China

In this study, the Surface Energy Balance Algorithms for Land (SEBAL) model and Moderate Resolution Imaging Spectroradiometer (MODIS) products from Terra satellite were combined with meteorological data to estimate evapotranspiration (ET) over the Sanjiang Plain, Northeast China. Land cover/land use was classified by using a recursive partitioning and regression tree with MODIS Normalized Difference Vegetation Index (NDVI) time series data, which were reconstructed based on the Savitzky-Golay filtering approach. The MODIS product Quality Assessment Science Data Sets (QA-SDS) was analyzed and all scenes with valid data covering more than 75% of the Sanjiang Plain were selected for the SEBAL modeling. This provided 12 overpasses during 184-day growing season from May 1st to October 31st, 2006. Daily ET estimated by the SEBAL model was misestimaed at the range of −11.29% to 27.57% compared with that measured by Eddy Covariance system (10.52% on average). The validation results show that seasonal ET from the SEBAL model is comparable to that from ground observation within 8.86% of deviation. Our results reveal that the time series daily ET of different land cover/use increases from vegetation on-going until June or July and then decreases as vegetation senesced. Seasonal ET is lower in dry farmland (average (Ave): 491 mm) and paddy field (Ave: 522 mm) and increases in wetlands to more than 586 mm. As expected, higher seasonal ET values are observed for the Xingkai Lake in the southeastern part of the Sanjiang Plain (Ave: 823 mm), broadleaf forest (Ave: 666 mm) and mixed wood (Ave: 622 mm) in the southern/western Sanjiang Plain. The ET estimation with SEBAL using MODIS products can provide decision support for operational water management issues.

SPOT5 multi-spectral (MS) and panchromatic (PAN) image fusion using an improved wavelet method based on local algorithm

Remote sensing image fusion is an effective way to extract a large volume of data from multi-source images. However, traditional image fusion methods cannot meet the requirements of applications because they can lose spatial information or distort spectral characteristics. In this paper, a new wavelet method based on a local algorithm is presented. The proposed method fuses multi-spectral (MS) and panchromatic (PAN) images to improve spatial information and preserve spectral characteristics. The main advantage of the new fusion method is the exploitation of the dependency between neighboring pixels. SPOT5 MS and PAN images were employed to execute the fusion methods. To compare with the new method, the principal component analysis (PCA), wavelet transformation, and PCA-based wavelet (PCA+W) image fusion methods were selected. Qualitative and quantitative analyses and classification accuracy assessment were conducted to evaluate the performance of the fusion methods. The results demonstrate that the new wavelet method based on a local algorithm is better than traditional image fusion methods. The new fusion method can achieve a wide range of balance between high spatial resolution retention and spectral characteristic preservation; thus, the new method is suitable for different applications.

Monitoring Loss and Recovery of Salt Marshes in the Liao River Delta, China

ABSTRACT Jia, M.; Wang, Z.; Liu, D.; Ren, C.; Tang, X., and Dong, Z., 2015. Monitoring loss and recovery of salt marsh in the Liao River Delta, China. Coastal salt marsh plays an important role in the aquatic food web and the export of nutrients to coastal waters. The salt marshes in the Liao River Delta of China, dominated by Suaeda heteroptera, experienced a dramatic loss in the 1990s and then recovered in the 2000s. This study investigates these changes of salt marsh using a time series of Landsat Thematic Mapper (TM) images acquired in 1988, 1995, 2000, 2004, 2007, and 2009. The classification tree method was used on these TM images to extract S. heteroptera, and an interactive self-organizing data analysis algorithm was used to determine other land cover types. The conversions between salt marsh and other land cover types were described with conversion matrices. The classification results show that, during 1998–2004, salt marsh decreased dramatically at an average rate of 662.68 ha/y. However, during the period 2004–2009, salt marsh recovered gradually at a rate of 115.51 ha/y. The conversion matrix indicates that, from 1988 to 2004, a large area of former salt marsh was directly replaced by man-made landscape types, such as reed field (5111 ha), aquaculture pond (2655 ha), reservoir (1720 ha), and paddy field (729 ha). In contrast, the result for the period from 2004 to 2009 shows that salt marshes were recovered by the conversion of some areas of former barren beaches and river back to salt marsh. Driving forces analysis suggests that salt marsh dynamics were mainly caused by human activities, with the secondary drivers being climatic warming and dry conditions.

Influence of vegetation phenology on modelling carbon fluxes in temperate deciduous forest by exclusive use of MODIS time-series data

Understanding the influence of vegetation phenology on modelling primary productivity, biomass, and natural carbon dynamics, as well as the underlying mechanisms, is crucial for assessing the vulnerability of terrestrial carbon pools under future changing climate conditions. Considering that the component fluxes of carbon sequestration, gross primary production (GPP) and ecosystem respiration (Re), are dominant alternately during the course of the year, here we propose a new model for estimating the carbon sequestration of temperate deciduous forest exclusively based on Moderate Resolution Imaging Spectroradiometer (MODIS) time-series data, including land-surface temperature (LST), Terra night-time LST (LST′), enhanced vegetation index (EVI), land-surface water index (LSWI), fraction of absorbed photosynthetically active radiation (FPAR), and leaf area index (LAI). This study aims to reveal the main environmental control variables that contribute to the net ecosystem exchange (NEE) variations and to develop an improved model that accurately predicts NEE according to the growing and dormant seasons. The seasonality information was extracted from time series of MODIS NDVI data based on nonlinear least squares fits of asymmetric Gaussian model functions in the computer program, TIMESAT. The results suggest that the improved model could provide substantially better NEE estimates and well reflect the seasonal dynamics of the temperate deciduous forest. In addition, because both ecosystem photosynthesis and respiration are powerful during the growing season, all variables are strongly correlated with NEE at the 0.01 p-level, whereas only some parameters (temperature and water) are significant during the non-growing period due to dominant respiration and limited photosynthesis.

Spectral Absorption Properties of Colored Dissolved Organic Matter (CDOM) and Total Suspended Matter (TSM) of Inland Waters

Spectral absorption properties of total suspended matter (TSM) and colored dissolved organic matter (CDOM) are important for the use of the bio-optical model to estimate water quality parameters. This study aims to investigate the variation in the absorption coefficients of TSM and CDOM of inland waters. A total of 92 water samples were collected from Shitoukoumen Reservoir and Songhua Lake in Northeast China, analyzed for TSM and Chl-a, and measured for the absorption coefficient of TSM, CDOM and total pigments using a laboratory spectrophotometer. The absorption coefficient of TSM has been decomposed for phytoplankton and inorganic sediments. The results show that for Shitoukoumen Reservoir, CDOM has strong absorptions with shallow absorption slopes (i.e., the coefficient S in a(λ)=a(λ0)exp[-S(λ- λ0)]) and large absorption at 355 nm; and for Songhua Lake, CDOM follows similar spectral absorption curves but less variation in the S value. The results also show TSM has the average absorption coefficient 5.7 m-1 at 440 nm and 0.93 m-1 at 675 nm, and their concentration is well correlated to TSM with R2 larger than 0.85 at 440 nm over both Songhu Lake and Shitoukoumen Reservoir. In summer, CDOM was mainly terrigenous and had a high proportion of humic acid derived from the decomposition of phytoplankton and there were no obvious difference of S value. The results indicate that inorganic sediments contributed much more absorption than phytoplankton pigments in Shitoukoumen Reservoir than that in Songhua Lake, and there is strong association of TSM concentration to absorption coefficient at 440 nm.

Landscape and land use effects on the spatial variation of soil chemical properties

The current study addressed the spatial variation of soil organic matter (SOM), total nitrogen (TN), extractable phosphorus (EP), and extractable potassium (EK) in agricultural soils of a representative region, northeast China. Soil cation exchange capacity (CEC) and the effects of landscape attributes and land use were also investigated. The techniques used included conventional statistics, geostatistics, and geographic information systems (GIS). Our study demonstrated that EP had the greatest coefficient of variation (CV), and CEC had the least CV. The experimental semivariograms of the five soil chemical properties included in this study were all fitted with exponential models. The five soil variables all showed moderate spatial dependence. The SOM, EK, and CEC decreased with increasing altitude. Significant negative relationships were found between the slope gradient and EP, EK, and CEC. Relatively steeper slopes might result in greater soil erosion, which leads to a decline in soil nutrients. Soil types had significant impacts on all soil chemical properties, which reflect the effect of the parent soil material. In general, the mean values of soil variables for vegetable land were statistically greater than those for upland and paddy fields. After being divided into two parts along the Yinma River, soil samples of the western part have statistically greater SOM, EP, EK, and CEC values than those collected from the eastern part.

Water Chlorophyll Concentration Estimation Using Landsat TM Data with Empirical Algorithms in Chagan Lake, China

IEEE, IEEE Geosci & Remote Sensing Soc, Canadian Remote Sensing Soc, NASA, NOAA, Off Naval Res, Natl Polar orbiting Operat Environm Satellite Syst, Japan Aerosp Explorat Agcy, Ball Aerosp & Technologies Corp, Cooperat Inst Res Atmosphere, Colorado State Univ, Univ Colorado, Int Union Radio SciIEEE, IEEE Geosci & Remote Sensing Soc, Canadian Remote Sensing Soc, NASA, NOAA, Off Naval Res, Natl Polar orbiting Operat Environm Satellite Syst, Japan Aerosp Explorat Agcy, Ball Aerosp & Technologies Corp, Cooperat Inst Res Atmosphere, Colorado State Univ, Univ Colorado, Int Union Radio Sci