Lihong Zeng

Evapotranspiration estimation using moderate resolution imaging spectroradiometer products through a surface energy balance algorithm for land model in Songnen Plain, China

The Songnen Plain is an important commodity grain product base in China for which a spatiotemporal pattern of actual evapotranspiration (ETa) would provide critical important information to evaluate crop growth status and water use efficiency. ETa over the Songnen Plain in the 2008 growing season (from May to September) was mapped using the moderate resolution imaging spectroradiometer time-series products based on the surface energy balance algorithm for land model and the Penman-Monteith equation. The estimated ETa was validated using eddy covariance surface data. The calculated and observed ETa values were highly consistent with a total difference of 18.26% in the whole growing season. Therefore, the ETa retrieval method based on remote sensing technology could satisfy the requirements for regional ETa estimation over the Songnen Plain. The total ETa over the Songnen Plain in the 2008 growing season ranged from 182.7 to 1002.4 mm, and the average value for the whole study area was 591.1 ± 122.2 mm (standard deviation). ETa exhibited obvious spatial variation, gradually increasing from low values in the southwest to higher values in the east and northeast. Monthly ETa varied with meteorological conditions, land covers, root-zone soil moisture, and vegetation phenology. Higher monthly ETa values appeared in June, July, and August with a maximum value of 139.5 mm observed in July. The average monthly ETa for water-body, woodland, and wetland was much higher than cropland and grassland during the growing season. Grassland obtained the lowest monthly ETa due to the scarcity of rainfall and lower groundwater level.

Land use/land cover (LULC) classification with MODIS time series data and validation in the Amur River Basin

There is a need for improved and up-to-date land use/land cover (LULC) data sets over an intensively changing area in the Amur River Basin (ARB) in support of science and policy applications focused on understanding of the role and response of the LULC to environmental change issues. The main goal of this study was to map LULC in the ARB using MODIS 250-m Normalized Difference Vegetation Index (NDVI), Land Surface Vegetation Index (LSWI), and reflectance time series data for 2001 and 2007. Another goal was to test the consistency of the classification results using relatively coarse resolution MODIS imagery data in order to develop a methodology for rapid production of an up-to-date LULC data set. The results on MODIS land cover were evaluated using existing land use/cover data as derived from Landsat TM data. It was found that the MODIS 250-m NDVI data sets featured sufficient spatial, spectral and temporal resolution to detect unique multi-temporal signatures for the region’s major land cover types. It turned out that MODIS 250 NDVI time series data have high potential for large-basin land use/land cover monitoring and information updating for purposes of environmental basin research and management.

Land use/cover characterizaitoin with MODIS time series data with hybrid classification mothed over Australia for 2001 and 2003

Improved and up-to-date land use/land cover (LULC) data sets are needed over the whole country of Australia to support science and policy applications focused on understanding the role and response of the LULC to environmental change. The main goal of this study was to map LULC in Australia using MODIS 250 m Normalized Difference Vegetation Index (NDVI), Land Surface Vegetation Index (LSWI) and reflectance time series data of 2000 and 2003. NDVI time-series were filtered by the Savitzky-Golay algorithm in the present study to smooth out noise. A combination of unsupervised ISODATA and a hierarchical decision tree classification were performed on 2 years 12-month time-series MODIS data. Also, Australian Vegetation Map and other land use/land cover data set were used as labeling reference during the classification process. The MODIS land cover products were evaluated using existing land use/cover data derived from Landsat TM as reference data (AUS-2000), also LULC information derived from 11 scenes of Landsat-5 TM data were used as validation data source. The overall classification accuracy was 76.4%. It turned out that our result is acceptable because the relative high resolution of MODIS data and more prior knowledge was applied.

Application of wavelet transform on hyperspectral reflectance for soybean lai estimation in the songnen plain, China

In this study, we present spectral measurements of soybean LAI and their estimation from reflectance spectra data in Songnen Plain. Soybean canopy reflectance and its derivative were subsequently used in a linear regression analysis against LAI on one by one spectral reflectance. It was found that determination coefficient for LAI was high in blue, red and near infrared spectral region, and it was low in green spectral region, however LAI obtained its high determination coefficient in blue, green and red edge spectral region, especially in red edge region. Regression models were established based upon spectral vegetation indices and wavelet energy coefficient. It was found that wavelet transforms is an effective method for hyperspectral reflectance variables extraction to retrieve LAI, and the best multivariable regressions R2 up to 0.90 for LAI. Further studies are still needed to refine the methods for determining and estimating corn bio-physical/chemical parameters or other physiological parameters of different vegetation as well in the future.

Spatial mapping of actual evapotranspiration and water deficit with MODIS products in the Songnen Plain, northeast China

Analysis of spatial patterns of evapotranspiration (ET) and water deficit (WD) is significant in the evaluation of crop growth status and water use efficiency for the Songnen Plain, an important commodity grain product base of China. Spatial patterns of ET and WD in the Songnen Plain of 2008 growing season (from May to September) were mapped by using MODIS products and meteorological data. The results indicated that ET and WD exhibited obvious spatial variation and gradually increased from southwest to east and northeast. Total ET over the Songnen Plain during the 2008 growing season ranged from 182.7mm to 1002.4mm with the mean value of 591.1mm, and WD ranged from −163.0mm to 645.9mm with the mean value of 195.9mm. Average ET and WD for different land covers varied significantly, water-body and wetlands obtained the highest ET and WD values, while grassland got the lowest ET and WD values. Through this study, it would provide some supports for the assessment of crop growth in arid environments of Songnen Plain.

Land use/land cover (LULC) characterizaitoin with MODIS time series data in the Amu River Basin

Improved and up-to-date land use/land cover (LULC) data sets are needed over intensively land use/cover change area in the Amur River Basin (ARB) to support science and policy applications focused on understanding of the role and response of the LULC to environmental change issues. The main goal of this study was to map LULC in the Amur River Basin using MODIS 250 m Normalized Difference Vegetation Index (NDVI) and Land Surface Water Index (LSWI) time series data in 2001 and 2007. A combination of unsupervised ISODATA and hierarchical decision tree classification were performed on 12-month time-series of MODIS NDVI data over the study region. The MODIS land cover result of Northeast China was evaluated using existing land use/cover data, and the rest part was evaluated by LULC information derived from LANDSAT-TM. MODIS 250m NDVI, LSWI and reflectance datasets were found to have sufficient spatial, spectral, and temporal resolutions to detect unique multitemporal signatures of the major land cover types over the region. The overall classification accuracy was 0.81 and the kappa coefficient is 0.64. In conclusion, this method has been used successively for LULC change monitoring in the year 2001 and 2007. The result indicate that MODIS 250 NDVI time series data can derive relatively accurate LULC information for hydrological and climate modeling.

Analysis on the spectral reflectance response to snow contaminants in northeast China

By simulating atmospheric deposition experiment, this paper analyzed the relationship between the measured spectral reflectance and the concentrations of contaminants in the snow. It is found that the visible spectrum is sensitive to snow contaminants. From 350nm to 850nm, with the increase concentrations of contaminants in snow, snow reflectivity dramatically decreases. We get the conclusion that the most sensitive bands to snow contaminants are 384nm, 450nm and 1495nm.Using the non-linear regression method to analyze the relationship between spectral reflectance and the contaminants. The results showed the reflectivity of snow at visible bands logarithmically decreases with the snow contaminants increasing; the R2 can reach 0.9.To the contrary, the spectral reflectance at nearinfrared increases with the snow contaminants increasing. Therefore, this method can be combined satellite image to forecast the contaminants in the snow at large-scale.

Wavelet Transform (WT) and neural network model applied to canopy hyperspectral data for corn Chl-a estimation in Songnen Plain, China

In this study, we present spectral measurements of corn chlorophyll content in Changchun (eight times in 2003) and Hailun (five time in 2004), both of which lie in the Songnen Plain, China. Corn canopy reflectance and its derivative reflectance were subsequently used in a linear regression analysis against Chl-a concentration on one by one spectral reflectance. It was found that determination coefficient for Chl-a concentration was high in blue, red and near infrared spectral region, and it was low in green and red edge spectral region, however Chl-a concentration obtained its high determination coefficient in blue, green and red edge spectral region, especially in red edge region with derivative reflectance. Regression models were established based upon 6 spectral vegetation indices and wavelet coefficient, reflectance principal components as well. It was found that wavelet transforms is an effective method of hyperspectral reflectance feature extraction for corn Chl-a estimation, and the best multivariable regressions obtain determination coefficient (R2) up to 0.87 for Chl-a concentration. Finally, neural network algorithms with both specific band reflectance and wavelet coefficient as input variables were applied to estimate corn chlorophyll concentration. The results indicate that estimation accuracy improved with nodes number increasing in the hidden layer, and neural network performs better with wavelet coefficient than that with specific band reflectance as input variables, determination coefficient was up to 0.96 for Chl-a concentration. Further studies are still needed to refine the methods for determining and estimating corn bio-physical/chemical parameters or other vegetation as well in the future.

Comprision study on mapping of ET in the CIA of Murrumbidgee catchment with remote sensed satellite data: Examples from National Airborne Field Experimentation

This study focused on actual Evapotranspiration (ET) characterization in Coleambally Irrigation Area (CIA), Murrubidgee Irrigation Area (MIA) and its ambient region using the SEBAL model with remotely sensed TERRA/MODIS, NOAA/AVHRR and Landsat-5 TM data in the Area Of Interest (AOI) for the National Airborne Field Experimentation (NAFE06) campaign in 2006. Results showed that actual ET estimated from NOAA AVHRR 18 was always overestimating as comparison to Eddy system (on average 38%) during the image acquisition dates. However, for the same image acquisition dates, TERRA/MODIS ET ranges from 3.7% lower to 21.7% higher than the Eddy system. Landsat 5 TM modeled ET results were comparable to the Eddy Covariance system having a minor error with an average of 4.09%. It was proven possible to simultaneously use SEBAL for different sensors with the combination of high spatial and temporal resolution to estimate ET spatial distribution characteristics though the accuracy of NOAA-AVHRR derived result is not ideal. Considering the lack of high spatial resolution thermal satellite and need of time-series ET dynamics, the MODIS data could be used to provide seasonal actual ET for regional studies. The combination of MODIS and Landsat can be a better choice for future ET study at catchments scale.

Comparison study of seasonal snow cover area from space-borne satellite data in the Heilongjiang Basin

The Heilongjiang Basin (HLB) located between N43° to N57° and E108° to E141° is a seasonal snow covered region. The monitoring of snow covered areas (SCA) and snow water equivalent (SWE) at regional scale are essential for climate and hydrological applications. Optical and microwave remote sensing have their own advantages and disadvantages for monitoring snow covered areas. In this study, we present the preliminary validation results of snow cover product produced by National Snow and Ice Data Centre (NSIDC) of USA using satellite data from Advanced Microwave Scanning Radiometer-EOS (AMSR-E) on board Aqua satellite and optical remote sensing data from Terra/MODIS over the HLB region. The data consist of snow cover and snow water equivalent product for the winters from 2002 to 2008 of coarse resolution and relative fine resolution of MODIS snow cover data for the winter of 2007-08. Our primary result indicates that AMSR-E snow product tends to overestimate snow covered area of the region, and snow cover extent derived from MOD10A2 more objectively reflects the truth. Our result also indicates that elevation is not a significant factor affecting snow covered area distribution in our study region, however, land use/cover do affect the accuracy of the snow cover product, especially in forested distribution areas. In the future, we will have several more test sites in Northeast China, representing the two main types of land-cover categories: forested and agricultural areas for accurately snow cover monitoring.