Lake Baiyangdian, a largest wetland ecosystem in North China Plain, has dried up on seven occasions since the 1960s. In
recent years, more than one billion of cubic meters of water from upstream reservoirs and Yellow river have been
transported to the lake to rescue the shrinking wetlands. Since the Lake Baiyangdian was actually composed of 143 small
lakes and more than 70 villages with large or small area of cropland, dynamic distribution of aquatic plants in wetland
such as reed and associated growth condition of these allowed to monitor the changes of wetland landscape and water
quality to support the policy applications of water conveyance and wetland environmental treatment and control.
Assisted with ground survey analyses and Landsat TM image, the MODIS 250 m time series Normalized Difference
Vegetation Index (NDVI), given its combination of medium spatial and high temporal resolution, were applied to detect
the unique rapid growth stage of reed in the spring from adjacent crops such as winter wheat, cotton, and spring maize,
of which has a similar phenology in development of leaf area index, and dynamic reed areas were mapped in recent
decade. Landscape changes of the wetland were analyzed using maps of reed area and hydrological data.
Mapping grain crop land productivity that associated soil quality and crop field management are needed over intensively
cropped regions such as the North China Plain to support science and policy application focused on understanding the
current and potential capacity of regional food support. In this study, the crop growth dynamic presenting by time series
field Greenness derived from MODIS 250 m data and soil moisture condition assessing by Normalized Difference Water
Index (NDWI) derived by MODIS 250 m and 500 m data were combined to detect the temporal and spatial variability
of productivity of winter wheat-summer maize field in the period 2000 to 2008 in Hebei and Shandong Province in
North China Plain. Annual average NDVI levels, average levels of nine years and coefficients of variation of levels in
the main growing season indicated corresponding crop growth condition and clearly presented spatial distribution of crop
growth. Both the levels of NDWI and the coefficients of variation of the levels have almost same pattern of spatial
distribution and correlations between two indexes levels were very high. The results of analysis of levels and coefficients
of variation of levels of NDVI and NDWI shows the combination analysis of two indexes can be used to assess the levels
of land productivity with a high spatial or temporal resolution .
KEYWORDS: Nitrogen, Soil science, Remote sensing, Magnetic resonance imaging, Data modeling, Agriculture, MODIS, Data acquisition, Geographic information systems, Statistical modeling
Overuse of chemical fertilizers raises the risk of nitrate pollution of groundwater in the North China Plain. To preserve
the groundwater and reduce the economic losses, an efficiently and quickly assessment of nitrate leaching risk on
regional farmland is crucial. In this research we developed a GIS-based model named 'Arc-NLEAP' based on NLEAP
model, combined the statistical and Remote Sensing data, to estimate applied fertilizer rates and crop yields, which are
two key variables indicating amount of input and output nitrogen in crop land, since crop greenness derived by MODIS
may reflect the content of chlorophyll of canopy which is closely related to nitrogen content, and NDVI values of crop
crucial growing periods determine crop production. The simulated results showed that the value for parameter NAL
(Nitrate Available for Leaching) was between 8 kg / ha and 474 kg / ha and the average was 117 kg / ha, for NL (amount
of Nitrate Leached) 18kg / ha (Low) , 59 kg / ha (Average) and 222 kg / ha(High).Percentages of parameter
MRI(Movement Risk Index) accounted for 8%,77% and 15% for low risk, medium risk and high risk respectively.
Taking water leaching index, nitrogen available for leaching, amount of Nitrate Leached, ammonia volatilization and
denitrification into consideration, we defined the N hazard class to evaluate the nitrogen leaching risk and the result
indicated that lager 74% of the study area was labeled as low N hazard class. Despite the spatial patterns for parameters
NAL and NL were similar, the values for MRI was determined by site-specific soil type and the capacity of water
movement principally, demonstrating that measures of controlling nitrate leaching should be based on the spatial pattern
of MRI, along with decreasing the amount of application rate simultaneity.
Tibet Plateau plays an important role in global changing and ecosystem studies because of its unique geographical
location and topography. Lhasa river basin which locates in the center of Tibet Plateau is a typical and important region
for agriculture and stockbreeding in Tibet. In this study a method of land cover mapping from 250m MODIS (Moderate
Resolution Imaging Spectroradiometer) product Normalized Difference Vegetation Index (NDVI) MOD13Q1 data is
presented. This knowledge-based method combines phenophase character of plants with time-series remote sensing data
and Geographic Information System spatial analysis. A quality assessment analysis is performed to time-series data by
temporal and spatial interpolation of invalid and missing data. The NDVI value is converted into a relative NVDI to
avoid the misclassification arising by data change of spatial and temporal. The preliminary results are compared with
both field observation points and classification mapped from Landsat TM imagery. The comparison indicates the result
of classification is promising.
Spatial variability of crop growth often needs to be evaluated due to different soil conditions, weather patterns and crop
information in a region. To simulate crop growth and productivity at a regional scale, a RS- and GIS-based crop growth
model named RS-CGM was developed. The model calculates crop distribution, leaf area index, soil water content using
remote sensing data that were integrated in crop growth module by inputting direct forcing variables, re-calibrating
specific parameters, and correcting yield prediction using simulation-observation difference of a variable. The main RS-CGM
components were intensively calibrated and verified against comprehensive field measurements of soil conditions,
irrigation, evapotranspiration (ET), crop leaf area index (LAI) and yields. .The RS-CGM was applied to a county in the
North China Plain to simulate winter-wheat yields in spatial and temporal dimensions. The model divides the simulating
area into a number of crop growth elements and calculates each element with a set of parameters, then achieves the
spatial crop yields and other concerned results aggregating to administrative regions. The simulated results show that the
model can effectively express the spatial variety of yields in a region. And suggest that it was feasible to develop a
spatial crop growth model combined with GIS, RS, and physiological process-oriented.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.