Modeling canopy water content in the assessment for rainfall induced surface and groundwater nitrate contamination: The Bilate cropland sub watershed
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View abstract on PubMed
Summary
This summary is machine-generated.This study predicts canopy water content to assess nitrate contamination from fertilizer runoff in agricultural watersheds. Findings help monitor water quality and manage nitrogen fertilizer loss effectively.
Area Of Science
- Environmental Science
- Agricultural Science
- Remote Sensing
Background
- Nitrate contamination in water is a significant issue in agricultural areas, mainly due to nitrogen fertilizer runoff.
- Predicting water quality impacts from agricultural practices is crucial for environmental management.
Purpose Of The Study
- To predict canopy water content for determining the nitrate contamination index.
- To assess nitrogen fertilizer loss in surface and groundwater using Earth observation data.
Main Methods
- Utilized the Geographically Weighted Regression (GWR) model.
- Integrated MODIS satellite data (MOD13Q1-EVI), crop information, and rainfall data.
- Calibrated satellite data with regional crop calendars and plant biomass.
Main Results
- Achieved high correlation (R² = 0.996) between predicted and observed rainfall, validating canopy water content predictions.
- Plant biomass ranged from 0.19 to 0.57 kg/m², supporting crop water productivity monitoring.
- Measured nitrate contamination index values varied across assessed years (2004-2020).
Conclusions
- Canopy water content serves as a reliable indicator for assessing nitrate contamination.
- The GWR model effectively predicts water content for environmental monitoring.
- This approach aids in understanding and mitigating nitrogen fertilizer impacts on water quality.
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