研究目的
To develop and demonstrate a lightweight L-band radiometric imaging system integrated into a small unmanned aerial system (sUAS) for high spatial resolution soil moisture mapping, supporting SMAP validation, precision agriculture, and other applications.
研究成果
The LDCR system successfully provides high spatial resolution soil moisture maps with good correlation to in-situ data. It is cost-effective and suitable for various applications. Future work includes enhancing RFI mitigation and vegetation correction for better accuracy.
研究不足
The LDCR VSM estimation error is approximately 4-7%, which could be reduced with advanced algorithms like time deconvolution. RFI mitigation is still under development, and vegetation effects require further correction for improved precision.
1:Experimental Design and Method Selection:
The study uses the Lobe Differencing Correlation Radiometer (LDCR) with a differential correlating architecture for intrinsic gain stability and sky calibration. It employs an unbiased Linear Minimum Mean Square Error (LMMSE) estimation method for soil moisture retrieval, considering antenna radiation patterns, soil type, vegetation cover, and thermal measurements.
2:Sample Selection and Data Sources:
Field experiments were conducted at the Canton Oklahoma Soilscape site (September 2015) and Yuma Colorado Irrigation Research Foundation (IRF) site (June-August 2016, October 2017). Data includes in-situ soil moisture measurements, Landsat 7 ETM+ data for vegetation water content, and LDCR measurements.
3:7). Data includes in-situ soil moisture measurements, Landsat 7 ETM+ data for vegetation water content, and LDCR measurements.
List of Experimental Equipment and Materials:
3. List of Experimental Equipment and Materials: LDCR Rev A and Rev B radiometers, Tempest and SuperSwift sUAS platforms, MiCo antenna arrays, RF/microwave hardware, digital and analog correlators, and navigation systems.
4:Experimental Procedures and Operational Workflow:
The sUAS flew serpentine tracks at low altitudes to collect LDCR data. Flights were performed at specific times and directions (e.g., E-W and N-S). Data processing involved Kriging for mapping, RFI mitigation, and vegetation correction.
5:Data Analysis Methods:
Data analysis included spatial covariance estimation for Kriging, correlation with in-situ measurements, RFI detection using spectral and statistical methods, and development of crop-specific retrievals.
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