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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publications at this Location

Publications at this Location

ARS scientists publish results of their research projects in many formats. Listed below are the publications from research projects conducted at this location.

Clicking on a publication title will take you to more information on the publication. Clicking on the reprint icon Repository URL will take you to the publication reprint.

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2023 Publications
(listed by order of acceptance date)

Current View: Peer Reviewed Publications Only

Show All Publications || Peer Reviewed Journal Publications Only

Displaying 1 to 10 of 10 Records

Multivariate calibration of the SWAT model using remotely sensed datasets Reprint Icon
(Peer Reviewed Journal)
Crop yield prediction via explainable AI and interpretable machine learning: Dangers of black box models for evaluating climate change impacts on crop yield Reprint Icon
(Peer Reviewed Journal)
SWAT-3PG: Improving forest growth simulation with a process-based forest model in SWAT Reprint Icon
(Peer Reviewed Journal)
Potential of remote sensing surface temperature- and evapotranspiration-based land-atmosphere coupling metrics for land surface model calibration Reprint Icon
(Peer Reviewed Journal)
Simulating agroecosystem soil inorganic nitrogen dynamics under long-term management with an improved SWAT-C model Reprint Icon
(Peer Reviewed Journal)
Form field observations to temporally dynamic roughness retrievals in the corn belt. Reprint Icon
(Peer Reviewed Journal)
ET partitioning assessment using the TSEB model and sUAS information across California Central Valley vineyards Reprint Icon
(Peer Reviewed Journal)
Applications of a thermal-based two-source energy balance model coupling the sun-induced chlorophyll fluorescence data Reprint Icon
(Peer Reviewed Journal)
Towards scalable within-season crop mapping with phenology normalization and deep learning Reprint Icon
(Peer Reviewed Journal)
Development of a benchmark eddy flux ET dataset for evaluation of remote sensing ET models over the CONUS Reprint Icon
(Peer Reviewed Journal)