Assessing inundation dynamics of conservation reserve program lands to quantify ecosystem services

Area of Science:

• Environmental Science
• Remote Sensing
• Hydrology
• Conservation Science

Background:

• The Conservation Reserve Program (CRP) is vital for conserving ecologically sensitive U.S. farmlands.
• Nationwide tools for continuous hydrological monitoring of CRP lands are limited.
• Surface water inundation is a key indicator of hydrologic connectivity and ecological function in CRP sites.

Purpose of the Study:

• To develop a scalable, novel approach for assessing inundation dynamics across 1.3 million CRP sites (2018-2024).
• To utilize Sentinel-2 imagery, Dynamic World land cover, and machine learning for mapping surface water extent.
• To evaluate the contribution of CRP lands to hydrological functions and inform conservation strategies.

Main Methods:

• Integrated Sentinel-2 satellite imagery, Dynamic World land cover data, and machine learning (SVM, Random Forest, CART) in Google Earth Engine.
• Employed spectral water indices (NDWI, MNDWI, NDMI) and land cover classifications to generate quarterly inundation maps at 10-m resolution.
• Validated findings using National Wetlands Inventory (NWI) and hydric soil data, focusing on the Midwest and Lower Mississippi River Basin.

Main Results:

• The Support Vector Machine (SVM) model demonstrated superior performance in surface water detection.
• Wetland-related CRP lands consistently exhibit seasonal inundation, acting as natural sponges for water quality improvement and flood mitigation.
• CRP enrollment increases inundation on non-wetland areas, contributing significantly to the agricultural landscape; longer enrollment correlates with improved wetland functionality.

Conclusions:

• The developed framework provides a long-term hydrological monitoring approach for CRP lands, supporting adaptive management.
• Seasonal inundation, particularly in spring and summer, is critical for biodiversity and migratory bird habitats.
• Identifying high-priority CRP sites for restoration using high-resolution remote sensing data enhances conservation decision-making and policy strategies.