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ARS Home » Plains Area » Temple, Texas » Grassland Soil and Water Research Laboratory » Research » Publications at this Location » Publication #376852
Research Project: Contributions of Climate, Soils, Species Diversity, and Management to Sustainable Crop, Grassland, and Livestock Production Systems

Location: Grassland Soil and Water Research Laboratory

Title: Detecting frequent harvesting of alfalfa using active and passive remote sensing

Author
item ZHOU, YUTING - Oklahoma State University
item Flynn, Kyle
item Gowda, Prasanna
item Wagle, Pradeep
item MA, SHENGFANG - Independent Ecological Researcher
item KAKANI, VIJAYA - Oklahoma State University
item STEINER, JEAN - Kansas State University

Submitted to: American Geophysical Union
Publication Type: Abstract Only
Publication Acceptance Date: 10/5/2020
Publication Date: 12/9/2020
Citation: Zhou, Y., Flynn, K.C., Gowda, P.H., Wagle, P., Ma, S., Kakani, V.G., Steiner, J.L. 2020. Detecting frequent harvesting of alfalfa using active and passive remote sensing. American Geophysical Union Annual Meeting, December 9-13, 2020, Virtual.

Interpretive Summary:

Technical Abstract: Harvesting alfalfa (Medicago sativa L.) hay is of high importance for the livestock industry as it provides quality forages. Traditionally, stockholders depend on statistical data for information regarding alfalfa harvests. Remote sensing has been seldom used in detecting alfalfa harvests due to the need for both high spatial and temporal resolutions to detect short-term events at the field scale. To investigate the potential of using satellite remote sensing to capture frequent alfalfa harvests in an alfalfa field, which was also monitored by an eddy covariance tower, this study used active and passive remote sensing data, namely Moderate Resolution Imaging Spectroradiometer (MODIS), Landsat-7 and -8, Sentinel-1 and -2, and Orbiting Carbon Observatory-2 solar-induced chlorophyll fluorescence (OCO-2 SIF). Our results indicate that both fine spatial (= 100 meter) and high temporal (effective observation for every 8 day) resolutions are important for detecting alfalfa harvests. Landsat (7 and 8) and Sentinel-2 are more sensitive to changes in vegetation indices after harvest than MODIS which suffered a mixed pixel issue caused by its coarser spatial resolution (~500 m). Combining both Landsat (7 and 8) with Sentinel-2 imageries increased the accuracy of detecting frequent alfalfa harvests. In most cases, Sentinel-1 radar backscatter coefficients (VV and VH) were decreased by harvesting events in small incident angle observations (34.31°). No consistent relationship existed between backscatter and alfalfa harvests in larger incident angle observations (45.11°). Overall, active radar showed the potential to detect alfalfa harvest events. However, it is visually less intuitive than optical data with incident angles, harvest sizes, and soil moisture being the compounding factors. OCO-2 SIF was limited by its sparse spatial sampling in alfalfa harvests detection. This study illustrates that combining multiple optical sensors with fine spatial resolution (e.g., Landsat-7, 8, and Sentinel-2) or fusing radar with optical remote sensing to increase the temporal resolution are promising approaches for detecting field scale disturbances such as frequent hay harvests.