Location: Grassland Soil and Water Research LaboratoryTitle: The potential of active and passive remote sensing to detect frequent harvesting of alfalfa
|ZHOU, YUTING - Oklahoma State University|
|MA, SHENGFANG - Independent Ecological Researcher|
|KAKANI, VIJAYA - Oklahoma State University|
|STEINER, JEAN - Kansas State University|
Submitted to: International Journal of Applied Earth Observation and Geoinformation
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/6/2021
Publication Date: 9/15/2021
Citation: Zhou, Y., Flynn, K.C., Gowda, P.H., Wagle, P., Ma, S., Kakani, V.G., Steiner, J.L. 2021. The potential of active and passive remote sensing to detect frequent harvesting of alfalfa. International Journal of Applied Earth Observation and Geoinformation. 104. Article 102539. https://doi.org/10.1016/j.jag.2021.102539.
Interpretive Summary: The timing and frequency of alfalfa hay harvesting have implications on its quality and quantity. Traditionally, the livestock industry depends generally on survey and statistical data for information regarding alfalfa harvests. Remote sensing has been seldom used in detecting alfalfa harvests due to the need for greater revisit times and pixel sizes as alfalfa is harvested many times throughout the growing season. This study investigated the potential of using satellite remote sensing to capture frequent harvesting events on an alfalfa field in central Oklahoma. Moreover, it was concerned with the power of combining many satellites with differing revisit times and sensitivities. This study illustrates that combining multiple optical-based sensors with fine spatial resolution and/or fusing radar with optical remote sensing to increase the revisit times are promising approaches to detect frequent alfalfa harvesting events.
Technical Abstract: Alfalfa (Medicago sativa L.), also referred to as the “Queen of Forages” because of its importance among forage crops, provides high quality forage for the livestock industry. The timing and frequency of alfalfa hay harvesting have implications on its quality and quantity. Traditionally, livestock industry depends generally on survey and 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 harvesting events at a field scale. This study investigated the potential of using satellite remote sensing to capture frequent harvesting events on an alfalfa field in central Oklahoma, which was also monitored with an eddy covariance system. Both passive remote sensing data, namely Moderate Resolution Imaging Spectroradiometer (MODIS), Landsat-7 and -8, Sentinel-2, Harmonized Landsat and Sentinel-2 (HLS), and Orbiting Carbon Observatory-2 solar-induced chlorophyll fluorescence (OCO-2 SIF), and active remote sensing data, namely Sentinel-1, were included. Our results indicate that good quality optical remote sensing datasets (i.e., cloud and cloud shadow free) with both fine spatial (= 100 meter) and high temporal (effective observation at 8-day intervals or better) resolutions are necessary to detect frequent alfalfa harvesting events challenged by possible adverse weather conditions and quick regrowth of vegetation after harvest. Landsat (7 and 8) and Sentinel-2 were more sensitive to changes in vegetation indices after harvest than MODIS due to their higher spatial resolutions which helped avoid the mixed pixel issue in MODIS caused by its coarser spatial resolution (~500 m). Combining both Landsat (7 and 8) with Sentinel-2 imageries through linear regression between the Normalized Difference Vegetation Index (NDVI) values, up to one week apart, increased the accuracy of detecting frequent alfalfa harvesting events. The responses of HLS to alfalfa harvesting events were similar with fused Landsat and Sentinel-2 data using their linear relationship of NDVI values. However, the high noise level in the HLS data needs to be reduced before it can be used to detect alfalfa harvests at regional scale. In most cases, both Sentinel-1 radar backscatter coefficients (VV and VH) and interferometric coherence from Sentinel-1 Simple Look Complex (SLC) data were decreased by harvesting events in small incident angle observations (34.31°). No consistent relationship existed between backscatter or coherence and alfalfa harvests in larger incident angle observations (45.11°). Future researchers should focus on small incident angle observations instead of processing all of the radar data, which has big data volume and is time-consuming. Overall, active radar has the potential to detect alfalfa harvesting events. However, it is visually less intuitive than optical data with incident angles, quantity harvested, and soil moisture being the compounding factors. The OCO-2 SIF was limited in detecting alfalfa harvesting events by its sparse spatial sampling. Spatially contiguous SIF observations are needed to further facilitate its application on monitoring alfalfa harvesting events. This study illustrates that combining multiple optical sensors with fine spatial resolution (e.g., Landsat-7, 8, and Sentinel-2) and/or fusing radar with optical remote sensing to increase the temporal resolution are promising approaches to detect frequent alfalfa harvesting events and other hay harvesting activities.