Alfalfa (Medicago sativa L.), 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. With ever-increasing capability, it is possible to use satellite remote sensing data to monitor alfalfa harvests. This study investigated the potential of using satellite remote sensing to capture frequent harvesting events on an alfalfa field in central Oklahoma. Both passive remote sensing data, namely Moderate Resolution Imaging Spectroradiometer (MODIS), Landsat-7 and -8, Sentinel-2, Harmonized Landsat and Sentinel-2 (HLS), 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 m) 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 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 minimized before it can be used to detect alfalfa harvests at the regional scale. In most cases, both Sentinel-1 radar backscatter coefficients (vertical transmit and vertical receive, VV + vertical transmit and horizontal receive, 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 relationships existed between backscatter or coherence and alfalfa harvests in larger incident angle observations (45.11°). Future studies 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. This study illustrates that combining multiple optical sensors with a 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.

紫花苜蓿（Medicago sativaL.)，因其在牧草作物中的重要性而被称为“牧草皇后”，为畜牧业提供优质牧草。苜蓿干草收获的时间和频率对其质量和数量有影响。随着能力的不断增强，利用卫星遥感数据监测苜蓿收成成为可能。这项研究调查了使用卫星遥感捕捉俄克拉荷马州中部苜蓿田频繁收获事件的潜力。被动遥感数据，即中分辨率成像光谱仪（MODIS）、Landsat-7和-8、Sentinel-2、Harmonized Landsat和Sentinel-2（HLS），以及主动遥感数据，即Sentinel-1，都包括在内。我们的结果表明，高质量的光学遥感数据集（即 云和无云影）具有精细空间（≤100 m）和高时间分辨率（以 8 天或更好的间隔进行有效观测）的分辨率对于检测频繁的苜蓿收获事件是必要的，这些事件受到可能的不利天气条件和植被快速再生的挑战收获后。Landsat（7 和 8）和 Sentinel-2 对收获后植被指数的变化比 MODIS 更敏感，因为它们具有更高的空间分辨率，这有助于避免 MODIS 中由于其较粗的空间分辨率（~500 m）引起的混合像素问题。通过标准化差异植被指数 (NDVI) 值之间的线性回归，将 Landsat（7 和 8）与 Sentinel-2 图像相结合，最多相隔一周，提高了检测频繁的苜蓿收获事件的准确性。HLS 对苜蓿收获事件的响应与融合 Landsat 和 Sentinel-2 数据使用它们的 NDVI 值的线性关系相似。然而，HLS 数据中的高噪声水平需要最小化，然后才能用于检测区域尺度的苜蓿收获。在大多数情况下，Sentinel-1 雷达后向散射系数（垂直发射和垂直接收，VV + 垂直发射和水平接收，VH）和来自 Sentinel-1 Simple Look Complex (SLC) 数据的干涉相干性都因小事件中的捕获事件而降低角度观察（34.31°）。在较大的入射角观测 (45.11°) 中，反向散射或相干性与苜蓿收获之间不存在一致的关系。未来的研究应该集中在小入射角观测上，而不是处理所有的雷达数据，数据量大，耗时长。总体而言，有源雷达具有检测苜蓿收获事件的潜力。然而，它在视觉上不如光学数据直观，因为入射角、收获量和土壤湿度是复合因素。这项研究表明，结合多个具有精细空间分辨率的光学传感器（例如 Landsat-7、8 和 Sentinel-2）和/或将雷达与光学遥感融合以提高时间分辨率是检测频繁的苜蓿收获事件的有前途的方法和其他干草收获活动。土壤水分是复合因素。这项研究表明，结合多个具有精细空间分辨率的光学传感器（例如 Landsat-7、8 和 Sentinel-2）和/或将雷达与光学遥感融合以提高时间分辨率是检测频繁的苜蓿收获事件的有前途的方法和其他干草收获活动。土壤水分是复合因素。这项研究表明，结合多个具有精细空间分辨率的光学传感器（例如 Landsat-7、8 和 Sentinel-2）和/或将雷达与光学遥感融合以提高时间分辨率是检测频繁的苜蓿收获事件的有前途的方法和其他干草收获活动。