## 1 Introduction  
Vegegetation phenology plays an important role in climate-carbon cycle feedback such as determining the impacts of climate change on the growing season and characterizing the sensitivity of ecosystem processes to climate change [1; 2; 3; 4]. Satellite remote sensing datasets have been extensively used to study vegetation phenology over space and time [5; 6; 7; 8; 9; 10; 11]. The Advanced Very High Resolution Radiometer instrument (AVHRR) onboard the National Oceanographic and Atmospheric Administration (NOAA) satellite series had been used to study global-scale phenology back in the 1990s [11]. Other satellite datasets, including the Satellite Pour I'Observation de la Terre-Vegetation (SPOT-VGT) [12], Landsat [13], and the Moderate Resolution Imaging Spectroradiometer (MODIS), have also been utilized to investigate vegetation phenology to understand the impacts of phenological changes on terrestrial carbon budget and evaluate crop productivity and management [14]. Many studies combined two [6; 8; 15] or more satellite datasets [9; 16] to take advantage of different sensors with various spatial and temporal resolutions. For example, one can merge the long-term AVHRR and Landsat datasets with high-quality but short-term MODIS and SPOT-VGT datasets to investigate the trends of regional land surface phenology [16]. The Normalized Difference Vegetation Index (NDVI), which combines two vegetation-sensitive bands to strengthen and simplify the information contained in vegetation [17; 18], is the most widely used index in vegetation monitoring. It first gained popularity in vegetation phenology detection [2; 19; 20; 21]. The Enhanced Vegetation Index (EVI) [22], which reduces sensitivity to soil and atmospheric effects, was frequently used in recent years in the monitoring of vegetation phenology [5; 7; 23]. Hybrid approaches, which combine multiple indicators, have also been developed [10; 12; 24]. In terms of phenology detection methods, a large variety of software packages exist [5; 7; 9; 10; 25; 26; 27; 28], with TIMESAT being one of the most popular software packages to analyze time series of satellite data [28]. Many case studies have tried to investigate the climatic controls on vegetation phenology using climate data and vegetation phenology from various satellite datasets [2; 23; 29; 30]. However, to truly understand carbon cycling on a global scale, assessing the impacts of climate and management interactions on vegetation phenology is also essential, though challenging to quantify due to their confounding effects [9; 26; 31; 32]. Using information on vegetation greenness and land surface temperature from MODIS, a disturbance index named MODIS Global Disturbance Index has been developed to study global disturbances [33; 34]. This index can help detect disturbance events, but has difficulty in identifying the causality without the support of other climate and management data. Additionally, the commonly used 500 m surface reflectance product [35] is generally too coarse for monitoring localized disturbances [36] such as grazing, baling, and patch burning. Finer spatial resolution of Landsat imagery (optical: 30-80 m and thermal: 60-120 m) can enable more detailed vegetation monitoring. While in comparison to the daily MODIS overpass, the 16-day revisit cycle of Landsat is more susceptible to cloud cover, making it prone to miss the short-term disturbances and quick recovery of vegetation after disturbances. Combining MODIS and Landsat is a good strategy to balance the trade-off between spatial and temporal resolutions [37] for studying field-scale vegetation responses to climate and management. In addition to frequent exposure to drought [38; 39; 40; 41; 42; 43; 44], grassland in the Great Plains of the United States also experiences various common management practices such as burning, grazing, and baling (hay harvesting). Prescribed burning is a recommended management practice to sustain the grassland ecosystem function and structure [45; 46; 47].