## 1 Introduction  
Grasslands represent one of the Earth's most common vegetation types [1; 2], covering nearly a fifth of the planet's land [3] and providing important ecological, economic, and cultural services. They are responsible for an estimated 16%-17% of global primary production [4; 5; 6], serve as hotspots for floral and faunal biodiversity [7; 8], support endemic species [7; 8; 9], affect runoff and water quality [10], and contain up to 30% of the Earth's total soil carbon, thus reducing greenhouse gas emissions [2; 8]. Grasslands are critical to the maintenance of human economies, livelihoods, and cultures, particularly for low-income and marginalized peoples [8; 11]. In 2006, area allocated for livestock grazing covered a quarter of the Earth's ice-free land [12]. By supporting livestock, grasslands allow people to produce high-protein food, such as meat and milk [8], in addition to creating employment opportunities and generating income [11]. In recent decades, grasslands have suffered severe and increasing degradation;between 2000 and 2010, approximately half of global grassland ecosystems underwent degradation, due primarily to climate change and human activities [13]. The importance of grasslands, paired with their vulnerability to climate change and human activity, makes their management a high priority. However, monitoring and managing grasslands is challenging. Field surveys of grass condition and production are costly and difficult to implement at the scale of large grasslands [14; 15; 16; 17]. Remote sensing has great potential for improving grassland monitoring. While traditionally underutilized, remotely sensed data have been employed in several recent studies to detect grass cover, biomass, and height in temperate to hyper-arid grazing ecosystems (e.g., [18; 19; 20; 21; 22; 23; 24; 25]). The studies cited here primarily used vegetation indices and fractions derived from data from passive, optical sensors, most commonly Landsat TM, to investigate correlates of spectral signatures with biophysical properties of grass, particularly biomass. [PERSON] et al. [21], [PERSON] et al. [22], and [PERSON] and [PERSON] [25] specifically focused on predicting grass height, using Landsat [21; 22] or Landsat and Sentinel [25] data to explain up to 80% of variation in grass height [21]. Only two of the studies cited here [24; 25] used radar data in their analyses. [PERSON] et al. [24] combined multitemporal optical data with multitemporal radar data collected in the X-band, C-band, and L-band; they described correlations between data collected via different sensors but did not incorporate field data on land cover. More recently, [PERSON] and [PERSON] [25] combined Landsat and Sentinel imagery with field data to predict grass height at medium resolution, but with limited success. Although previous work suggests that remote sensing may be applicable to grassland management, several gaps in our current capabilities are apparent. First, most efforts to map grasslands fail to exploit the benefits of optical and radar sensor fusion. Second, most of the studies to date have been conducted using highly homogenous grasslands, such as pastures and prairies [17; 19; 22; 23; 24]. [PERSON] et al. [18], [PERSON] et al. [20], [PERSON] et al. [21], and [PERSON] and [PERSON] [25], on the other hand, used spatially heterogeneous study sites, but with varying success in characterizing the biophysical properties of grass. The mapping algorithm applied to the most heterogeneous of the landscapes (a hyper-arid erosional cirque in Israel [18]) performed particularly poorly at characterizing plant cover. Here, we seek to build upon previous work by extending the use of remote sensing in estimating land cover to include differentiation of discrete grass height classes in a dynamic savanna landscape representing a mosaic of open grasslands, shrubs, riverine forests, and wetlands.