# A SAR-Based Index for Landscape Changes in African Savannas
## 1 Introduction  
Detecting and understanding processes at the earth's surface are among the key tasks of spaceborne remote sensing. Thousands of images stored in archives allow for the analysis of dense time-series of nearly every region of the earth. In particular is the Landsat continuity mission, delivering valuable data since the 1970s, which provides an excellent foundation of long-term observations [1; 2]. Their potential for the mapping of land-use and land cover (LULC) has been demonstrated in numerous studies which exploit the temporal variability of the image information in different methodological frameworks [3; 4; 5; 6; 7; 8; 9; 10]. Among others, [PERSON] et al. portray the problems regarding atmospheric disturbances in the data and raise the question to what extent these applications are affected by cloud cover [11]. Of primary concern is large-scaled classification of LULC often being constrained by cloud cover. According to the US Department of Energy, about 52% of global land surfaces are covered by clouds on average [12]. In particular, regions within the intertropical convergence zone (ITC) are highly affected by seasonal or full-year cloud coverage [13; 14; 15] which hinders proper analysis of LULC based on series of optical satellite imagery [16; 17; 18]. To overcome this dependency upon favorable atmospheric conditions, satellites with synthetic aperture radar (SAR), operating at wavelengths which can penetrate cloud cover, are employed. Their reliability in acquiring usable imagery is one of the main reasons to utilize them in LULC applications. Accordingly, the long-term missions of ERS-1/2 [19] and RADARSAT-1/2 [20], plus the relatively recent Sentinel-1 constellation [21] which was launched in 2014, show high potential for the investigation of changes in land cover over decades. They also serve well as complementary sources when operating at the same wavelength [22; 23]. The use of SAR imagery has proven effective for LULC classification in many cases. Early studies mostly applied knowledge-based methods on SAR backscatter in order to separate different classes of land cover [24; 25; 26]. In fact, the first article published in Remote Sensing of Environment, back in 1969, was an interpretation key for SAR backscatter at the landscape level [27]. These studies require detailed a-priori knowledge about the study area but offer a high degree of control to the user. Additional features were used to further increase the classification quality in later approaches, mainly interferometric parameters such as coherence [28; 29; 30], but also textural information within the intensity values at different levels [31; 32; 33]. Besides these technological advances, new methods of supervised classification were developed to assign classes based on sample data. These were required because, in contrast to optical image information, SAR parameters are not necessarily of consistent units and value ranges. Among the most popular methods are Bayesian classifiers [34; 35; 36], neural networks [37; 38; 39] and random forest classifiers [40; 41; 42]. These developments targeting LULC classifications were complemented by progress in SAR polarimetry. Deeper knowledge of the backscattering behavior of surfaces and decompositions into different scattering mechanisms additionally increased the quality of analyses related to land cover [43; 44; 45; 46]. Utilizing SAR imagery in a multi-temporal context to monitor changes in land-use or land cover requires particular attention because of the characteristics of image acquisition and signal propagation. While the very identification of areas of change is scientifically well-proven and widely-used [47; 48; 49], quantitative and long-term monitoring of distinct classes requires accurate calibration and a robust handling of speckle [50; 51].