**1. Introduction**

Ecosystem services offered by mangrove forests are facing severe risks. Within the transition zone of land and sea of (sub)tropical coastal regions, mangroves have carved out a distinct niche to flourish and thereby provide vital services to mankind. Specifically, mangrove ecosystems have shown to be one of the world's most productive in terms of carbon sequestration, shelter and breeding grounds for aquatic species, and as important physical barriers against tides and ocean surges [1–4]. Despite the multitude of crucial ecosystem services these coastal forests offer to communities in coastal regions of more than 124 countries, the status of mangrove forests in many regions is under pressure due to forest loss and land degradation, caused by overexploitation and land use change driven by human development [5–7]. Due to the inaccessible, ever-changing, and extensive nature of these mangroves, remote sensing has become a primary instrument to monitor the health and dynamics of these ecosystems [8–10].

The field of Satellite Remote Sensing has moved into an era in which a tremendous wealth of earth observation (EO) data are gathered at increasing spectral, spatial, and temporal resolutions—supporting the wide-spread application of satellite data for studying global changes [11]. Orbiting EO satellites allow us to repeatedly revisit areas of interest to study temporal changes and facilitate time series analysis. The iconic Landsat-7 and Landsat-8 missions both offer average revisit intervals of 16 days and observations that go back as early as the year 2000. The later Landsat-8 mission collected over 3.35 Petabyte of scenes over the course of a single year in 2017 [12]. These data collections hold grea<sup>t</sup> potential to improve our monitoring efforts of mangrove ecosystems and study changes over time.

A critical review by Younes Cardenas et al. (2017) on using satellite remote sensing to monitor mangrove ecosystems points out that the majority of studies conducted—reviewing 55 recent peer-reviewed articles using Landsat/Aster imagery—are not making full use of the wealth of EO data available [13]. The authors specify that most studies between 2001–2016 used fewer than 10 images and longitudinal studies often analyze temporal changes with 7–11 years between scenes which leaves much of the potential of current satellite archives unlocked [13]. Yet, mangrove forests are frequently part of fast-changing landscapes driven by land use change at the interplay of volatile aquaculture markets, policy-making, and the biophysical dynamics of erosion, sedimentation, and changing tides [14–16]. This raises the question of how we can better unlock the potential of available satellite imagery archives to facilitate high temporal resolution monitoring of the fast-paced land use processes surrounding mangrove forest ecosystems.

The advances in high-performance computing (HPC) in combination with cloud-computing services, such as provided by the Google Earth Engine platform (GEE), allow us to address the major challenges of processing and handling enormous EO datasets and turning these into comprehensible information [13,17–21]. The GEE platform provides straightforward HPC cloud access to many of the major satellite archives as well as numerous image classifiers for mapping applications, including Classification and Regression Trees (CART) and Random Forests (RF) approaches. Illustrative of its capabilities, Hansen et al. (2013) mapped global forest cover change products from over 650 thousand Landsat-7 scenes [22]. Following this, a large body of regional studies has demonstrated high mapping accuracies using GEE's land use classifiers (CART) with Landsat images [19,23,24]. More specifically, we observe an increasing use and successful implementation (accuracies between 92% and 97%) of GEE-based land use classification for mangrove mapping [25–27].

Through GEE, we can efficiently organize longitudinal time series from satellite observations and independent classification efforts can be repeated over time with ease. These time series can be valuable to study and monitor temporal changes in land use. Conversely, the temporal dependencies of each time point in the series can also be used to further optimize the time series in terms of missing information and consistency. In other words, the temporal domain can facilitate post-classification optimization of GEE output towards maps that are gap-free and temporally consistent with logical land use transitions as well as provide a means of cross-validation. This is particularly crucial in cases

with hampering climatic conditions (clouds, snow, dust, and aerosols), instrumentation errors, losses of image data during data transmission, or high uncertainties in information processing [28].

Temporal gap-filling and smoothing approaches are common practice in remote sensing of phenology and cropping cycles through continuous parameters, such as vegetation indices (e.g., NDVI, EVI) and surface parameters (Land Surface Temperature) [28–32]. However, in discrete land cover classification exercises, this practice remains less common, including in combination with the GEE platform [33,34]. Current studies tend to focus on gap-filling based on spatially neighboring pixels [35,36], spectral similarity, and/or multi-sensor (source) data fusion [34,37,38], rather than temporal integration. As such, few land use studies have taken full advantage of temporal dependencies to reduce both information gaps and inconsistent land use transitions [13,39–41]. This is a particularly rare undertaking for the monitoring of mangrove forests land use changes, whereas consistent and gap-free time series are crucial to closely monitor mangrove deforestation, degradation, and disturbance [13,15]. Land use changes tend to follow logical temporal land use transitions which can guide the optimization of classification maps [13,40].

The main objective of our study is to deploy high-performance computing techniques to monitor mangrove forest cover changes in our case study area; the mangrove-rich Ngoc Hien District, Ca Mau province in the Vietnamese Mekong delta. Rather than a single land use classification approach, we demonstrate how independent land use classifications conducted in GEE can be combined to optimize classification results in terms of completeness and consistency. As such, the study exploits both; (1) the computational capacity of GEE to deal with the entire Landsat-7 and -8 archives and (2) the temporal element of a longitudinal time series to optimize land use classification results into "gap-free" and temporally consistent information. This can help us better understand the spatio-temporal dynamics of mangrove forests, in terms of extent, distribution, and land use change and disturbances that threaten their conservation.

#### **2. Materials and Methods**
