# A Very High-Resolution Urban Green Space from the Fusion of Microsatellite, SAR, and MSI Images
## 1 Introduction  
Urban green space (UGS) provides the residents of a city with various benefits, including improved public health and well-being [1, 2, 3], social sustainability [4], contact with nature [5], and ecosystem service [6]. Some studies have argued that UGS also provides benefits to older people [7, 8] and improves mental health [9, 10]. Studies have defined UGS through greenness. Greenness can be quantified using spectral indices like the Normalized Vegetation Inference Index (NDVI) [11]. Another study classified UGS from a combination of hyperspectral images and LiDAR data [12]. Both studies utilized spectral-based analysis to classify UGS using Earth observation data. A recent study [13] presented a method for classifying UGS based on their features using a deep learning approach. However, this method requires significant computational resources due to its complexity. A comprehensive study was conducted to evaluate the usage of UGS on a national scale, utilizing big data analysis. It also considered the variations in UGS utilization between areas within the city (intra-urban) and its outskirts (peri-urban). The study encompassed 366 cities across mainland China. The results indicated that 94.01% of UGS in China remained unused. This study used data with a spatial resolution of 1 km \(\times\) 1 km [14]. Another study conducted in Xiamen, China, aimed to assess the fairness of UGS andexplored how they are distributed in relation to urbanization. The study discovered that increased urbanization correlated with a greater provision of UGS services. It used images of Landsat-8 OLI from 2015 with a spatial resolution of 30 m \(\times\) 30 m [15]. A study conducted in 2017 across 371 major cities in Latin America resulted in the creation of the first 10 m resolution UGS map for the main urban clusters. This study utilized a supervised classification method on Sentinel-2 satellite images and achieved an accuracy rate of 87% [16]. The increasing collection of remote sensing images from MODIS, Landsat, and Sentinel series, along with the availability of cloud-based image processing platforms such as Google Earth Engine (GEE), have the potential to enhance the UGS classification maps. However, the public currently has access to a maximum spatial resolution of 10 m. In this study, the use of Planet datasets was introduced, which offer a higher spatial resolution of 3 m.  
The current methods and technologies used to monitor urban green spaces, such as aerial photography and satellite imagery, have some limitations. Aerial photogrammetry can provide very high spatial resolution but only covers small areas. To cover larger areas, more time and expense are required. Meanwhile, satellite imagery offers broader coverage but suffers from low spatial resolution. Another challenge arises from cloud coverage in tropical countries like Indonesia. To address these issues, the suggestion in this study is to utilize Sentinel-1 SAR data to overcome cloud cover challenges. Additionally, there are existing gaps or limitations in current research concerning urban green space monitoring. For instance, accurately mapping and quantifying green spaces in densely built urban environments, such as Jakarta, poses significant challenges. Moreover, there is insufficient integration of multiple remote sensing data sources for comprehensive analysis. Thus, in this study, the proposal is to integrate multiple remote sensing data sources, including microsatellite, SAR, and MSI, to potentially benefit from improved accuracy, enhanced feature detection, and better discrimination of urban green space types. Integrating satellite data from different sensors is a common approach in remote sensing. To the best of the author's knowledge, two studies have partially quantified the improvement from applying a machine learning approach to Planet data within GEE. A previous study integrated Planet imagery with Sentinel-1 and Sentinel-2 data to create land use and land cover maps for Central Brazil.