# The Suitability of PlanetScope Imagery for Mapping Rubber Plantations
###### Abstract  
Quickly and accurately understanding the spatial distribution of regional rubber resources is of great practical significance. Using the unique phenological characteristics of rubber trees derived from remotely sensed data is a common effective method for monitoring rubber trees. However, due to the lack of high-quality images available during the key phenological period, it is still very difficult to apply this method in practical applications. PlanetScope data with high temporal (daily resolution have great advantages in acquiring high-quality images, but these images have not been previously used to monitor rubber plantations. In this paper, multitemporal PlanetScope images were used as data sources, and the spectral features, index features, first principal components, and textural features of the images were comprehensively utilized. Four classification methods, including a pixel-based random forest (RF) approach, pixel-based support vector machine (SVM) approach, object-oriented RF approach and object-oriented SVM approach, were utilized to discuss the feasibility of using PlanetScope data to monitor rubber forests. The results showed that the optimal time window for monitoring rubber forests in the study area spanned from the 49 th day to the 65 th day of 2019 according to the MODIS-NDVI analysis. The contribution rate of the difference in the modified simple ratio (dMSR) feature was largest among all considered features for all pixel-based and object-oriented methods. The object-oriented RF/SVM classification method achieved the best classification results with an overall accuracy of 93.87% and a Kappa index of agreement (KIA) of 0.92. The highest producer's accuracy and user's accuracy obtained with this method were 95.18% for rubber plantations. The results of this study show that it is feasible to use PlanetScope data to perform rubber monitoring, thus effectively solving the problem of missing images in the optimal rubber monitoring period; additionally, this method can be extended to other real-life applications. r 2022 2022 2022 2022 2022 2022 2022 2022 2022 2022 2022 2022 2022 2022 2022 2022 2022 20222 2022 2022 2022 20222 2022 2022 2022 20222 2022 2022 2022 20222 2022 20222 2022 2  
published through December 2020 based on searches [2] of six bibliographical databases (Science Direct, Web of Science Core Collection, Scopus, ProQuest and Google Scholar) with the keywords \"remote sensing\" and \"rubber plantation\". These studies can be summarized as follows. In terms of the remote sensing data used, according to the spatial resolution, the remote sensing images considered in the existing studies can be classified into four classes: low resolution (MODIS [3]), moderate resolution (Landsat [1; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18], PALSAR [19], ASTER [20], IRS [21; 22], THEOS [23]), high resolution (Sentinel-2 [17; 24], GaoFen-1 [17]), and very high resolution (IKONOS [25]). Single-date image information was primarily considered when mapping rubber plantations in early studies. Considering the unique wintertime phenological characteristics of rubber, such as defoliation and refoliation, multitemporal remote sensing images were adopted to obtain temporally variable features to use in estimates [3; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 19; 24]. It was verified that the method based on multiple phenology information can improve rubber estimation results [9; 10]. Landsat TM/ETM+ is currently the most broadly used sensor in rubber plantation estimation studies, especially multitemporal analyses, due to the advantages associated with its high revisit time of 16 days [2]. However, the cloudy climate conditions in the tropics introduce challenges for optical sensors; therefore, it has been very difficult to popularize this method for studies of specific periods and over large regions [9; 10].