## 5 Conclusions  
Overall, this study demonstrates the ability to use remote sensing data to facilitate understanding of rubber phenology over a decade. The finding is strongly supported by  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Important Climatic** & **Rainfall** & **Temperature** & **Rainfall and Temperature** & **Temperature and Sunshine Hour** \\ \hline Rubber & & [9], This study * & [42] & [5] \\ Forest & [99; 100] & & [101] * & \\ Alpine region & & [33] *, [102] & & \\ \hline \multirow{3}{*}{Others} & Vegetation across Southeast & Savanna in Africa [104] * & \multirow{3}{*}{Grassland in China [105] *} \\  & Asia [84] * & Vegetation in Europe [92] * & & \\ \cline{1-1}  & Vegetation across Africa [103] * & Vegetation in South Korea [97] & & \\ \hline \hline \end{tabular}
* study utilising remote sensing data.  
\end{table}
Table 4: Climatic factors that influence the phenological development of several crops and regions in previous studies.  
several previous studies, including the one performed using ground data. The results also indicate that appropriate selection of data, good data fusion and proper phenological metrics extraction technique can provide enough information on phenological studies. These also minimise the adverse effects of missing data due to persistent clouds, which are common in rubber-producing countries. Combining MODIS Terra and Aqua at 250 m spatial resolution has enabled adequate data acquisition for phenological analysis. Although some fluctuations and inter-annual variability exist, these were caused by variability in rubber tree age and clones. Most SOS and EOS were within relevant timeframes, between July and August. Analysis of the results indicates that there was a significant downward trend detected in the rainfall data and a significant upward trend detected for temperature data. The mixed model analysis showed that temperature was the main factor in modulating rubber phenology.  
In addition, this is the first study to demonstrate the use of multi-series remote sensing data to monitor rubber phenology. Although the study used moderate spatial resolution, it was shown to capture the annual dynamic growth of rubber plantations due to the large and homogenous planting area. This study provides a deeper understanding of how climate modulates rubber phenological metrics. Simultaneously, this information and insight are vital to developing effective control measures for rubber diseases, as well as for predicting rubber yields.  
Conceptualisation, F.A.A., I.S.A. and A.W.; methodology, F.A.A.; resources, T.R.F.; software, F.A.A. and M.I.A.; validation, I.S.A.; analysis, F.A.A.; writing--original draft preparation, F.A.A.; writing--review and editing, A.W., A.Y. and A.A.A.; supervision, I.S.A., A.Y. and A.A.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The MODIS and Sentinel-2 data are available from USGS EarthExplorer ([[https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)]([https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)) (accessed on 1 February 2020)). Other data used in this study are available from the corresponding author upon request.  
We would like to thank Indonesian Rubber Research Institute for their excellent cooperation, valuable information, and assistance during data collection. We also thank expert reviewers for insightful contributions and suggestions. The first author acknowledges the Ministry of Higher Education Malaysia and University Malaysia Perlis for providing scholarship.  
The authors declare no conflict of interest.
Figure 10: 90-day pre-season data for: (**a**) preSOS rainfall; (**b**) preSOS temperature; (**c**) preEOS rainfall; and (**d**) preSOS temperature.