Estimating Crop Sowing and Harvesting Dates Using Satellite Vegetation Index: A Comparative Analysis  
[PERSON]  
1 Remote Sensing Postgraduate Program (POSER), Coordination of Teaching, Research and Extension (COEFE), National Institute for Space Research (INPE), Av. dos Astronautas, 1,758, Sia Jose dos Campos 12227-010, SP, Brazil; [PERSON]ADDRESS] (I.D.S. ); [EMAIL_ADDRESS] (M.A.) [PERSON]  
1 Remote Sensing Postgraduate Program (POSER), Coordination of Teaching, Research and Extension (COEFE), National Institute for Space Research (INPE), Av. dos Astronautas, 1,758, Sia Jose dos Campos 12227-010, SP, Brazil; [PERSON]ADDRESS] (I.D.S. ); [EMAIL_ADDRESS] (M.A.) [PERSON]  
2 CSIRO, 14 Underwood Av., Floreat, WA 6014, Australia; [EMAIL_ADDRESS] (J.R.); [EMAIL_ADDRESS] (R.L.) [PERSON]  
4  
[PERSON]  
2 CSIRO, 14 Underwood Av., Floreat, WA 6014, Australia; [EMAIL_ADDRESS] (J.R.); [EMAIL_ADDRESS] (R.L.) [PERSON]  
3 ESIRO, 14 Underwood Av., Floreat, WA 6014, Australia; [EMAIL_ADDRESS] (J.R.); [EMAIL_ADDRESS] (R.L.) [PERSON]  
1 Remote Sensing Postgraduate Program (POSER), Coordination of Teaching, Research and Extension (COEFE), National Institute for Space Research (INPE), Av. dos Astronautas, 1,758, Sia Jose dos Campos 12227-010, SP, Brazil; [PERSON]ADDRESS] (I.D.S. ); [EMAIL_ADDRESS] (M.A.) Footnote 1: [[https://www.mdpi.com/journal/remotesensing](https://www.mdpi.com/journal/remotesensing)]([https://www.mdpi.com/journal/remotesensing](https://www.mdpi.com/journal/remotesensing))  
Footnote 2: email: {[PERSON], 2, [PERSON], 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45  
To extract sowing and harvest dates from earth observation, several time-series methods have been developed [11; 16; 18; 19; 20; 21]. The first phenology extraction software is the TIMESAT, released in the early 2000s [2; 22; 23]. Since then, many researchers have sought to implement improvements in extracting vegetation phenology. Many have continued to implement methodologies for LSP extraction, such as CropPhenology [18], phenex [24], phenofit [21], greenbrown [25], rTIMESAT [26] developed in R language, and Digital Earth Australia tools (DEA) [27] in Python. Although much effort has been spent on developing and improving different phenology extraction packages, little effort has been conducted to evaluate and compare the differences between them. Furthermore, there is a gap in understanding the robustness of such algorithms when input with different data sources, such as data from different satellite platforms and their products. Phenology estimation from earth observation is challenging because different platforms provide different remote sensing products with issues to be dealt with, such as noisy data from cloud cover and low temporal, spatial, or radiometric resolution [28; 29; 30]. The most common remote sensing data for LSP is the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on board the Terra and Aqua satellites, which provides consistent spatial-temporal data [22; 31; 32]. However, with the wide availability and reliability of newer platforms, finer spatial resolution can present more information about vegetation seasonality and possibly more adequate spatial resolutions to investigate changes in LSP [33; 34]. Thus, an assessment of the impact of different data sources to assess LSP is needed to evaluate this method's robustness. In addition, from a practical point of view, a significant obstacle in obtaining satellite phenology is the incompatibility between ground-based and satellite-based observations [35]. The main cause of this incompatibility is the inherent difference in their respective definitions of phenology [28]. That is, the start of the season is estimated at the time when green leaf reflections are detected by the sensors [36], and it is associated with the emergence date rather than the sowing date of the crop.