# Machine Learning Regression Model for Predicting Honey Harvestis
## 2 Materials and Methods
### Satellite-Derived Data  
All other input datasets for modelling were derived from the Moderate Resolution Imaging Spectrometer (MODIS) sensor which is carried on the Terra and Aqua satellites [19], with data retrieved from the Application for Extracting and Exploring Analysis Ready Samples (AppEEARS) online portal [20]. A summary of the scientific basis for the creation of these datasets follows below. All datasets were retrieved from the AppEEARS portal at a pixel size of \(1000\times 1000\) m, from the pixel in which the apiary site is located. Retrieving data from coincident pixels meant that the subsequent integration of the data into a single database was a straightforward process. This pixel size was selected based on the typical forage range for honeybees with moderate nectar availability being 1-2 km [21]. This spatial resolution scale is also in line with studies elsewhere in the world investigating links between honeybee foraging and satellite data, such as eastern Australia [6] and Europe [22]. These studies concluded that, due to the foraging distances, the larger pixels of MODIS datasets were a more  
Figure 5: Examples of rainfall events from the Perth region viewed in the Bureau of Meteorology (BOM) rain radar for Perth [18] for (**a**) a localised and intense summer storm event in January (highlighted in red), and (**b**) the storm front of a winter thunderstorm in July. accurate representation of the foraging conditions for a given apiary site than data from sensors with a higher spatial resolution. All satellite derived data sets were filtered using the quality control channel contained in the AppEEARS download files. The particulars of the quality control parameters for each dataset can be found in the references provided in the following paragraphs. The quality control filter was set to only allow the highest quality data through into the machine learning algorithm input dataset. The Gross Primary Production (GPP) MODIS-derived product was developed by [PERSON] and [PERSON] [23], based on the theory that productivity of crops with sufficient water and nutrient availability is linearly related to the amount of absorbed solar radiation and the efficiency of its use [24]. The MOD17 data product [25] used for this study incorporates the vegetation conversion efficiency factor (\(\varepsilon\)), as well as the effect of water stress and cold conditions on this conversion factor. Ground-based validation of the MOD17 data by [PERSON] and [PERSON] [26] found that MOD17 GPP is responsive to general trends associated with local climate and land use, but tended to overestimate GPP in low productivity areas and underestimate GPP in high production areas. The MOD16A2 Net Evapotranspiration data product [27] is based on the retrieval of key parameters of the Penman-Monteith evapotranspiration formula from MODIS data [28]. A reliability study of this data product in dry, heterogenous forests against ground measurements yielded a strong correlation of \(\mathrm{r}^{2}=0.82\)[29], indicating the high overall reliability of the product. The Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) were both retrieved from the MOD13Q1 data product [30]. NDVI has been found to be strongly related to leaf chlorophyll content, whereas EVI is more indicative of canopy structural variations, including leaf area index (LAI) [31]. The Marri Flowering Index (MFI), developed by [PERSON] and [PERSON] [32], was designed for direct detection of marri flowers from MODIS data, based on an analysis of spectroradiometer surveys of this particular species. While the MFI has proven to be an effective index for classifying the marri honey harvest weight for some apiary sites with a high proportion of canopy cover [32], it appears to be less reliable across multiple apiary sites and years [9]. The Normalised Difference Water Index (NWDI) [33] is sensitive to changes in the liquid water content of vegetation canopies.