Rice is the most important staple crop in the Philippines. Like many other rice producing countries in Asia, it is cultivated in all regions of the country. A diversity of rice varieties, management practices and environments lead to a large variation in seasonality (when rice is grown) and cropping intensity (how often rice is grown per year in the same plot of land, once, twice or even three times per year). Seasonality and intensity also change over time depending on climatic, environmental and economic factors. Detecting where and when these changes occur can provides information on where and how rapidly practices have changed over time and why. Remote sensing offers a unique opportunity to detect these changes and trends with methods that can account for spatial and temporal variability.
We applied the PhenoRice algorithm to 14 years of moderate resolution remote sensing (MODIS) data (utilizing 250m resolution 16 days composites from Terra and Aqua) over the Philippines to detect rice growing areas, Start and End dates (SoS and EoS) and Length (LoS) of each season. The detected intensity and seasonality were compared to published data – static provincial level crop calendars - to check that the PhenoRice detections were valid. Then, to generate statistically robust estimates of trends in seasonality we averaged the SoS and EoS of each detected rice season within a 10km hexagonal tessellation over the entire country and mapped the spatial patterns of trends in SoS, EoS and LoS.
We observed a general trend towards shorter LoS in both wet and dry seasons, with seasons reducing by as much as one day per year over the 14 years period. The trends in SoS and EoS were much more variable over the country, with some regions showing trends of earlier SoS and EoS and others later. The strong spatial clustering of these trends suggests that they are influenced by local factors such as adoption of new management practices, changes in irrigation water release dates and weather. We discuss the spatial patterns in relation to these factors and describe how this approach could be scaled up to detect trends in rice seasonality and intensity across all of rice growing Asia.