The Green Revolution from space: Mapping the historic dynamics of main rice types in one of the world's food bowls

This paper develops a methodology to map the two main rice types in northwest Bangladesh from 1989 to 2016, when Green Revolution technologies and policies resulted in a 300% rice area expansion and localised unsustainable groundwater use. The mapping is performed for the largely irrigated dry season Boro rice (grown from November/December to May/June), and the largely rainfed Aman rice (grown from June/July to October/ November). The petabyte archive of Landsat reflectance data available via Google Earth Engine is used to extract monthly time-series of two remotely sensed vegetation indices - the Enhanced Vegetation Index (EVI) and the Global Vegetation Moisture Index (GVMI) - which can capture crop phenological dynamics as they correlate during crop growth stages and can be used to discriminate standing water when EVI is low and GVMI is high. This feature is exploited to detect the effects of flooding before transplanting Boro rice in January and to capture the late phenological phases of Aman rice through harvesting months (October to November). A gap-filled and smoothed EVI and GVMI monthly time-series sequences for both rice cropping months are grouped using machine learning unsupervised K-means clustering into clusters (25 used here), and manually aggregated using crop calendars and local expert knowledge into rice types, other vegetated areas, water, non-permanent water and bare areas. A representative subset of these data are then used to construct a training sample using additional covariates besides the monthly EVI and GVMI values and train a Random Forest (RF) model capable of predicting the land cover types in other years. The resulting RF maps for the two cropping seasons were evaluated against publicly available survey crop statistics (from 1989 to 2016) and published Boro 30 m resolution maps for 2013 to 2016. The maps provide qualitative evidence that unsustainable groundwater extractions can be linked to Boro rice irrigation in some parts of northwest Bangladesh. Further research, using these maps and other hydrological data, is required to quantitatively assess when, where, and how much irrigation contributes to unsustainable groundwater use. The results show that the methodology can accurately (generally within 20% absolute error) capture Boro and Aman rice over the Green Revolution period, and other landscape dynamic characteristics of importance for environmental assessments, such as areas of standing water.