Remote sensing of croplands, crop productivity, and water use efficiency with a focus on smallholder systems in southern Africa

Abstract

The increasing availability of large geospatial datasets has allowed development of remote monitoring methods for broadacre crops. Ongoing deployment of new satellite missions means researchers have an increasing volume of data from which information on crop production can be derived. However, more accurate mapping of croplands, estimation of crop productivity, and monitoring of production trends are required. Accommodating the dynamic, seasonal nature of farming systems in mapping and productivity models, and production trend monitoring methods, may enhance their accuracy. Further, these methods must be adapted and applied to diverse farming systems such as smallholder irrigation schemes in southern Africa, and broadacre grain regions in Australia.

This thesis deals with several aspects of geospatial analysis of croplands including mapping by image classification, estimation of crop Gross Primary Productivity, and quantifying spatio-temporal trends in crop production and water use efficiency. Mapping methods were developed using known irrigation sites in Zimbabwe, while ground data constraints meant that well-studied global study sites were used to develop the Gross Primary Productivity and trend analysis methods. Finally, these methods were combined to analyse trends for study sites in Mozambique, Tanzania, and Zimbabwe. In each of these analyses, models which captured temporal dynamics of farming systems were the most accurate and useful.

Mapping of irrigated land use can be undertaken with classification of satellite images, though these methods may overlook small fields when applied with moderate to low resolution sensors. Smallholder irrigated areas were identified using composite images and high-dimensional statistics of temporal variation. This produced irrigated area maps with overall accuracy of 95.9% for test sites in Zimbabwe. The spectral median absolute deviation, as a measure of temporal variation on irrigated lands, was an important predictor of irrigated area. Incorporating within-season temporal variation into a machine learning prediction for light use efficiency also improved the accuracy of Gross Primary Productivity estimation for croplands. The root mean square error of Gross Primary Productivity estimates at cropland flux tower sites was reduced from 6.26gC/m2/day, using a stress-scalar approach, to 0.099gC/m2/day using the machine learning approach. Again, days after sowing, as a measure of within-season temporal variation, was an important predictor of light use efficiency.

Trends in crop productivity were estimated using generalised additive models accommodating spatiotemporal variation. This modelling approach captured the spatial and temporal dynamics of crop production and enables inferences to be drawn on inter-annual change at a range of spatial scales. For example, it was identified that cropland Normalised Difference Vegetation Index (NDVI) decreased at 2.5% per year from 2014 to 2021 in Madagascar. The interannual trends correlated with real grain production figures for the Western Australian wheatbelt (p = 0.03), confirming agreement with production.

The final chapter combined the light use efficiency and trend estimation methods to quantify decoupling of crop production from water consumption at some southern African irrigation schemes. For example, Gross Primary Productivity increased at 4.8% per year and evapotranspiration decreased at 3.6% per year at the Landela irrigation block in Zimbabwe. This finding shows that sustainable intensification of irrigated farming can result in more food being produced with less water. This thesis presents discrete but related applications of earth observation and remote sensing to agricultural systems research, with a focus on smallholder systems. Collectively, the chapters demonstrate that remote sensing methods for croplands are most accurate when they accommodate the dynamic nature of the agricultural system in time and space. Show more