Estimating the phenological dynamics of irrigated rice leaf area index using the combination of PROSAIL and Gaussian Process Regression

Oluseun Adeluyi,Angela Harris,Timothy Foster,Gareth D. Clay,Jochem Verrelst

doi:10.1016/j.jag.2021.102454

Abstract

The growth of rice is a sequence of three different phenological phases. This sequence of change in rice phenology implies that the condition of the plant during the vegetative phase relates directly to the health of leaves functioning during the reproductive and ripening phases. As such, accurate monitoring is important towards understanding rice growth dynamics. Leaf Area Index (LAI) is an important indicator of rice yields and the availability of this information during key phenological phases can support more informed farming decisions. Satellite remote sensing has been adopted as a proxy to field measurements of LAI and with the launch of freely available high resolution Satellite images such as Sentinel-2, it is imperative that accurate retrieval methods are adopted towards monitoring LAI at irrigated rice fields. Here, we evaluate the potential of a hybrid radiative transfer model (i.e., PROSAIL - Gaussian Process Regression (GPR), for estimating the phenological dynamics of irrigated rice LAI using imager derived from the Sentinel-2 multispectral instrument. LAI field measurements were obtained from an experimental rice field in Nasarawa state, Nigeria during the dry season. We used the PROSAIL radiative transfer model to create a look up table (LUT) that was subsequently used to train a GPR model. Afterwards, we evaluated the potential of the hybrid modelling approach by assessing the overall model accuracy and the extent to which LAI was able to accurately predict LAI during key rice phenological phases. We compared the predicted hybrid GPR LAI values with LAI values generated from the SNAP toolbox, based on a hybrid Artificial Neural Network (ANN) modelling approach. Our results show that the overall predictive accuracy of the hybrid GPR model (R2 = 0.82, RMSE = 1.65) was more accurate than that of the hybrid ANN model (R2 = 0.66, RMSE = 3.89) for retrieving LAI values from Sentinel-2 imagery. Both models underestimated LAI values during the reproductive and ripening phases . However, the accuracy during the phenological phases were more significant when using the hybrid GPR model (P < 0.05). During the different phenological phases, the hybrid GPR model predicted LAI more accurately during the reproductive (R2 = 0.7) and ripening (R2 = 0.59) phases compared to the hybrid ANN reproductive and ripening phases. When monitoring LAI phenological profiles of both hybrid models, the hybrid GPR and ANN models underestimated LAI during the reproductive and ripening phases. However, the ANN model underestimations were statistically significantly greater than those for the hybrid GPR model (P < 0.05). Our results highlight the potential of hybrid GPR models for estimating the phenological dynamics of irrigated rice LAI from Sentinel-2 data. They provided more accurate estimation of LAI patterns from varying nitrogen and water applications than hybrid ANN models.

Highlights

Grain crops are the main source of nutrition and food for populations around the world, with rice accounting for over 40% of consumption globally (Muthayya et al, 2014)
We evaluate the potential of a hybrid radiative transfer model (i.e., PROSAIL - Gaussian Process Regression (GPR), for estimating the phenological dynamics of irrigated rice leaf area index (LAI) using imager derived from the Sentinel-2 multispectral instrument
LAI values varied with nitrogen and water application rates with the highest LAI values (8.67) observed in plots with high amount (HN) and continuously flooded (CF) treatment and lowest values occurring in plots with low amount (LN) and AWSD treatment

Summary

Introduction

Grain crops are the main source of nutrition and food for populations around the world, with rice accounting for over 40% of consumption globally (Muthayya et al, 2014). A wide range of factors have been proposed as causes of the large rice yield gaps observed in Nigeria and the wider sub-Saharan Africa region, including inadequate use of, and access to, inputs (e.g. water, nutrients, pest and diseases) (Fahad et al, 2017; Wahid and Close, 2007), limited farm mechanization, and a lack of expertise amongst smallholder farmers about best agronomic management practices (Hengsdijk and Langeveld, 2009). Limited access to water for irrigation and fertilizers (including nitrogen), in particular, are key factors limiting productivity and resilience of rice production in sub-Saharan Africa and other smallholder farming regions globally (Ju et al, 2009; Wang et al, 2016). The launch of the European Space Agency’s Sentinel-2 satellites provide a suitable platform for timely monitoring of LAI during different phenological phases due to the high spatial, spectral and temporal resolution (Drusch et al, 2012)

Results

Discussion

Conclusion