First Insights on Soil Respiration Prediction across the Growth Stages of Rainfed Barley Based on Simulated MODIS and Sentinel-2 Spectral Indices

Víctor Cicuéndez,Rosa Inclán,Víctor Sánchez-Girón,Javier Litago,Alicia Palacios-Orueta,Laura Recuero,Thomas Schmid,Margarita Huesca,Manuel Rodríguez-Rastrero

doi:10.3390/rs12172724

Abstract

Rainfed agriculture occupies the majority of the world’s agricultural surface and is expected to increase in the near future causing serious effects on carbon cycle dynamics in the context of climate change. Carbon cycle across several temporal and spatial scales could be studied through spectral indices because they are related to vegetation structure and functioning and hence with carbon fluxes, among them soil respiration (Rs). The aim of this work was to assess Rs linked to crop phenology of a rainfed barley crop throughout two seasons based on spectral indices calculated from field spectroscopy data. The relationships between Rs, Leaf Area Index (LAI) and spectral indices were assessed by linear regression models with the adjusted coefficient of determination (Radj2). Results showed that most of the spectral indices provided better information than LAI throughout the studied period and that soil moisture and temperature were relevant variables in specific periods. During vegetative stages, indices based on the visible (VIS) region showed the best relationship with Rs. On the other hand, during reproductive stages indices containing the near infrared-shortwave infrared (NIR-SWIR) spectral region and those related to water content showed the highest relationship. The inter-annual variability found in Mediterranean regions was also observed in the estimated ratio of carbon emission to carbon fixation between years. Our results show the potential capability of spectral information to assess soil respiration linked to crop phenology across several temporal and spatial scales. These results can be used as a basis for the utilization of other remote information derived from satellites or airborne sensors to monitor crop carbon balances.

Highlights

Agricultural areas represent 11% of the global land surface [1] generating around 13% of the greenhouse gases emissions and are expected to increase in the near future [2,3]
In our experiment we found high NDWI, SASI and NDWI2 Radj2 values with both Leaf Area Index (LAI) (Tables 4 and 5) and Rs, indicating that the capability of these indices to capture variability in vegetation structure and moisture could be the basis in using them for assessing Rs
This study showed that spectral indices improve the information provided by LAI to assess soil respiration in a rainfed barley crop

Summary

Introduction

Agricultural areas represent 11% of the global land surface [1] generating around 13% of the greenhouse gases emissions and are expected to increase in the near future [2,3]. Soil autotrophic (Ra) and heterotrophic respiration (Rh) are highly dependent on biotic and abiotic factors. Heterotrophic respiration coming from the organic matter mineralization by the decomposer community is highly affected by the availability of soil carbon, moisture and temperature [8,9,10]. The accumulation of carbon exudates in roots as well as variation in canopy transpiration and shading through the phenological cycle creates local soil conditions modifying heterotrophic respiration processes [19]. In agricultural fields all these factors result in a high CO2 fluxes spatial and temporal variability [20,21] which may be stronger in rainfed crops in Mediterranean climates due to the irregular distribution of rainfall and temperature [22,23]

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