Assessment of soil respiration patterns in an irrigated corn field based on spectral information acquired by field spectroscopy
Cicuéndez, Víctor | Rodríguez-Rastrero, Manuel | Huesca, Margarita | Uribe, Carla | Schmid, Thomas | Inclán, Rosa | Litago, Javier | Sánchez-Girón, Víctor | Merino-de-Miguel, Silvia | Palacios-Orueta, Alicia
The assessment of soil respiration processes in agroecosystems is essential to understand the C balance and to study the effects of soil respiration on climate change. The use of spectral data through remote sensing techniques constitutes a valuable tool to study ecological processes such as the C cycle dynamics. The objective of this work was to evaluate the potential to assess total (Rs) and autotrophic (Ra) soil respiration through spectral information acquired by field spectroscopy in a row irrigated corn crop (Zea mays L.) throughout the growing period. The relationships between Rs and Ra with leaf area index (LAI), spectral indexes and abiotic factors (soil moisture and soil temperature) were assessed by linear regression models using the adjusted coefficient of determination (Radj2) to measure and compare the proportion of variance explained by the models. Results showed significant differences and a high variability in the relationships between Rs and Ra with spectral indexes within the corn field during the phenological stages and in measurements under the plants and between the rows. Best results were obtained when assessing Ra during vegetative stages. However, during the reproductive stages, spectral indexes were better related to Rs which could be related to the presence of rhizomicrobial respiration linked to vegetation activity. Spectral indexes contain significant functional information, beyond mere structural changes, that could be related to carbon fluxes. However, specific models should be applied for the different phenological stages and there is a need to be cautious when upscaling remote sensing models. The results obtained confirm that in irrigated crop systems remote sensing data can produce relevant information to assess both Rs and Ra through spectral indexes.
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