# Machine learning estimates of eddy covariance carbon flux in a scrub in the Mexican highland

Arid and semiarid ecosystems contain relatively high species diversity and are subject to intense use, in particular extensive cattle grazing, which has favored the expansion and encroachment of perennial thorny shrubs into the grasslands, thus decreasing the value of the rangeland. However, these environments have been shown to positively impact global carbon dynamics. Machine learning and remote sensing have enhanced our knowledge about carbon dynamics, but they need to be further developed and adapted to particular analysis. We measured the net ecosystem exchange (NEE) of C with the eddy covariance (EC) method and estimated gross primary production (GPP) in a thorny scrub at Bernal in Mexico. We tested the agreement between EC estimates and remotely sensed GPP estimates from the Moderate Resolution Imaging Spectroradiometer (MODIS), and also with two alternative modeling methods: ordinary-least-squares (OLS) regression and ensembles of machine learning algorithms (EMLs). The variables used as predictors were MODIS spectral bands, vegetation indices and products, and gridded environmental variables. The Bernal site was a carbon sink even though it was overgrazed, the average NEE during 15 months of 2017 and 2018 was inline-formula−0.78 inline-formula $M2inlinescrollmathmlunit\mathrm{normal g}\phantom{\rule{0ex}{0ex}}\mathrm{normal C}\phantom{\rule{0ex}{0ex}}{\mathrm{normal m}}^{-normal 2}\phantom{\rule{0ex}{0ex}}{\mathrm{normal d}}^{-normal 1}$ 56pt15ptsvg-formulamathimg15ac761ab043ccc04915a8227df2339e bg-18-367-2021-ie00001.svg56pt15ptbg-18-367-2021-ie00001.png , and the flux was negative or neutral during the measured months. The probability of agreement (inline-formulaθs) represented the agreement between observed and estimated values of GPP across the range of measurement. According to the mean value of inline-formulaθs, agreement was higher for the EML (0.6) followed by OLS (0.5) and then MODIS (0.24). This graphic metric was more informative than inline-formular2 (0.98, 0.67, 0.58, respectively) to evaluate the model performance. This was particularly true for MODIS because the maximum inline-formulaθs of 4.3 was for measurements of 0.8 inline-formula $M7inlinescrollmathmlunit\mathrm{normal g}\phantom{\rule{0ex}{0ex}}\mathrm{normal C}\phantom{\rule{0ex}{0ex}}{\mathrm{normal m}}^{-normal 2}\phantom{\rule{0ex}{0ex}}{\mathrm{normal d}}^{-normal 1}$ 56pt15ptsvg-formulamathimg1c7ec7db4a4b0be66e19c59f4e1acc18 bg-18-367-2021-ie00002.svg56pt15ptbg-18-367-2021-ie00002.png and then decreased steadily below 1 inline-formulaθs for measurements above 6.5 inline-formula $M9inlinescrollmathmlunit\mathrm{normal g}\phantom{\rule{0ex}{0ex}}\mathrm{normal C}\phantom{\rule{0ex}{0ex}}{\mathrm{normal m}}^{-normal 2}\phantom{\rule{0ex}{0ex}}{\mathrm{normal d}}^{-normal 1}$ 56pt15ptsvg-formulamathimgb8e1ecc86fefb11b9db1227eb7813bb1 bg-18-367-2021-ie00003.svg56pt15ptbg-18-367-2021-ie00003.png for this scrub vegetation. In the case of EML and OLS, the inline-formulaθs was stable across the range of measurement. We used an EML for the Ameriflux site US-SRM, which is similar in vegetation and climate, to predict GPP at Bernal, but inline-formulaθs was low (0.16), indicating the local specificity of this model. Although cacti were an important component of the vegetation, the nighttime flux was characterized by positive NEE, suggesting that the photosynthetic dark-cycle flux of cacti was lower than ecosystem respiration. The discrepancy between MODIS and EC GPP estimates stresses the need to understand the limitations of both methods.

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Guevara-Escobar, Aurelio / González-Sosa, Enrique / Cervantes-Jiménez, Mónica / et al: Machine learning estimates of eddy covariance carbon flux in a scrub in the Mexican highland. 2021. Copernicus Publications.

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