Forest ecosystems sequester large amounts of atmospheric CO₂, and the contribution from seasonally dry tropical forests is not negligible. Thus, the objective of this study was to quantify and evaluate the seasonal and annual patterns of CO₂ exchanges in the Caatinga biome, as well as to evaluate the ecosystem condition as carbon sink or source during years. In addition, we analyzed the climatic factors that control the seasonal variability of gross primary production (GPP), ecosystem respiration (R_eco) and net ecosystem CO₂ exchange (NEE). Results showed that the dynamics of the components of the CO₂ fluxes varied depending on the magnitude and distribution of rainfall and, as a consequence, on the variability of the vegetation state. Annual cumulative NEE was significantly higher (p < 0.01) in 2014 (−169.0 g C m⁻²) when compared to 2015 (−145.0 g C m⁻²) and annual NEP/GPP ratio was 0.41 in 2014 and 0.43 in 2015. Global radiation, air and soil temperature were the main factors associated with the diurnal variability of carbon fluxes. Even during the dry season, the NEE was at equilibrium and the Caatinga acted as an atmospheric carbon sink during the years 2014 and 2015.

Many remote sensing‐based evapotranspiration (RSBET) algorithms have been proposed in the past decades and evaluated using flux tower data, mainly over North America and Europe. Model evaluation across South America has been done locally or using only a single algorithm at a time. Here, we provide the first evaluation of multiple RSBET models, at a daily scale, across a wide variety of biomes, climate zones, and land uses in South America. We used meteorological data from 25 flux towers to force four RSBET models: Priestley–Taylor Jet Propulsion Laboratory (PT‐JPL), Global Land Evaporation Amsterdam Model (GLEAM), Penman–Monteith Mu model (PM‐MOD), and Penman–Monteith Nagler model (PM‐VI). was predicted satisfactorily by all four models, with correlations consistently higher () for GLEAM and PT‐JPL, and PM‐MOD and PM‐VI presenting overall better responses in terms of percent bias (%). As for PM‐VI, this outcome is expected, given that the model requires calibration with local data. Model skill seems to be unrelated to land‐use but instead presented some dependency on biome and climate, with the models producing the best results for wet to moderately wet environments. Our findings show the suitability of individual models for a number of combinations of land cover types, biomes, and climates. At the same time, no model outperformed the others for all conditions, which emphasizes the need for adapting individual algorithms to take into account intrinsic characteristics of climates and ecosystems in South America.

The Caatinga dry forest encompasses 11% of the total continental territory of Brazil. Nevertheless, most research on the relationship between phenology and ecosystem productivity of Brazilian tropical forests is aimed at the Amazon basin. Thus, in this study we evaluated the seasonality of ecosystem productivity (gross primary production—GPP) in a preserved Caatinga environment in northeast Brazil. Analyses were carried out using eddy covariance measurements and satellite-derived data from sensor MODIS (MODerate Resolution Imaging Spectroradiometer, MOD17 and MOD13 products). In addition to GPP, we investigated water use efficiency (WUE) and meteorological and phenological aspects through remotely sensed vegetation indices (NDVI and EVI). We verified that ecosystem productivity is limited mainly by evapotranspiration, with maximum GPP values registered in the wetter months, indicating a strong dependency on water availability. NDVI and EVI were positively associated with GPP (r = 0.69 and 0.81, respectively), suggesting a coupling between the emergence of new leaves and the phenology of local photosynthetic capacity. WUE, on the other hand, was strongly controlled by consecutive dry days and not necessarily by total precipitation amount. The vegetation indices adequately described interannual variations of the forest response to environmental factors, and GPP MODIS presented a good relationship with tower-measured GPP in dry (R² = 0.76) and wet (R² = 0.62) periods.