The aerosol radiative effect is an important source of uncertainty in estimating the anthropogenic impact of global climate change. One of the main open questions is the role of radiation absorption by aerosols and its relation to land use worldwide, particularly in the Amazon Rainforest. Using AERONET (Aerosol Robotic Network) long-term measurements of aerosol optical depth (AOD) at a wavelength of 500 nm and absorption AOD (AAOD) at wavelengths of 440, 675, and 870 nm, we estimated the fraction and seasonality of the black carbon (BC) and brown carbon (BrC) contributions to absorption at 440 nm. This was conducted at six Amazonian sites, from central Amazon (Manaus and the Amazon Tall Tower Observatory—ATTO) to the deforestation arc (Rio Branco, Cuiabá, Ji-Paraná, and Alta Floresta). In addition, land use and cover data from the MapBiomas collection 6.0 was used to access the land transformation from forest to agricultural areas on each site. The results showed, for the first time, important geographical and seasonal variability in the aerosol optical properties, particularly the BC and BrC contributions. We observed a clear separation between dry and wet seasons, with BrC consistently accounting for an average of approximately 12% of the aerosol AAOD at 440 nm in the deforestation arc. In central Amazon, the contribution of BrC was approximately 25%. A direct relationship between the reduction in forests and the increase in the area dedicated to agriculture was detected. Moreover, places with lower fractions of forest had a smaller fraction of BrC, and regions with higher fractions of agricultural areas presented higher fractions of BC. Therefore, significant changes in AOD and AAOD are likely related to land-use transformations and biomass burning emissions, mainly during the dry season. The effects of land use change could introduce differences in the radiative balance in the different Amazonian regions. The analyses presented in this study allow a better understanding of the role of aerosol emissions from the Amazon Rainforest that could have global impacts.

The Brazilian savanna (Cerrado) has been heavily impacted by agricultural activities over the last four to five decades, and reliable estimates of reference evapotranspiration (ET_o) are needed for water resource management and irrigation agriculture. The Penman–Monteith (PM) is one of the most accepted models for ET_o estimation, but it requires many inputs that are not commonly available. Therefore, assessing the FAO guidelines to compute ET_o when meteorological data are missing could lead to a better understanding of which variables are critically important for reliable estimates of ET_o and how climatic variables are related to water requirements and atmospheric demands. In this study, ET_o was computed for a grass-dominated part of the Cerrado from April 2010 to August 2019. We tested 12 different scenarios considering radiation, relative humidity, and/or wind speed as missing climatic data using guidelines given by the FAO. Our results presented that wind speed and actual vapor pressure do not affect ET_o estimates as much as the other climatic variables; therefore, in the Cerrado’s conditions, wind speed and relative humidity measurements are less required than temperature and radiation data. When radiation data were missing, the computed ET_o was overestimated compared to the benchmark. FAO procedures to estimate the net radiation presented good results during the wet season; however, during the dry season, their results were overestimated because the method could not estimate negative R_n. Our results indicate that radiation data have the highest impact on ET_o for our study area and presumably for regions with similar climatic conditions. In addition, those FAO procedures for estimating radiation are not suitable when radiation data are missing.

The techniques and analyses employed by remote sensing provide key information about atmospheric particle properties at regional and global scales. However, limitations in optical spectral models used to represent the different types of aerosols in the atmosphere and their effects (direct and indirect) are still one of the major causes of sources of uncertainties and substantial impacts in climate prediction. There are no studies yet in South America, especially in the Amazon Basin, that have evaluated the advantages, disadvantages, inconsistencies, applicability, and suitability of the MODIS sensor (Moderate Resolution Imaging Spectroradiometer) destined for monitoring the ambient aerosol optical thickness over rivers and continents. In this study, the results of the DT (Dark Target) algorithm for products with 3 km and 10 km resolutions were systematically evaluated for six sites in the Amazon rainforest. The comparisons between the products were carried out with the AERONET (Aerosol Robotic Network) measurements, which were used as reference. Statistical parameters between AERONET vs. MODIS were also evaluated based on biomass burning records in the site regions. Here, the DT 10 km product showed satisfactory performance for the Amazon region, with observations between the expected error (EE) limits above 66%, in addition to R > 0.8 and RMSE < 0.3. However, the regional analysis for the two sites in the central and southern regions of the Amazon basin did not have the same performance, where the results showed an EE of 24 and 47%, respectively. The DT 3 km product did not perform well in any site, with an EE below 50%. Both products overestimated the AOD, but the 3 km product overestimated it approximately four times more due to its algorithm setup. Thus, we recommend the 10 km product for general analysis in Amazonia. Regional biomass burning records showed a direct relationship with the AERONET vs. MODIS DT with overestimation of both products. All variations between products and sites were justified based on the difficulty of retrieving surface reflectance and the model selected for local aerosols. Improvements in the optical spectral model currently implemented in the algorithms, with more realistic representations of the main types of the aerosol present in the Amazon Basin, may contribute to better performance among the evaluated products.

Carbon cycling in the Amazon fundamentally depends on the functioning of ecosystems and atmospheric dynamics, which are highly intricate. Few studies have hitherto investigated or measured the radiative effects of aerosols on the Amazon and Cerrado. This study examines the effects of atmospheric aerosols on solar radiation and their effects on net ecosystem exchange (NEE) in an area of semideciduous tropical forest in the north of Mato Grosso. Our results show that for a relative irradiance (f) 1.10–0.67, a decrease in incident solar radiation is associated with a reduction in the NEE. However, an average increase of 25 %–110 % in NEE was observed when pollution levels and aerosol optical depth (AOD) were above ≈ 1.25 and f < 0.5. The increase NEE was attributed to the increase of up to 60 % in the diffuse fraction of photosynthetically active radiation. The change in AOD and f was mainly attributable to biomass burning organic aerosols from fires. Important influences on vapor pressure deficit (VPD) as well as air temperature (T_air) and canopy (LC_T), induced by the interaction between solar radiation and high aerosol load in the observation area, were also noticed. On average, a cooling of about 3–4 ^∘C was observed for T_air and LC_T, and a decrease of up to 2–3 hPa was observed for VPD. Given the long-distance transport of aerosols emitted by burning biomass, significant changes in atmospheric optical properties and irradiance will impact the CO₂ flux of semideciduous forests distributed in the region.

Meteorological elements can affect the environment and cultures differently and may alter the natural development process contributing significantly to climate change. Meteorological variables of the Brazilian Pantanal were studied and used to determine evapotranspiration with fewer variables. It was found that artificial intelligence can substantially improve environmental modeling when alternative prediction techniques are used, resulting in lower project costs and more reliable results. This work tried to find the best combination by comparing machine learning techniques such as artificial neural networks, random forests, and support vector machines. A new model was created that depends on fewer climatic variables compared to the Penman–Monteith method (the standard method for estimating reference evapotranspiration) and can efficiently describe the reference evapotranspiration. Machine learning techniques are highly efficient for modeling environmental systems since they can process large amounts of data and find the best interactions between the parameters involved. In addition, more than 98% accuracy was obtained using fewer variables compared to the standard method when artificial neural networks are utilized.