ABSTRACT
The ability of soil to sequester carbon and reduce atmospheric CO2 concentrations is limited and depends on the soil minerals and their interaction with the microbiota. Microbial activities are closely associated with the types and amounts of soil organic matter (SOM) and clay minerals that have functional groups that interact with energy in Vis NIR-SWIR and Mid-IR wavelengths. The main objective of this research was to determine, based on these spectral ranges, the relation between mineralogical and organic compounds, as their sequestration and specialization in soils from Brazil. It was possible to map microbiological activity by spectral transfer functions and digital soil mapping reaching R2 from 0.77 to 0.85. Multiple regression equations were constructed to quantify enzymatic activity, microbial biomass carbon (MBC), particulate organic matter (POM), and resistant forms of carbon, and SOM associated with the mineral fraction (MAOM). All these properties were detected by specific bands obtained with the recursive feature elimination (RFE) algorithm, reaching correlations from 0.64 to 0.98 in specific ranges. The prediction model of the carbon sequestration potential was adjusted with microbiological and mineralogical variables from Vis-NIR-SWIR and the Mid-IR spectral range. A SARAR double autoregressive model was adjusted with r 0.61 and to a spatial error model (SEM) with r 0.7. The explanatory variables were associated with kaolinite, hematite, goethite, gibbsite, and the abundance of fungi, actinomycetes, vesico-arbuscular mycorrhizal fungi, enzymatic activity of beta-glucosidase, urease and phosphatase, and POM. Among the microbiological variables, the general abundance of fungi was the most important, in contrast to enzymatic activity that was the least important. The interaction between the different maps constructed and historical land use allowed the identification of areas that contribute to sequestering new carbon and could be the key to climate change mitigation strategies.
ABSTRACT
The Earth's surface dynamics provide essential information for guiding environmental and agricultural policies. Uncovered and unprotected surfaces experience several undesirable effects, which can affect soil ecosystem functions. We developed a technique to identify global bare surface areas and their dynamics based on multitemporal remote sensing images to aid the spatiotemporal evaluation of anthropic and natural phenomena. The bare Earth's surface and its changes were recognized by Landsat image processing over a time range of 30 years using the Google Earth Engine platform. Two additional products were obtained with a similar technique: a) Earth's bare surface frequency, which represents where and how many times a single pixel was detected as bare surface, based on Landsat series, and b) Earth's bare soil tendency, which represents the tendency of bare surface to increase or decrease. This technique enabled the retrieval of bare surfaces on 32% of Earth's total land area and on 95% of land when considering only agricultural areas. From a multitemporal perspective, the technique found a 2.8% increase in bare surfaces during the period on a global scale. However, the rate of soil exposure decreased by ~4.8% in the same period. The increase in bare surfaces shows that agricultural areas are increasing worldwide. The decreasing rate of soil exposure indicates that, unlike popular opinion, more soils have been covered due to the adoption of conservation agriculture practices, which may reduce soil degradation.
