Evaluation of Water and Carbon Estimation Models in the Caatinga Biome Based on Remote Sensing

Abstract

The study of energy, water, and carbon exchanges between ecosystems and the atmosphere is important in understanding the role of vegetation in regional microclimates. However, they are still relatively scarce when it comes to Caatinga vegetation. This study aims to identify differences in the dynamics of critical environmental variables such as net radiation (Rn), evapotranspiration (ET), and carbon fluxes (gross primary production, GPP) in contrasting recovered Caatinga (dense Caatinga, DC) and degraded Caatinga (sparse Caatinga, SC) in the state of Paraíba, northeastern Brazil. Estimates were performed using the Surface Energy Balance Algorithm for Land (SEBAL), and comparisons between estimated and measured data were conducted based on the coefficient of determination (R2). The fluxes were measured using the Eddy Covariance (EC) method for comparison with the same variables derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data aboard the Terra satellite. The estimates showed higher Rn values for the DC, indicating that this area should have greater energy availability for physical, biological, and chemical processes. The R2 between daily Rn estimates and observations was 0.93. The ET estimated using the SEBAL showed higher differences in relation to the observed values; however, it presented better spatial discrimination of the surface features. The MOD16A2 algorithm, however, presented ET values closer to the observed data and agreed with the seasonality of the Enhanced Vegetation Index (EVI). The DC generally showed higher ET values than the SC, while the MODIS data (GPP MOD17A2H) presented a temporal behavior closer to the observations. The difference between the two areas was more evident in the rainy season. The R2 values between GPP and GPP MOD17A2H were 0.76 and 0.65 for DC and SC, respectively. In addition, the R2 values for GPP Observed and GPP modeled were lower, i.e., 0.28 and 0.12 for the DC and SC, respectively. The capture of CO2 is more evident for the DC considering the whole year, with the SC showing a notable increase in CO2 absorption only in the rainy season. The GPP estimated from the MOD17A2H showed a predominant underestimation but evidenced the effects of land use and land cover changes over the two areas for all seasons.

Accuracy of Estimated Crop Evapotranspiration Using Locally Developed Crop Coefficients against Satellite-Derived Crop Evapotranspiration in a Semiarid Climate

“…The results gave significantly different evapotranspiration estimates compared to the local kc evapotranspiration estimated for 4 years over 6 (Table 5). These results agree with those of de Oliveira et al [60], who found that evapotranspiration estimated using the SEBAL showed higher differences in relation to the observed values. In contrast, Kaysert et al [61] reported that daily estimates of geeSEBAL yielded an average RMSD of 0.91 mm/day when compared to eddy covariance system data, while Gonçalves et al [62] indicated that geeSEBAL has significant potential for use in the assessment of crop evapotranspiration for irrigation monitoring and management in Brazil, even in areas with missing climate data.…”

Carbon Biomass Estimation Using Vegetation Indices in Agriculture–Pasture Mosaics in the Brazilian Caatinga Dry Tropical Forest

“…Other studies showed that semiarid regions present representative values of AGB, as in the case of agriculture-pasture mosaics [7][8][9]. Monitoring the AGB variation in agriculture-pasture mosaics may indicate changes in vegetation cover, loss of biodiversity, or soil degradation.…”

“…Forests and family farming contribute to carbon sequestration, while mechanized agriculture can lead to greenhouse gas emissions [1][2][3]. While the mapping of carbon storage and sequestration has been extensively studied in forest regions [4][5][6], there is a lack of research focusing on semiarid areas [7][8][9] and on estimating carbon in different types of vegetation, including regions with agricultural activities [10].…”

Interannual Variability of Energy and CO2 Exchanges in a Remnant Area of the Caatinga Biome under Extreme Rainfall Conditions

“…Typically, studies with larger spatial and/or temporal scales are conducted using remote sensing data [4,18,24,25] or via modeling [2,[26][27][28][29][30]. Studies using soil-vegetationatmosphere transfer (SVATS) models are a viable alternative for determining and evaluating energy and CO 2 fluxes in various ecosystems.…”