Gustavo M. Vasques

Spectroscopic models of soil organic carbon in Florida, USA.

Soil organic carbon (SOC) is an indicator of ecosystem quality and plays a major role in the biogeochemical cycles of major nutrients and water. Shortcomings exist to estimate SOC across large regions using rapid and cheap soil sensing approaches. Our objective was to estimate SOC in 7120 mineral and organic soil horizons in Florida using visible/near-infrared diffuse reflectance spectroscopy (VNIRS) calibrated by committee trees and partial least squares regression (PLSR). The derived VNIRS models were validated using independent datasets and explained up to 71 and 38% of the variance of SOC in mineral and organic horizons, respectively. We stratified the mineral horizons into seven soil orders and derived PLSR models for each order, which explained from 32% (Histosols) to 75% (Ultisols) of the variance of SOC concentration in validation mode. Estimates of SOC from all models were highly scattered along the regression lines, especially for high SOC values, and the slopes of the regression lines were generally <1 because VNIRS models tended to underestimate high SOC values and overestimate low SOC. Despite the great scatter of estimates in the prediction plots, VNIRS models had reasonable explanatory power for mineral horizons, given the heterogeneity of soils and environmental conditions in Florida, and have potential for the rapid assessment of SOC, with implications for regional SOC assessments, modeling, and monitoring. However, VNIRS models for organic horizons were hampered by small sample size and had very limited explanatory power.

Influence of the spatial extent and resolution of input data on soil carbon models in Florida, USA

(R 2 from 0.10 in the cattle station to 0.61 in Florida) and with a decrease in the resolution of input data (R 2 from 0.33 at 1920-m resolution to 0.61 at 30-m resolution in Florida). Soil and hydrologic variables were the most important across the seven resolutions both in Florida and in the watershed. The spatial extent and resolution of environmental covariates modulate soil C variation and soil-landscape correlations influencing soil C predictive models. Our results provide scale boundaries to observe environmental data and assess soil C spatial patterns, supporting C sequestration, budgeting and monitoring programs.

Prediction of Soil Physical and Chemical Properties by Visible and Near-Infrared Diffuse Reflectance Spectroscopy in the Central Amazon

Visible and near-infrared diffuse reflectance spectroscopy (VIS-NIR) has shown levels of accuracy comparable to conventional laboratory methods for estimating soil properties. Soil chemical and physical properties have been predicted by reflectance spectroscopy successfully on subtropical and temperate soils, whereas soils from tropical agro-forest regions have received less attention, especially those from tropical rainforests. A spectral characterization provides a proficient pathway for soil characterization. The first step in this process is to develop a comprehensive VIS-NIR soil library of multiple key soil properties to be used in future soil surveys. This paper presents the first VIS-NIR soil library for a remote region in the Central Amazon. We evaluated the performance of VIS-NIR for the prediction of soil properties in the Central Amazon, Brazil. Soil properties measured and predicted were: pH, Ca, Mg, Al, H, H+Al, P, organic C (SOC), sum of bases, cation exchange capacity (CEC), percentage of base saturation (V), Al saturation (m), clay, sand, silt, silt/clay (S/C), and degree of flocculation. Soil samples were scanned in the laboratory in the VIS-NIR range (350–2500 nm), and forty-one pre-processing methods were tested to improve predictions. Clay content was predicted with the highest accuracy, followed by SOC. Sand, S/C, H, Al, H+Al, CEC, m and V predictions were reasonably good. The other soil properties were poorly predicted. Among the soil properties predicted well, SOC is one of the critical soil indicators in the global carbon cycle. Besides the soil property of interest, the landscape position, soil order and depth influenced in the model performance. For silt content, pH and S/C, the model performed better in well-drained soils, whereas for SOC best predictions were obtained in poorly drained soils. The association of VIS-NIR spectral data to landforms, vegetation classes, and soil types demonstrate potential for soil characterization.

Fotopedologia, espectroscopia e sistema de informação geográfica na caracterização de solos desenvolvidos do Grupo Barreiras no Amapá

This study applied remote sensing and geographic information system tools, along with soil spectral data and quantitative and qualitative relief information, to characterize and separate soil classes developed over the Barreiras Group in the region of Porto Grande, state of Amapa, Brazil. After mapping soils at the semi-detailed level, soil samples from individual profiles were characterized using visible and infrared (400-2500 nm) spectroscopy, and the drainage system and relief were analyzed spatially based on aerial photographs and radar imagery. Quantitative relief information was more efficient than qualitative ones to characterize and separate soils, whereas the soil spectral data allowed the characterization of soil profiles individu - ally. The spatial assessment of the drainage system and relief, and the spectral assessment of individual soil profiles are complementary to characterize and separate soils on the landscape.

Soil Carbon Stock and Particle Size Fractions in the Central Amazon Predicted from Remotely Sensed Relief, Multispectral and Radar Data

Soils from the remote areas of the Amazon Rainforest in Brazil are poorly mapped due to the presence of dense forest and lack of access routes. The use of covariates derived from multispectral and radar remote sensors allows mapping large areas and has the potential to improve the accuracy of soil attribute maps. The objectives of this study were to: (a) evaluate the addition of relief, and vegetation covariates derived from multispectral images with distinct spatial and spectral resolutions (Landsat 8 and RapidEye) and L-band radar (ALOS PALSAR) for the prediction of soil organic carbon stock (CS) and particle size fractions; and (b) evaluate the performance of four geostatistical methods to map these soil properties. Overall, the results show that, even under forest coverage, the Normalized Difference Vegetation Index (NDVI) and ALOS PALSAR backscattering coefficient improved the accuracy of CS and subsurface clay content predictions. The NDVI derived from RapidEye sensor improved the prediction of CS using isotopic cokriging, while the NDVI derived from Landsat 8 and backscattering coefficient were selected to predict clay content at the subsurface using regression kriging (RK). The relative improvement of applying cokriging and RK over ordinary kriging were lower than 10%, indicating that further analyses are necessary to connect soil proxies (vegetation and relief types) with soil attributes.