Predicting soil erosion using Rusle and NDVI time series from TM Landsat 5

The objective of this work was to evaluate the seasonal variation of soil cover and rainfall erosivity, and their influences on the revised universal soil loss equation (Rusle), in order to estimate watershed soil losses in a temporal scale. Twenty-two TM Landsat 5 images from 1986 to 2009 were used to estimate soil use and management factor (C factor). A corresponding rainfall erosivity factor (R factor) was considered for each image, and the other factors were obtained using the standard Rusle method. Estimated soil losses were grouped into classes and ranged from 0.13 Mg ha-1 on May 24, 2009 (dry season) to 62.0 Mg ha-1 on March 11, 2007 (rainy season). In these dates, maximum losses in the watershed were 2.2 and 781.5 Mg ha-1 , respectively. Mean annual soil loss in the watershed was 109.5 Mg ha-1 , but the central area, with a loss of nearly 300.0 Mg ha-1 , was characterized as a site of high water-erosion risk. The use of C factor obtained from remote sensing data, associated to corresponding R factor, was fundamental to evaluate the soil erosion estimated by the Rusle in different seasons, unlike of other studies which keep these factors constant throughout time.

A simple model for predicting soil temperature in snow-covered and seasonally frozen soil: model description and testing

Microbial processes in soil are moisture, nutrient and temperature dependent and, consequently, accurate calculation of soil temperature is important for modelling nitrogen processes. Microbial activity in soil occurs even at sub-zero temperatures so that, in northern latitudes, a method to calculate soil temperature under snow cover and in frozen soils is required. This paper describes a new and simple model to calculate daily values for soil temperature at various depths in both frozen and unfrozen soils. The model requires four parameters average soil thermal conductivity, specific beat capacity of soil, specific heat capacity due to freezing and thawing and an empirical snow parameter. Precipitation, air temperature and snow depth (measured or calculated) are needed as input variables. The proposed model was applied to five sites in different parts of Finland representing different climates and soil types. Observed soil temperatures at depths of 20 and 50 cm (September 1981-August 1990) were used for model calibration. The calibrated model was then tested using observed soil temperatures from September 1990 to August 2001. R-2-values of the calibration period varied between 0.87 and 0.96 at a depth of 20 cm and between 0.78 and 0.97 at 50 cm. R-2 -values of the testing period were between 0.87 and 0.94 at a depth of 20cm. and between 0.80 and 0.98 at 50cm. Thus, despite the simplifications made, the model was able to simulate soil temperature at these study sites. This simple model simulates soil temperature well in the uppermost soil layers where most of the nitrogen processes occur. The small number of parameters required means, that the model is suitable for addition to catchment scale models.

Visible near infra-red (VisNIR) spectroscopy for predicting soil organic carbon in Ethiopia

Over the past few decades, the advantages of the visible-near infra-red (VisNIR) diffuse reflectance spectrometer (DRS) method have enabled prediction of soil organic carbon (SOC). In this study, SOC was predicted using regression models for samples taken from three sites (Gununo, Maybar and Anjeni) in Ethiopia. SOC was characterized in laboratory using conventional wet chemistry and VisNIR-DRS methods. Principal component analysis (PCA), principal component regression (PCR) and partial least square regression (PLS) models were developed using Unscrambler X 10.2. PCA results show that the first two components accounted for a minimum of 96% variation which increased for individual sites and with data treatments. Correlation (r), coefficient of determination (R2) and residual prediction deviation (RPD) were used to rate four models built. PLS model (r, R2, RPD) values for Anjeni were 0.9, 0.9 and 3.6; for Gununo values 0.6, 0.3 and 1.2; for Maybar values 0.6, 0.3 and 0.9, and for the three sites values 0.7, 0.6 and 1.5, respectively. PCR model values (r, R2, RPD) for Anjeni were 0.9, 0.8 and 2.7; for Gununo values 0.5, 0.3 and 1; for Maybar values 0.5, 0.1 and 0.7, and for the three sites values 0.7, 0.5 and 1.2, respectively. Comparison and testing of models shows superior performance of PLS to PCR. Models were rated as very poor (Maybar), poor (Gununo and three sites) and excellent (Anjeni). A robust model, Anjeni, is recommended for prediction of SOC in Ethiopia.

Near infrared spectroscopy for soil bulk density assessment

P>Soil bulk density values are needed to convert organic carbon content to mass of organic carbon per unit area. However, field sampling and measurement of soil bulk density are labour-intensive, costly and tedious. Near-infrared reflectance spectroscopy (NIRS) is a physically non-destructive, rapid, reproducible and low-cost method that characterizes materials according to their reflectance in the near-infrared spectral region. The aim of this paper was to investigate the ability of NIRS to predict soil bulk density and to compare its performance with published pedotransfer functions. The study was carried out on a dataset of 1184 soil samples originating from a reforestation area in the Brazilian Amazon basin, and conventional soil bulk density values were obtained with metallic ""core cylinders"". The results indicate that the modified partial least squares regression used on spectral data is an alternative method for soil bulk density predictions to the published pedotransfer functions tested in this study. The NIRS method presented the closest-to-zero accuracy error (-0.002 g cm-3) and the lowest prediction error (0.13 g cm-3) and the coefficient of variation of the validation sets ranged from 8.1 to 8.9% of the mean reference values. Nevertheless, further research is required to assess the limits and specificities of the NIRS method, but it may have advantages for soil bulk density predictions, especially in environments such as the Amazon forest.

Uncertainties in the prediction of spatial variability of soil CO2 emissions and related properties

The soil CO2 emission has high spatial variability because it depends strongly on soil properties. The purpose of this study was to (i) characterize the spatial variability of soil respiration and related properties, (ii) evaluate the accuracy of results of the ordinary kriging method and sequential Gaussian simulation, and (iii) evaluate the uncertainty in predicting the spatial variability of soil CO2 emission and other properties using sequential Gaussian simulations. The study was conducted in a sugarcane area, using a regular sampling grid with 141 points, where soil CO2 emission, soil temperature, air-filled pore space, soil organic matter and soil bulk density were evaluated. All variables showed spatial dependence structure. The soil CO2 emission was positively correlated with organic matter (r = 0.25, p < 0.05) and air-filled pore space (r = 0.27, p < 0.01) and negatively with soil bulk density (r = -0.41, p < 0.01). However, when the estimated spatial values were considered, the air-filled pore space was the variable mainly responsible for the spatial characteristics of soil respiration, with a correlation of 0.26 (p < 0.01). For all variables, individual simulations represented the cumulative distribution functions and variograms better than ordinary kriging and E-type estimates. The greatest uncertainties in predicting soil CO2 emission were associated with areas with the highest estimated values, which produced estimates from 0.18 to 1.85 t CO2 ha-1, according to the different scenarios considered. The knowledge of the uncertainties generated by the different scenarios can be used in inventories of greenhouse gases, to provide conservative estimates of the potential emission of these gases.

Revisiting the S-index for soil physical quality and its use in Brazil

The S-index was introduced in 2004 in a publication by A.R. Dexter. S was proposed as an indicator of soil physical quality. A critical value delimiting soils with rich and poor physical quality was proposed. At present, Brazil is world leader in citations of Dexter's publication. In this publication the S-theory is mathematically revisited and extended. It is shown that S is mathematically correlated to bulk density and total porosity. As an absolute indicator, the value of S alone has proven to be incapable of predicting soil physical quality. The critical value does not always hold under boundary conditions described in the literature. This is to be expected because S is a static parameter, therefore implicitly unable to describe dynamic processes. As a relative indicator of soil physical quality, the S-index has no additional value over bulk density or total porosity. Therefore, in the opinion of the author, the fact that bulk density or total porosity are much more easily determined than the water retention curve for obtaining S disqualifies S as an advantageous indicator of relative soil physical quality. Among the several equations available for the fitting of water retention curves, the Groenevelt-Grant equation is preferable for use with S since one of its parameters and S are linearly correlated. Since efforts in soil physics research have the purpose of describing dynamic processes, it is the author's opinion that these efforts should shift towards mechanistic soil physics as opposed to the search for empirical correlations like S which, at present, represents far more than its reasonable share of soil physics in Brazil.

PEDO-TRANSFER FUNCTIONS FOR ESTIMATING SOIL BULK DENSITY IN CENTRAL AMAZONIA

Under field conditions in the Amazon forest, soil bulk density is difficult to measure. Rigorous methodological criteria must be applied to obtain reliable inventories of C stocks and soil nutrients, making this process expensive and sometimes unfeasible. This study aimed to generate models to estimate soil bulk density based on parameters that can be easily and reliably measured in the field and that are available in many soil-related inventories. Stepwise regression models to predict bulk density were developed using data on soil C content, clay content and pH in water from 140 permanent plots in terra firme (upland) forests near Manaus, Amazonas State, Brazil. The model results were interpreted according to the coefficient of determination (R2) and Akaike information criterion (AIC) and were validated with a dataset consisting of 125 plots different from those used to generate the models. The model with best performance in estimating soil bulk density under the conditions of this study included clay content and pH in water as independent variables and had R2 = 0.73 and AIC = -250.29. The performance of this model for predicting soil density was compared with that of models from the literature. The results showed that the locally calibrated equation was the most accurate for estimating soil bulk density for upland forests in the Manaus region.

Modelled soil organic carbon stocks and changes in the Indo-Gangetic Plains, India from 1980 to 2030

The Global Environment Facility co-financed Soil Organic Carbon (GEFSOC) Project developed a comprehensive modelling system for predicting soil organic carbon (SOC) stocks and changes over time. This research is an effort to predict SOC stocks and changes for the Indian, Indo-Gangetic Plains (IGP), an area with a predominantly rice (Oryza sativa) - wheat (Triticum aestivum) cropping system, using the GEFSOC Modelling System and to compare output with stocks generated using mapping approaches based on soil survey data. The GEFSOC Modelling System predicts an estimated SOC stock for the IGP, India of 1.27, 1.32 and 1.27 Pg for 1990, 2000 and 2030, respectively, in the top 20 cm of soil. The SOC stock using a mapping approach based on soil survey data was 0.66 and 0.88 Pg for 1980 and 2000, respectively. The SOC stock estimated using the GEFSOC Modelling System is higher than the stock estimated using the mapping approach. This is due to the fact that while the GEFSOC System accounts for variation in crop input data (crop management), the soil mapping approach only considers regional variation in soil texture and wetness. The trend of overall change in the modelled SOC stock estimates shows that the IGP, India may have reached an equilibrium following 30-40 years of the Green Revolution. This can be seen in the SOC stock change rates. Various different estimation methods show SOC stocks of 0.57-1.44 Pg C for the study area. The trend of overall change in C stock assessed from the soil survey data indicates that the soils of the IGP, India may store a projected 1.1 Pg of C in 2030. (C) 2007 Elsevier B.V. All rights reserved.

Uncertainties in the prediction of spatial variability of soil CO2 emissions and related properties

A emissão de CO2 do solo apresenta alta variabilidade espacial, devido à grande dependência espacial observada nas propriedades do solo que a influenciam. Neste estudo, objetivou-se: caracterizar e relacionar a variabilidade espacial da respiração do solo e propriedades relacionadas; avaliar a acurácia dos resultados fornecidos pelo método da krigagem ordinária e simulação sequencial gaussiana; e avaliar a incerteza na predição da variabilidade espacial da emissão de CO2 do solo e demais propriedades utilizando a simulação sequencial gaussiana. O estudo foi conduzido em uma malha amostral irregular com 141 pontos, instalada sobre a cultura de cana-de-açúcar. Nesses pontos foram avaliados a emissão de CO2 do solo, a temperatura do solo, a porosidade livre de água, o teor de matéria orgânica e a densidade do solo. Todas as variáveis apresentaram estrutura de dependência espacial. A emissão de CO2 do solo mostrou correlações positivas com a matéria orgânica (r = 0,25, p < 0,05) e a porosidade livre de água (r = 0,27, p <0,01) e negativa com a densidade do solo (r = -0,41, p < 0,01). No entanto, quando os valores estimados espacialmente (N=8833) são considerados, a porosidade livre de água passa a ser a principal variável responsável pelas características espaciais da respiração do solo, apresentando correlação de 0,26 (p < 0,01). As simulações individuais propiciaram, para todas as variáveis analisadas, melhor reprodução das funções de distribuição acumuladas e dos variogramas, em comparação à krigagem e estimativa E-type. As maiores incertezas na predição da emissão de CO2 estiveram associadas às regiões da área estudada com maiores valores observados e estimados, produzindo estimativas, ao longo do período estudado, de 0,18 a 1,85 t CO2 ha-1, dependendo dos diferentes cenários simulados. O conhecimento das incertezas gerado por meio dos diferentes cenários de estimativa pode ser incluído em inventários de gases do efeito estufa, resultando em estimativas mais conservadoras do potencial de emissão desses gases.

Prediction of soil organic carbon for Ethiopian highlands using soil spectroscopy

Soil spectroscopy was applied for predicting soil organic carbon (SOC) in the highlands of Ethiopia. Soil samples were acquired from Ethiopia’s National Soil Testing Centre and direct field sampling. The reflectance of samples was measured using a FieldSpec 3 diffuse reflectance spectrometer. Outliers and sample relation were evaluated using principal component analysis (PCA) and models were developed through partial least square regression (PLSR). For nine watersheds sampled, 20% of the samples were set aside to test prediction and 80% were used to develop calibration models. Depending on the number of samples per watershed, cross validation or independent validation were used.The stability of models was evaluated using coefficient of determination (R2), root mean square error (RMSE), and the ratio performance deviation (RPD). The R2 (%), RMSE (%), and RPD, respectively, for validation were Anjeni (88, 0.44, 3.05), Bale (86, 0.52, 2.7), Basketo (89, 0.57, 3.0), Benishangul (91, 0.30, 3.4), Kersa (82, 0.44, 2.4), Kola tembien (75, 0.44, 1.9),Maybar (84. 0.57, 2.5),Megech (85, 0.15, 2.6), andWondoGenet (86, 0.52, 2.7) indicating that themodels were stable. Models performed better for areas with high SOC values than areas with lower SOC values. Overall, soil spectroscopy performance ranged from very good to good.

Cadmium availability and accumulation by lettuce and rice

Among the toxic elements, Cd has received considerable attention in view of its association with a number of human health problems. The objectives of this study were to evaluate the Cd availability and accumulation in soil, transfer rate and toxicity in lettuce and rice plants grown in a Cd-contaminated Typic Hapludox. Two simultaneous greenhouse experiments with lettuce and rice test plants were conducted in a randomized complete block design with four replications. The treatments consisted of four Cd rates (CdCl2), 0.0; 1.3; 3.0 and 6.0 mg kg-1, based on the guidelines recommended by the Environmental Agency of the State of São Paulo, Brazil (Cetesb). Higher Cd rates increased extractable Cd (using Mehlich-3, Mehlich-1 and DTPA chemical extractants) and decreased lettuce and rice dry matter yields. However, no visual toxicity symptoms were observed in plants. Mehlich-1, Mehlich-3 and DTPA extractants were effective in predicting soil Cd availability as well as the Cd concentration and accumulation in plant parts. Cadmium concentration in rice remained below the threshold for human consumption established by Brazilian legislation. On the other hand, lettuce Cd concentration in edible parts exceeded the acceptable limit.

Cadmium availability and accumulation by lettuce and rice

Among the toxic elements, Cd has received considerable attention in view of its association with a number of human health problems. The objectives of this study were to evaluate the Cd availability and accumulation in soil, transfer rate and toxicity in lettuce and rice plants grown in a Cd-contaminated Typic Hapludox. Two simultaneous greenhouse experiments with lettuce and rice test plants were conducted in a randomized complete block design with four replications. The treatments consisted of four Cd rates (CdCl2), 0.0; 1.3; 3.0 and 6.0 mg kg(-1), based on the guidelines recommended by the Environmental Agency of the State of São Paulo, Brazil (Cetesb). Higher Cd rates increased extractable Cd (using Mehlich-3, Mehlich-1 and DTPA chemical extractants) and decreased lettuce and rice dry matter yields. However, no visual toxicity symptoms were observed in plants. Mehlich-1, Mehlich-3 and DTPA extractants were effective in predicting soil Cd availability as well as the Cd concentration and accumulation in plant parts. Cadmium concentration in rice remained below the threshold for human consumption established by Brazilian legislation. on the other hand, lettuce Cd concentration in edible parts exceeded the acceptable limit.

Funções de pedotransferência para a curva de resistência do solo à penetração

Funções de pedotransferência são regressões utilizadas para estimar atributos edáficos dependentes a partir de atributos independentes e de fácil determinação. Nesse sentido, são propostas na literatura diversas funções de pedotransferência que visam predizer a resistência do solo à penetração. Objetivou-se, portanto, com este trabalho, desenvolver e comparar a eficiência de cinco funções de pedotransferência para a curva de resistência do solo à penetração, presentes na literatura, por meio do ajuste de dados obtidos tanto com o penetrômetro de impacto (campo) quanto com o penetrômetro eletrônico (laboratório), em um Latossolo manejado sob diferentes modos (convencional e plantio direto). Foram coletadas amostras indeformadas de solo na entrelinha das culturas, nas camadas de 0-0,10, 0,10-0,20 e 0,20-0,30 m, logo após a semeadura, no florescimento e na colheita, para determinação dos atributos físico-hídricos do solo e também da resistência do solo à penetração, com o uso do penetrômetro eletrônico. A resistência do solo à penetração, obtida com o penetrômetro de impacto, foi determinada conforme a variação do conteúdo de água no solo ao longo do ciclo das culturas. As curvas ajustadas de resistência do solo à penetração tiveram a precisão e a acurácia testadas por meio de parâmetros estatísticos e foram comparadas pelo teste F. Houve sobreposição dos valores estimados pelo ajuste das curvas, evidenciando que a maneira de obtenção da resistência do solo à penetração (campo ou laboratório) não influenciou a relação entre a resistência à penetração e os atributos do solo. As equações RP = aUg b; RP = a(1-Ug)b; RP = ae bUg e RP = a + be não diferiram e foram as mais precisas e acuradas na predição da resistência do solo à penetração.

Aplicação da RUSLE a uma pequena bacia hidrográfica da Amazônia

A Amazônia vem sofrendo severas mudanças provocadas por atividades antrópicas, dentre as quais se destaca a transformação de áreas de floresta em áreas de uso agropecuário, resultando na intensificação dos processos erosivos. A erosão, com destaque ao arraste de partículas pelo escoamento superficial, causa redução da fertilidade do solo, prejudicando a produtividade agrícola, além de impactar a qualidade e quantidade dos recursos hídricos superficiais, fato agravado pelo forte regime pluviométrico e solos naturalmente pobres da região. Nesse contexto, o conhecimento dos processos erosivos, através da utilização de modelos matemáticos para predição da perda de solo, auxilia na determinação de práticas de manejo para o uso sustentável dos recursos naturais. A presente pesquisa buscou avaliar a aplicabilidade do modelo empírico RUSLE (Revised Universal Soil Loss Equation) na região, o qual considera a interação entre a energia da chuva, as características de solo e relevo, assim como os usos e manejos praticados. A pesquisa aplicou a RUSLE no trecho superior da bacia do igarapé da Prata, com uma área aproximada de 37 km^², que fica localizada no município de Capitão Poço/PA, aproximadamente 160 km da capital Belém, na Meso Região Nordeste Paraense. A metodologia empregada constou da construção de uma base de dados georreferenciada, formada a partir de fontes públicas, que passaram por adequações para inserção no ambiente SIG, que quando combinados permitiram a geração de um mapa de perda de solo da área de estudo. A pequena bacia do igarapé da Prata apresentou valores da perda de solo que variaram entre 0,004 e 72,48 t/ha.ano, com uma média de 5,12 e desvio padrão de 6,97, onde aproximadamente 12% de sua área total apresentam riscos ambientais devido aos processos erosivos. E o percentual restante, para não sofrer riscos, depende de boas práticas conservacionistas.

Caracterização geológica da bacia hidrográfica da albufeira de São domingos e avaliação da compatibilidade textural entre áreas-fonte e depósitos sedimentares

Tese de mestrado, Geologia do Ambiente, Riscos Geológicos e Ordenamento do TerritórioUniversidade de Lisboa, Faculdade de Ciências, 2016