Caracterização de óxidos de ferro de Latossolos da bacia hidrográfica do rio Marombas

TCC (graduação)- Universidade Federal de Santa Catarina. Centro de Curitibanos. Agronomia.Os óxidos de ferro são considerados indicadores pedoambientais. A quantificação indireta é feita pela extração de diferentes formas de ferro, enquanto que os minerais de estrutura cristalina são identificados diretamente via Difratometria de Raio X (DRX). A Espectroscopia de Reflectância Difusa (ERD) também tem sido utilizada para semiquantificação de minerais óxidos de ferro. O objetivo do trabalho foi de caracterizar os Latossolos da bacia hidrográfica do rio Marombas quanto ao conteúdo de óxidos de ferro utilizando diferentes métodos de quantificação. Dez perfis foram descritos morfologicamente e classificados pelo Sistema Brasileiro de Classificação de Solos. Análises químicas, granulométricas, DRX e espectrais foram feitas nos horizontes pedogenéticos dos perfis. Foram identificados os minerais hematita, goetita e maghemita nos Latossolos da bacia hidrográfica do rio Marombas. Os resultados demonstraram que a ERD foi ineficiente para a estimativa da relação hematita/(hematita+goetita) quando comparada com a DRX.The iron oxides are considered soil indicators. Their indirect quantification is done by extracting different forms of iron, while the X-ray diffraction (XRD) is used to directly quantify crystal structured minerals. The Diffuse Reflectance Spectroscopy (DRS) has been used for directly identification of iron oxides minerals. The aim of this study was to characterize Oxisoils from Marombas river’s watershed regarding its iron oxides content evaluated by different approaches. Ten soil profiles were morphologically described and classified by the Brazilian System of Soil Classification. Chemical, particle size, XRD and spectrum analysis were made in the pedogenetic horizons of the profiles. It was identified hematite, ghoetite and maghemita in the Oxisoils of Marombas river’s watershed. The results evidenced that DRS was inefficient for estimating hematite/(hematite+ghoetite) ratio when compared to XRD

Avanços na observação e no conhecimento do solo via o sensoriamento próximo

Agriculture employs increasingly innovative techniques in search of optimize inputs, maximize profitability, and reduce environmental impact. An example of this is the emergence of Agriculture 4.0, in which sensors collect information through Proximal Soil Sensing. These methods, called photon-based methods, employ different electromagnetic radiation wavelengths to measure soil attributes and properties in situ or ex situ. National and international research institutions have produced knowledge and contributed to the training of professionals able to apply these new approaches in soil science. In this context, this review aimed to produce a synthesis of the main proximal soil sensing techniques and made it accessible to students, technicians, and researchers.A agricultura emprega técnicas cada vez mais inovadoras na busca por otimizar insumos, maximizar a lucratividade e diminuir o impacto ambiental. Exemplo disso é o despontar da agricultura 4.0, na qual sensores coletem informações através do Sensoriamento Proximal do Solo. Esses métodos, chamados photon-based methods, empregam distintos comprimentos de onda da radiação eletromagnética para mensurar atributos e propriedades do solo in-situ ou ex-situ. Instituições nacionais de pesquisa têm produzido conhecimento relevante e contribuído para a formação de profissionais aptos a aplicar essas novas abordagens em ciência do solo. Nesse contexto, esta revisão bibliográfica teve como objetivo verter as principais técnicas de sensoriamento proximal em uma síntese acessível para estudantes, técnicos e pesquisadores

Potencializando a aplicação de dados de observação da Terra para o mapeamento e monitoramento de solos agrícolas

The use and sustainable development of cropland soils requires the continuous monitoring and promotion of good practices that support soil quality and the provision of its several functions. As the soil quality and functioning can be affected by several factors and interventions, resulting in changes at the temporal and spatial scales, Earth observation (EO) systems become an sound alternative for monitoring soils due to ability in providing data in a timely manner, covering large geographical areas, and revisiting the same place in Earth in short periods of time. Furthermore, as the availability of detailed information about cropland soils is still a challenge in most countries, and recent literature has been supporting the proposition that collections of EO data are a valuable source for environmental studies, this study aimed at exploring the collection of satellite images for mapping and monitoring cropland soils over large geographical areas. For this, we developed the routines for processing big EO data within a high-performance cloud-based platform. With the combination of extracted features from EO data, legacy soil datasets, and machine learning algorithms, we performed the medium-resolution mapping of cropland soils over the geographical extents of Europe and Brazil. We demonstrated in this study that the collection of Landsat images is a valuable source for extracting spectral features useful for mapping and monitoring cropland soils. The bare soil composite based on the median of 37 years of Landsat imagery allowed the prediction of clay and calcium carbonates with moderate performance in Europe. In addition to that, using the Google Earth Engine, we developed and made publicly available a package to calculate terrain attributes customized to different spatial resolutions, which can be scaled up to the global extent. This package was particularly important for preparing additional information for mapping the cropland soils in Brazil. The spectral and terrain features extracted from EO data allowed the calibration of prediction models of clay, sand, soil organic carbon (SOC) content, and SOC stock with satisfactory accuracy across the Brazilian cropland soils. With the resulting maps, we were able to estimate the total SOC stock and identify some aspects related to the distribution of soil attributes regarding the main agricultural regions. Therefore, this study supports the proposition that EO data is a valuable source for extracting environmental features for mapping and monitoring cropland soils at finer resolutions, assisting the evaluation of soil spatial distribution and the historical agriculture expansion in Europe and Brazil.O uso e desenvolvimento sustentável de terras agrícolas requer o monitoramento contínuo e a promoção de boas práticas que preservam a qualidade do solo e o proporcionem suas diversas funções. Como a qualidade e o funcionamento do solo pode ser afetado por diversos fatores e intervenções, as quais resultam em mudanças nas escalas temporal e espacial, os sistemas de observação da Terra (OT) tornam-se uma alternativa atrativa de monitoramento devido à capacidade de fornecer dados em tempo hábil, cobrindo grandes áreas geográficas, e revisitando o mesmo lugar na Terra em curtos períodos de tempo. Além disso, como a disponibilidade de informações detalhadas sobre solos de terras agrícolas ainda é um desafio na maioria dos países, e a literatura recente tem apoiado a proposição de que as coleções de dados de OT é uma fonte valiosa para estudos ambientais, este estudo teve como objetivo explorar as coleções de imagens de satélite para o mapeamento e monitoramento de solos agrícolas em grandes extensões geográficas. Para isso, desenvolvemos as rotinas de processamento de grande volume de dados OT em uma plataforma de alto desempenho baseada na nuvem. Com a combinação de recursos extraídos de dados de OT, informações legadas de solo, e algoritmos de aprendizado de máquina, realizamos o mapeamento de média resolução de solos agrícolas sobre as extensões geográficas da Europa e do Brasil. Demonstramos neste estudo que a coleção de imagens do Landsat é uma fonte valiosa para extrair recursos espectrais úteis para o mapeamento e monitoramento solos agrícolas. Imagens de solo exposto, baseados no valor mediano de 37 anos de imagens Landsat, permitiu a predição do teor de argila e carbonatos de cálcio com desempenho moderado no continente Europeu. Além disso, usando o Google Earth Engine, desenvolvemos e disponibilizamos publicamente um pacote para calcular atributos de terreno personalizados para diferentes resoluções espaciais, a qual pode ser explorada em estudos globais. Este pacote também foi particularmente importante para preparar informações adicionais para o mapeamento de solos agrícolas no Brasil. As características extraídas dos dados de OT permitiram a predição de argila, areia, conteúdo de carbono orgânico do solo (COS) e estoque de COS com acurácia satisfatória em solos agrícolas do território brasileiro. Com os mapas resultantes, conseguimos estimar o estoque total de SOC e identificar alguns aspectos relacionados à distribuição dos atributos do solo nas principais regiões agrícolas. Portanto, este estudo apoia a proposição de que dados de OT são uma fonte valiosa para extrair características da paisagem úteis ao mapeamento e monitoramento de solos agrícolas com resoluções mais precisas, auxiliando na avaliação da distribuição espacial do solo e no entendimento da expansão histórica da agricultura no Brasil e Europa

Soil Color and Mineralogy Mapping Using Proximal and Remote Sensing in Midwest Brazil

Soil color and mineralogy are used as diagnostic criteria to distinguish different soil types. In the literature, 350–2500 nm spectra were successfully used to predict soil color and mineralogy, but these attributes currently are not mapped for most Brazilian soils. In this paper, we provided the first large-extent maps with 30 m resolution of soil color and mineralogy at three depth intervals for 850,000 km2 of Midwest Brazil. We obtained soil 350–2500 nm spectra from 1397 sites of the Brazilian Soil Spectral Library at 0–20 cm, 20–60, and 60–100 cm depths. Spectra was used to derive Munsell hue, value, and chroma, and also second derivative spectra of the Kubelka–Munk function, where key spectral bands were identified and their amplitude measured for mineral quantification. Landsat composites of topsoil and vegetation reflectance, together with relief and climate data, were used as covariates to predict Munsell color and Fe–Al oxides, and 1:1 and 2:1 clay minerals of topsoil and subsoil. We used random forest for soil modeling and 10-fold cross-validation. Soil spectra and remote sensing data accurately mapped color and mineralogy at topsoil and subsoil in Midwest Brazil. Hematite showed high prediction accuracy (R2 > 0.71), followed by Munsell value and hue. Satellite topsoil reflectance at blue spectral region was the most relevant predictor (25% global importance) for soil color and mineralogy. Our maps were consistent with pedological expert knowledge, legacy soil observations, and legacy soil class map of the study region

Site-Specific Management Zones Delineation Based on Apparent Soil Electrical Conductivity in Two Contrasting Fields of Southern Brazil

Management practices that aim to increase the profitability of agricultural production with minimal environmental impact must consider within-field soil variability, and this site-specific management can be addressed by precision agriculture (PA). Thus, this work aimed to investigate which key soil attributes are distinguishable management zones (MZ) delineated based on the soil apparent electrical conductivity (ECa), using fuzzy k-means, in two fields with contrasting soil textures in southern Brazil. For this, a grid scheme (50 × 50 m) was applied to measure ECa, conduct soil sampling for analysis, and determine soybean yield. The MZ were delineated based on the ECa spatial distribution, and statistical non-parametric tests (p 2+, Mg2+, SB, Al3+, H+ + Al3+, AS%, and BS%. In the field classified as sandy clay loam texture, management zones were able to differentiate the average values of soybean yield, Clay, Ca2+, Mg2+, SB, and CEC. Thus, this study supports the ECa as an efficient tool for delineating MZ of contrasting cropland soils in southern Brazil to understand the within-field soil variability and adjust the inputs according

Site-Specific Management Zones Delineation Based on Apparent Soil Electrical Conductivity in Two Contrasting Fields of Southern Brazil

Management practices that aim to increase the profitability of agricultural production with minimal environmental impact must consider within-field soil variability, and this site-specific management can be addressed by precision agriculture (PA). Thus, this work aimed to investigate which key soil attributes are distinguishable management zones (MZ) delineated based on the soil apparent electrical conductivity (ECa), using fuzzy k-means, in two fields with contrasting soil textures in southern Brazil. For this, a grid scheme (50 × 50 m) was applied to measure ECa, conduct soil sampling for analysis, and determine soybean yield. The MZ were delineated based on the ECa spatial distribution, and statistical non-parametric tests (p < 0.05) were employed to compare the soil chemical and physical attributes among MZ. The management zones were able to distinguish the average values of Clay, Silt, pH, Ca2+, Mg2+, SB, Al3+, H+ + Al3+, AS%, and BS%. In the field classified as sandy clay loam texture, management zones were able to differentiate the average values of soybean yield, Clay, Ca2+, Mg2+, SB, and CEC. Thus, this study supports the ECa as an efficient tool for delineating MZ of contrasting cropland soils in southern Brazil to understand the within-field soil variability and adjust the inputs according

Diffuse reflectance mid-infrared spectroscopy is viable without fine milling

While diffuse reflectance Fourier transform mid-infrared spectroscopy (mid-DRIFTS) has been established as a viable low-cost surrogate for traditional soil analyses, the assumed need for fine milling of soil samples prior to analysis is constraining the commercial appeal of this technology. Here, we reevaluate this assumption using a set of 2380 soil samples collected across North American agricultural soils. Cross-validation indicated that the best preprocessing (standard normal variate) and model form (memory-based learning) resulted in very good and nearly identical predictions for the <2 mm preparation and fine-milled preparation of these soils for total organic carbon (TOC), clay, sand, pH and bulk density (BD). Application of larger models built from the USDA NRCS mid-DRIFTS library also resulted in minimal performance differences between the two sample preps. Lower predictive performance of the existing library was attributed to less-than-perfect spectral representativeness of the library. Regardless of model form, there was very little variability between replicates of the <2 mm prep, suggesting that the lack of fine milling did not lead to more heterogeneous subsamples. Additionally, there was no relationship between residual error and soil texture, implying these results should be robust across most soil types. Overall, in agreement with other recent findings, these results suggest that routine scanning of standard <2 mm preparation does not degrade predictive performance of mid-DRIFTS-based inference systems. With good standard operating procedures including quality control and traditional analysis on a small percent of samples, mid-DRIFTS can become a routine tool in commercial soil laboratories

Fine-scale soil mapping with Earth Observation data: a multiple geographic level comparison

Multitemporal collections of satellite images and their products have recently been explored in digital soil mapping. This study aimed to produce a bare soil image (BSI) for the São Paulo State (Brazil) to perform a pedometric analysis for different geographical levels. First, we assessed the potential of the BSI for predicting the surface (0.00-0.20 m) and subsurface (0.80-1.00 m) clay, iron oxides (Fe 2 O 3 ), aluminum (m%) and bases saturation (V%) contents at the state level, which are important properties for soil classification. In this task, legacy soil samples, the BSI and terrain attributes were employed in machine learning. In a second moment, we evaluated the capacity of the BSI for clustering the landscape at the regional level, comparing the predicted patterns with a legacy semi-detailed soil map from a smaller reference site. In the final stage, the predicted soil maps from the state level were investigated at the farm level considering several sites distributed across the São Paulo state. Our results demonstrated that clay and Fe 2 O 3 reached the best prediction performance for both depths at the state level, reaching a RMSE of less than 10 %, RPIQ higher than 1.6 and R 2 of at least 0.41. Additionally, the predicted landscape clusters had a significant association with the main pedological classes, subsurface color, soil mineralogy and texture from the legacy semi-detailed soil map. Illustrative examples at the farm level indicated great capacity of BSI in detecting the variations of soils, which were linked to several soil properties, such as texture, iron content, drainage network, among others. Therefore, this study demonstrates that BSI is valuable information derived from optical Earth Observation data that can contribute to the future of soil survey and mapping in Brazil (PronaSolos)

Vegetation indexes and delineation of management zones for soybean1

<div><p>ABSTRACT The delimitation of site-specific management zones may be an operational and economically feasible approach in precision agriculture. This study aimed at investigating the spatial correlations between spectral indexes sampled during different growth stages of soybean and crop yield. Soil attributes stratified in each zone and the influence of altitude were also assessed. The simple ratio index, normalized difference vegetation index and soil-adjusted vegetation index were calculated for soybean at the V6, R5 and R5.5 stages. Spatial dependence analysis via semivariogram was performed for the vegetation indexes, soybean yield and terrain elevation. The crop yield map was taken as a reference to assess the spatial agreement with the different maps generated from the spectral indexes. The average values for chemical and granulometric soil attributes were calculated and analyzed by their means among the zones delineated. The field division into two management zones, due to the combination of altitude, simple ratio index of the V6 stage and soil-adjusted vegetation index of the R5.5 stage, showed the highest agreement with the soybean yield map. Differences between the delineated zones were identified for the phosphorus, clay and silt contents.</p></div

Constructing soils for climate-smart mining

Abstract Surface mining is inherently linked to climate change, but more precise monitoring of carbon dioxide (CO2) emissions is necessary. Here we combined the geolocation of mine sites and carbon stock datasets to show that if all legal active mining sites in Brazil are exploited over the next decades, 2.55 Gt of CO2 equivalent (CO2eq) will be emitted due to the loss of vegetation (0.87 Gt CO2eq) and soil (1.68 Gt CO2eq). To offset these emissions, we propose constructing soils (Technosols) from mine and other wastes for mine reclamation. We show that this strategy could potentially offset up to 60% (1.00 Gt CO2eq) of soil-related CO2 emissions. When constructed with suitable parent materials, Technosols can also restore important soil-related ecosystem services while improving waste management. The construction of healthy Technosols stands out as a promising nature-based solution towards carbon-neutral mining and should, therefore, be considered in future environmental policies of major mining countries