4.6 Article

Analysing the effects of applying agricultural lime to soils by VNIR spectral sensing: a quantitative and quick method

期刊

INTERNATIONAL JOURNAL OF REMOTE SENSING
卷 34, 期 13, 页码 4570-4584

出版社

TAYLOR & FRANCIS LTD
DOI: 10.1080/01431161.2013.779045

关键词

-

向作者/读者索取更多资源

The potential of visible and near-infrared (VNIR) diffuse reflectance spectra to predict the chemical properties of Ferralsols and Arenosols cultivated with maize during four crop cycles were evaluated. The study was carried out in a greenhouse and aimed to (i) evaluate soil chemistry variation induced by plants and the application of lime with different degrees of reactivity using conventional methods and proximal soil-sensing techniques, (ii) identify the wavelength ranges related to soil chemistry changes, and (iii) construct models that predict soil chemistry attributes using soil VNIR spectra. Treatments used were three lime rates applied to raise the base saturation to 40%, 60% and 80% and one control. Partial least squares regression with cross-validation was used to establish relationships between the VNIR spectra and the reference data from chemical analyses. The predicted results were evaluated based on the values of coefficient of determination (R-2), the ratio of the standard deviation of the validation set to the root mean square error of cross-validation (RPD), and the root mean square of prediction. The predicted results were excellent (R-2>0.90 and RPD>3) for potassium and for the lime requirement calculation. Good predictions (0.81<0.90 and 2.5<3) were also obtained for pH and sum of bases. The resulting models for exchangeable calcium, cation exchangeable capacity, and base saturation had moderate predictive power (0.66<0.80 and 2.0<2.5). Our findings suggest that VNIR reflectance spectroscopy could be used as a rapid, inexpensive, and non-destructive technique to predict some soil chemistry properties for these soil types. As this methodology evolves, it may eventually permit real-time analyses of soil variability and real-time management responses via sensors installed on tractors.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.6
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

Article Environmental Sciences

Spatial prediction of soil surface properties in an arid region using synthetic soil image and machine learning

Salman Naimi, Shamsollah Ayoubi, Jose A. M. Dematte, Mojtaba Zeraatpisheh, Merilyn Taynara Accorsi Amorim, Fellipe Alcantara de Oliveira Mello

Summary: This study successfully spatialized soil properties in an arid region of Iran by integrating multisource environmental covariates and machine learning methods. The prediction accuracy of the models varied for different soil properties, and remote sensing data showed promise in enhancing the accuracy of digital soil mapping and reducing soil sampling costs.

GEOCARTO INTERNATIONAL (2022)

Article Environmental Sciences

The Brazilian Soil Spectral Service (BraSpecS): A User-Friendly System for Global Soil Spectra Communication

Jose A. M. Dematte, Ariane Francine da Silveira Paiva, Raul Roberto Poppiel, Nicolas Augusto Rosin, Luis Fernando Chimelo Ruiz, Fellipe Alcantara de Oliveira Mello, Budiman Minasny, Sabine Grunwald, Yufeng Ge, Eyal Ben Dor, Asa Gholizadeh, Cecile Gomez, Sabine Chabrillat, Nicolas Francos, Shamsollah Ayoubi, Dian Fiantis, James Kobina Mensah Biney, Changkun Wang, Abdelaziz Belal, Salman Naimi, Najmeh Asgari Hafshejani, Henrique Bellinaso, Jean Michel Moura-Bueno, Nelida E. Q. Silvero

Summary: Although many Soil Spectral Libraries have been created globally, they have not been operationalized for end-users. To address this, an online Brazilian Soil Spectral Service (BraSpecS) was created. The system allows users to find spectra, estimate soil properties, and act as data custodians.

REMOTE SENSING (2022)

Article Agricultural Engineering

Estimating particle-size distribution from limited soil texture data: Introducing two new methods

Hasan Mozaffari, Ali Akbar Moosavi, Jose A. M. Dematte

Summary: In this paper, two simple methods are introduced to estimate the particle-size distribution (PSD) of soils using fractions of sand, silt, clay, and very coarse sand. The accuracy of these methods is compared with the traditional Skaggs method using soil samples from different regions. The results show that the proposed methods can accurately predict the full range of PSD in a wide range of soil textures, with slightly lower accuracy in coarse-textured soils.

BIOSYSTEMS ENGINEERING (2022)

Article Geosciences, Multidisciplinary

The fundamental of the effects of water, organic matter, and iron forms on the pXRF information in soil analyses

Ncolas Augusto T. Rosin, Jose A. M. Dematte, Mauricio Cunha Almeida Leite, Hudson Wallace Pereira de Carvalho, Antonio Carlos Costa, Lucas Greschuk, Nilton Curi, Sergio Henrique Godinho Silva

Summary: Portable X-ray fluorescence (pXRF) has great potential for various applications in soil science. This study evaluated the effects of moisture, soil organic matter content, and iron forms on pXRF data. The results showed that particle size distribution and elemental content influenced the counts. There was also a high correlation between pXRF data and particle size distribution and mineralogy attributes.

CATENA (2022)

Article Soil Science

The Brazilian soil Mid-infrared Spectral Library: The Power of the Fundamental Range

Wanderson de Sousa Mendes, Jose A. M. Dematte, Nicolas Augusto Rosin, Fabricio da Silva Terra, Raul R. Poppiel, Diego F. Urbina-Salazar, Cacio Luiz Boechat, Elisangela Benedet Silva, Nilton Curi, Sergio Henrique Godinho Silva, Uemeson Jose dos Santos, Gustavo Souza Valladares

Summary: This study aimed to build a national soil spectral library from the middle infrared spectral range (MIR) and evaluate its descriptive and quantitative potential for soil assessment. The results showed that MIR could accurately predict soil physicochemical attributes and had a strong association with environmental and geographical variables. The findings provide practical information on fundamental soil signatures for future agronomic and environmental decisions.

GEODERMA (2022)

Article Soil Science

Complex hydrological knowledge to support digital soil mapping

Fellipe A. O. Mello, Jose A. M. Dematte, Rodnei Rizzo, Danilo C. de Mello, Raul R. Poppiel, Nelida E. Q. Silvero, Jose L. Safanelli, Henrique Bellinaso, Benito R. Bonfatti, Andres M. R. Gomez, Gabriel P. B. Sousa

Summary: Drainage network (DN) is an important factor influencing the formation and characteristics of soil. This study used complex DN variables to predict soil attributes in a specific area of Brazil. The results showed that DN variables significantly contributed to the prediction of clay, sand, and soil organic carbon (SOC) content. Drainage density (DD) and drainage frequency (DF) were the most important variables in the models. The study suggests the need for further exploration and understanding of the relationship between DN and soil information.

GEODERMA (2022)

Article Environmental Sciences

Soil Erosion Satellite-Based Estimation in Cropland for Soil Conservation

Bruna Cristina Gallo, Paulo Sergio Graziano Magalhaes, Jose A. M. Dematte, Walter Rossi Cervi, Joao Luis Nunes Carvalho, Leandro Carneiro Barbosa, Henrique Bellinaso, Danilo Cesar de Mello, Gustavo Vieira Veloso, Marcelo Rodrigo Alves, Elpidio Inacio Fernandes-Filho, Marcio Rocha Francelino, Carlos Ernesto Goncalves Reynaud Schaefer

Summary: This research presents a new approach to evaluate soil loss by water erosion in cropland using the RUSLE model and Synthetic Soil Image. By analyzing remote sensing data and satellite images, it predicts and assesses soil erosion and proposes conservation measures.

REMOTE SENSING (2023)

Article Chemistry, Analytical

A Novel Method for Estimating Chlorophyll and Carotenoid Concentrations in Leaves: A Two Hyperspectral Sensor Approach

Renan Falcioni, Werner Camargos Antunes, Jose Alexandre Melo Dematte, Marcos Rafael Nanni

Summary: Leaf optical properties can be used to identify environmental conditions, light intensities, plant hormone levels, pigment concentrations, and cellular structures. Using two hyperspectral sensors for both reflectance and absorbance data can lead to more accurate predictions of absorbance spectra. The green/yellow regions have a greater impact on photosynthetic pigment predictions, while the blue and red regions have a minor impact. Carotenoids show high correlation coefficients using the partial least squares regression (PLSR) method when associated with hyperspectral absorbance data, supporting the effectiveness of using two hyperspectral sensors for optical leaf profile analysis and predicting the concentration of photosynthetic pigments.

SENSORS (2023)

Article Plant Sciences

Enhancing Pigment Phenotyping and Classification in Lettuce through the Integration of Reflectance Spectroscopy and AI Algorithms

Renan Falcioni, Joao Vitor Ferreira Goncalves, Karym Mayara de Oliveira, Caio Almeida de Oliveira, Jose A. M. Dematte, Werner Camargos Antunes, Marcos Rafael Nanni

Summary: In this study, artificial intelligence algorithms (AIAs) combined with VIS-NIR-SWIR hyperspectroscopy were used to classify eleven lettuce plant varieties. The highest accuracy and precision were achieved using the full hyperspectral curves or specific spectral ranges. Four models, AdB, CN2, G-Boo, and NN, demonstrated exceptional performance, exceeding 0.99 for R-2 and ROC values, highlighting the potential of AIAs and hyperspectral fingerprints for precise classification and pigment phenotyping in agriculture.

PLANTS-BASEL (2023)

Article Plant Sciences

Reflectance Spectroscopy for the Classification and Prediction of Pigments in Agronomic Crops

Renan Falcioni, Werner Camargos Antunes, Jose Alexandre M. Dematte, Marcos Rafael Nanni

Summary: Reflectance spectroscopy, combined with machine learning and artificial intelligence algorithms, is an effective method for classifying and predicting pigments and phenotyping in agronomic crops. This study successfully developed a robust and precise method for evaluating pigments in six agronomic crops using hyperspectral data. The results showed high accuracy and precision, with the integration of vegetation indices further improving accuracy. Hyperspectral reflectance offers a promising alternative for monitoring and classification in agriculture, providing a non-destructive technique for evaluating pigments in important agronomic plants.

PLANTS-BASEL (2023)

Article Biology

Biophysical, Biochemical, and Photochemical Analyses Using Reflectance Hyperspectroscopy and Chlorophyll a Fluorescence Kinetics in Variegated Leaves

Renan Falcioni, Werner Camargos Antunes, Jose A. M. Dematte, Marcos Rafael Nanni

Summary: The study investigates the use of chlorophyll a fluorescence kinetics analyses and reflectance hyperspectroscopy to monitor the photosynthetic process in Codiaeum variegatum (L.) A. Juss. The results show that certain vegetation indexes are highly correlated with morphological and pigment parameters, while others are associated with photochemical components of photosynthesis. These findings are significant for monitoring nonuniform leaves, especially those with high pigment profiling variations.

BIOLOGY-BASEL (2023)

Article Environmental Sciences

Assessment of Combined Reflectance, Transmittance, and Absorbance Hyperspectral Sensors for Prediction of Chlorophyll a Fluorescence Parameters

Renan Falcioni, Werner Camargos Antunes, Roney Berti de Oliveira, Marcelo Luiz Chicati, Jose Alexandre M. Dematte, Marcos Rafael Nanni

Summary: This study improved predictive models for chlorophyll a fluorescence (ChlF) parameters in plants using hyperspectral sensors and statistical techniques. The findings showed a strong relationship between hyperspectral sensor data and ChlF parameters, demonstrating the potential of hyperspectral sensors for noninvasive evaluations of plant photosynthetic efficiency and health monitoring.

REMOTE SENSING (2023)

Article Plant Sciences

Chemometric Analysis for the Prediction of Biochemical Compounds in Leaves Using UV-VIS-NIR-SWIR Hyperspectroscopy

Renan Falcioni, Joao Vitor Ferreira Goncalves, Karym Mayara de Oliveira, Caio Almeida de Oliveira, Amanda Silveira Reis, Luis Guilherme Teixeira Crusiol, Renato Herrig Furlanetto, Werner Camargos Antunes, Everson Cezar, Roney Berti de Oliveira, Marcelo Luiz Chicati, Jose Alexandre M. Dematte, Marcos Rafael Nanni

Summary: Reflectance hyperspectroscopy has the potential to elucidate biochemical changes in plants. This study used UV-VIS-NIR-SWIR spectral range to identify different biochemical constituents in Hibiscus and Geranium plants. Through the application of advanced algorithms, the most responsive wavelengths were discerned, and PLSR models consistently achieved high R2 values. These findings highlight the efficacy of spectroscopy coupled with multivariate analysis in evaluating biochemical compounds and indicate the promising potential of hyperspectroscopy in precision agriculture and plant phenotyping.

PLANTS-BASEL (2023)

Article Agricultural Engineering

VNIR-SWIR Spectroscopy, XRD and Traditional Analyses for Pedomorphogeological Assessment in a Tropical Toposequence

Jean J. Novais, Raul R. Poppiel, Marilusa P. C. Lacerda, Jose A. M. Dematte

Summary: This study assessed soil characteristics and properties in a representative toposequence of the Brazilian Midwest using traditional analyses and geotechnologies. The results showed that spectroscopy and X-ray diffraction can identify soil features and properties, and reveal the correlations in soil formation processes. These findings are important for the future development of soil science.

AGRIENGINEERING (2023)

Article Agricultural Engineering

Spectral Mixture Modeling of an ASTER Bare Soil Synthetic Image Using a Representative Spectral Library to Map Soils in Central-Brazil

Jean J. J. Novais, Raul R. R. Poppiel, Marilusa P. C. Lacerda, Manuel P. P. Oliveira, Jose A. M. Dematte

Summary: Pedological maps in suitable scales are scarce in most countries due to high surveying costs. This study aimed to develop a digital soil map by extrapolating multispectral data from a source area to a target area using the ASTER time series modeling technique. The soil profiles were analyzed and classified, and the soil spectra were interpreted to identify typical features of tropical soils. Cluster analysis grouped the soil spectra by soil texture, forming a spectral library. The ASTER time series was processed to generate a bare soil synthetic image, and the spectral library was modeled on the synthetic image to create a digital soil map.

AGRIENGINEERING (2023)

暂无数据