Article
Environmental Sciences
De Shorn E. Bramble, Gregory A. Gouveia, Ravindra Ramnarine, Richard E. Farrell
Summary: This study investigated the interactive effects of aglime and organic residues on CO2 emissions from two contrasting acid soils. Results showed that carbon mineralization in the Piarco soil increased with aglime addition but decreased in organic residue-amended soils. The rate of aglime-CO2 emissions decreased with poultry litter and corn stover application, and increased with glucose application, particularly in the Piarco soil, likely due to changes in soil pH induced by organic residues.
JOURNAL OF SOILS AND SEDIMENTS
(2021)
Article
Agricultural Engineering
Konrad Metzger, Chaosheng Zhang, Karen Daly
Summary: The study investigates the potential of handheld mid-infrared (MIR) spectrometers for soil analysis. It demonstrates the accuracy of predicting soil fertility metrics with calibration models, while also showcasing the partial effectiveness of moisture correction in field conditions.
BIOSYSTEMS ENGINEERING
(2021)
Article
Geosciences, Multidisciplinary
Luis Augusto Di Loreto Di Raimo, Eduardo G. Couto, Raul R. Poppiel, Danilo Cesar de Mello, Ricardo S. S. Amorim, Gilmar Nunes Torres, Edwaldo D. Bocuti, Gustavo Vieira Veloso, Elpidio Inacio Fernandes-Filho, Marcio Rocha Francelino, Jose A. M. Dematte
Summary: This study evaluates the potential of different sensing techniques to characterize and estimate sandy soil texture fractions and subfractions. The results show that the proximal level models are slightly more accurate in predicting the texture fractions compared to models based on satellite data.
Article
Environmental Sciences
Yi Chen, Zhe Zhang, Cheng Gao, Wenyang Deng, Wenqing Chen, Tianqi Ao
Summary: The study examined the application of compound amendments with different pH values in paddy fields, finding that alkaline amendments were more effective in remediating cadmium-contaminated soils. However, excessive use of alkaline amendments may inhibit processes such as microbial nitrogen assimilation and removal, leading to a weakening of the remediation effect. Furthermore, when applied to four other cadmium-contaminated soils, it was found that different amendments showed both negative and promoting effects over the long term.
ENVIRONMENTAL POLLUTION
(2021)
Review
Agronomy
Tho Nguyen
Summary: This paper reviews the existing lime requirement methods and discusses a new method suitable for routine use in the laboratory. The titration-based method shows promise for lime requirement determination in most agricultural soils due to its linear relationship with soil pH.
JOURNAL OF PLANT NUTRITION AND SOIL SCIENCE
(2023)
Article
Environmental Sciences
Wanderson de Sousa Mendes, Jose A. M. Dematte, Maria Eduarda B. de Resende, Luiz Fernando Chimelo Ruiz, Danilo Cesar de Mello, Jorge Tadeu Fim Rosas, Nelida Elizabet Quinonez Silvero, Luis Reynaldo Ferracciu Alleoni, Marina Colzato, Nicolas Augusto Rosin, Lucas Rabelo Campos
Summary: The study evaluated the potential of using long-term remote sensing images to detect and map PTEs in agricultural fields, showing a strong linear relationship between the selected PTEs and the near infrared (NIR) and shortwave infrared (SWIR) bands of various satellite sensors. The results indicated that satellite data could efficiently detect the contents of PTEs in soils due to their relation with soil attributes and parent materials, offering insights into the spatial dynamics and environmental effects of PTEs in tropical regions.
ENVIRONMENTAL POLLUTION
(2022)
Article
Agriculture, Multidisciplinary
Golnaz Ebrahimzadeh, Nafiseh Yaghmaeian Mahabadi, Hossein Bayat, HamidReza MatinFar
Summary: This study develops a model to estimate pre-compression stress in soil compaction using remote sensing technology and soil properties. The results show that the Boosted Regression Tree method performs better than the Random Forest method. Using the Redness Index and Surface Water Capacity Index as spectral indices, along with soil properties as inputs, improves the estimation of pre-compression stress. This study is important for understanding soil compaction processes and developing sustainable land management strategies.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2023)
Article
Biotechnology & Applied Microbiology
Shiyan Liao, Gaoqi Jin, Muhammad Aman Khan, Youwei Zhu, Lili Duan, Wenxuan Luo, Junwei Jia, Bin Zhong, Jiawei Ma, Zhengqian Ye, Dan Liu
Summary: The study found that intensive human activities, especially atmospheric deposition, industrial emissions, and traffic emissions, are the main causes of heavy metals pollution in suburban farmland soil. Although natural sources still make the largest contribution, input of human activities including industry, transportation, and agriculture cannot be ignored.
ENVIRONMENTAL TECHNOLOGY & INNOVATION
(2021)
Article
Chemistry, Analytical
Jian-Hang Hu, Wei Zhang, Chuang-Xin Ren, Yu Xiong, Jia-Yi Zhang, Jiao He, Ying Huang, Zhu Tao, Xin Xiao
Summary: A supramolecular polymer material BTPY@Q[8] with aggregation induced emission (AIE) was prepared by self-assembly of Q[8] and BTPY, which exhibited sensitive specificity and effective selectivity for picric acid (PA). A quick and simple on-site visual PA fluorescence quantitative detection platform was developed using smart phones. This sensing platform offers a reliable method for the quantitative detection of PA and can be applied to other analytes or micropollutant screening.
ANALYTICA CHIMICA ACTA
(2023)
Article
Environmental Sciences
Haiwei Liu, Yan Zhang, Jiashuo Yang, Haiyun Wang, Yile Li, Yi Shi, Decheng Li, Peter E. Holm, Quan Ou, Wenyou Hu
Summary: The study evaluated the levels of heavy metals in soil samples from a tobacco growing region in Shandong Peninsula, China, and identified Cd and Hg accumulations as posing ecological risks, while Cr and Ni levels presented a carcinogenic health risk to humans. The sources of heavy metals in the soils were mainly attributed to natural sources, coal combustion, agricultural practices, and industrial activities.
SCIENCE OF THE TOTAL ENVIRONMENT
(2021)
Article
Computer Science, Information Systems
Nafiseh Kakhani, Mehdi Mokhtarzade, Mohammad Javad Valadan Zoej
Summary: The advancement of remote sensing technology has led to higher spatial resolution of satellite images, allowing for precise analysis of small complex objects in a scene. Researchers have proposed a new method based on differential extinction profile (DEP) with significant progress in thematic map classification.
Article
Computer Science, Information Systems
Xiaoai Dai, Xuwei He, Shouheng Guo, Senhao Liu, Fujiang Ji, Huihua Ruan
Summary: This study introduces a deep learning-based feature extraction method for hyper-spectral data classification by using stacked de-noising auto-encoders to extract in-depth features of the image data and fine-tuning the neural network for better classification performance. Testing on a Hyspex imaging spectrometer image shows that this newly introduced model outperforms traditional methods including PCA, SVM, PCA-SVM, and MNF-SVM classifiers.
MULTIMEDIA TOOLS AND APPLICATIONS
(2021)
Article
Forestry
Kevin Keys, David L. Burton, G. W. Price, Peter N. Duinker
Summary: The study compared the effects of different amendments on ion availabilities in forest soils over a 10-week period. ATB was effective in supplying Ca2+ but not Mg2+, while lime was most effective for Mg2+. Fly ash amended soils had the greatest availability of K+ and SO42--S.
CANADIAN JOURNAL OF FOREST RESEARCH
(2021)
Article
Environmental Sciences
Xibo Xu, Zeqiang Wang, Xiaoning Song, Wenjie Zhan, Shuting Yang
Summary: The selection of predictor variables is crucial in building a digital mapping model for potentially toxic elements (PTEs) in soil. Traditionally, spatial and spectral parameters have been used as predictor variables, but the temporal dimension is often overlooked. This study demonstrates the value of incorporating temporal indices in the model, leading to significant performance improvements. The temporal-spatial-spectral covariate combinations used in a random forest (RF) algorithm achieve satisfactory mapping accuracy and outperform other methods.
ENVIRONMENTAL RESEARCH
(2024)
Article
Environmental Sciences
Zhifan Chen, Yongfeng Ding, Xingyuan Jiang, Haijing Duan, Xinling Ruan, Zhihong Li, Yipeng Li
Summary: This study successfully quantitatively identified the sources of heavy metals in agricultural soils in Kaifeng, China using various methods and techniques. The results showed that local anthropogenic pollution sources had a significant impact on heavy metal accumulation, with sewage irrigation and atmospheric deposition being the primary anthropogenic sources.
ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY
(2022)
Article
Environmental Sciences
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
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.
Article
Agricultural Engineering
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
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.
Article
Soil Science
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.
Article
Soil Science
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.
Article
Environmental Sciences
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.
Article
Chemistry, Analytical
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.
Article
Plant Sciences
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.
Article
Plant Sciences
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.
Article
Biology
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.
Article
Environmental Sciences
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.
Article
Plant Sciences
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.
Article
Agricultural Engineering
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.
Article
Agricultural Engineering
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.