This page has only limited features, please log in for full access.

Dr. Songchao Chen
INRAE Unité InfoSol, Orléans 45075, France

Basic Info


Research Keywords & Expertise

0 Pedometrics
0 Digital Soil Mapping
0 Proximal soil sensing
0 Soil Spectroscopy
0 Spatial predictive modelling

Fingerprints

Digital Soil Mapping

Honors and Awards

The user has no records in this section


Career Timeline

The user has no records in this section.


Short Biography

The user biography is not available.
Following
Followers
Co Authors
The list of users this user is following is empty.
Following: 0 users

Feed

Journal article
Published: 01 June 2021 in Environmental Research Letters
Reads 0
Downloads 0
ACS Style

Hongfen Teng; Zhongkui Luo; Jinfeng Chang; Zhou Shi; Songchao Chen; Yin Zhou; Philippe Ciais; Hanqin Tian. Climate change-induced greening on the Tibetan Plateau modulated by mountainous characteristics. Environmental Research Letters 2021, 16, 064064 .

AMA Style

Hongfen Teng, Zhongkui Luo, Jinfeng Chang, Zhou Shi, Songchao Chen, Yin Zhou, Philippe Ciais, Hanqin Tian. Climate change-induced greening on the Tibetan Plateau modulated by mountainous characteristics. Environmental Research Letters. 2021; 16 (6):064064.

Chicago/Turabian Style

Hongfen Teng; Zhongkui Luo; Jinfeng Chang; Zhou Shi; Songchao Chen; Yin Zhou; Philippe Ciais; Hanqin Tian. 2021. "Climate change-induced greening on the Tibetan Plateau modulated by mountainous characteristics." Environmental Research Letters 16, no. 6: 064064.

Journal article
Published: 26 May 2021 in Land
Reads 0
Downloads 0

Potentially toxic element (PTE) pollution in farmland soils and crops is a serious cause of concern in China. To analyze the bioaccumulation characteristics of chromium (Cr), zinc (Zn), copper (Cu), and nickel (Ni) in soil-rice systems, 911 pairs of top soil (0–0.2 m) and rice samples were collected from an industrial city in Southeast China. Multiple linear regression (MLR), support vector machines (SVM), random forest (RF), and Cubist were employed to construct models to predict the bioaccumulation coefficient (BAC) of PTEs in soil–rice systems and determine the potential dominators for PTE transfer from soil to rice grains. Cr, Cu, Zn, and Ni contents in soil of the survey region were higher than corresponding background contents in China. The mean Ni content of rice grains exceeded the national permissible limit, whereas the concentrations of Cr, Cu, and Zn were lower than their thresholds. The BAC of PTEs kept the sequence of Zn (0.219) > Cu (0.093) > Ni (0.032) > Cr (0.018). Of the four algorithms employed to estimate the bioaccumulation of Cr, Cu, Zn, and Ni in soil–rice systems, RF exhibited the best performance, with coefficient of determination (R2) ranging from 0.58 to 0.79 and root mean square error (RMSE) ranging from 0.03 to 0.04 mg kg−1. Total PTE concentration in soil, cation exchange capacity (CEC), and annual average precipitation were identified as top 3 dominators influencing PTE transfer from soil to rice grains. This study confirmed the feasibility and advantages of machine learning methods especially RF for estimating PTE accumulation in soil–rice systems, when compared with traditional statistical methods, such as MLR. Our study provides new tools for analyzing the transfer of PTEs from soil to rice, and can help decision-makers in developing more efficient policies for regulating PTE pollution in soil and crops, and reducing the corresponding health risks.

ACS Style

Modian Xie; Hongyi Li; Youwei Zhu; Jie Xue; Qihao You; Bin Jin; Zhou Shi. Predicting Bioaccumulation of Potentially Toxic Element in Soil–Rice Systems Using Multi-Source Data and Machine Learning Methods: A Case Study of an Industrial City in Southeast China. Land 2021, 10, 558 .

AMA Style

Modian Xie, Hongyi Li, Youwei Zhu, Jie Xue, Qihao You, Bin Jin, Zhou Shi. Predicting Bioaccumulation of Potentially Toxic Element in Soil–Rice Systems Using Multi-Source Data and Machine Learning Methods: A Case Study of an Industrial City in Southeast China. Land. 2021; 10 (6):558.

Chicago/Turabian Style

Modian Xie; Hongyi Li; Youwei Zhu; Jie Xue; Qihao You; Bin Jin; Zhou Shi. 2021. "Predicting Bioaccumulation of Potentially Toxic Element in Soil–Rice Systems Using Multi-Source Data and Machine Learning Methods: A Case Study of an Industrial City in Southeast China." Land 10, no. 6: 558.

Journal article
Published: 03 May 2021 in Soil and Tillage Research
Reads 0
Downloads 0

Soil organic carbon (SOC) is receiving increasing attention due to its large storage potential in global carbon cycles and its great importance to soil fertility, agricultural production, and ecosystem services. The increases of SOC storage and reliable estimation of its potential are essential for evaluating the soil sustainability and climate change adaptation under intensive cultivation. In this work, a data-driven approach combining mixture clustering and Random Forest models was proposed to estimate the SOC storage potential of cropland topsoil and its controlling factors in East China. The carbon landscapes systems (CLSs) were delineated using a mixture clustering model by combining the climatic condition, soil properties, cropping systems, and soil management practices. The SOC storage potentials with 95 % confidence intervals at 250 m spatial resolution were estimated as the difference between the current SOC stock and empirically maximum SOC stock at basic (75 %), intermediate (85 %), and ambitious (95 %) expectation objectives for each CLS. The SOC storage potential increased with the increasing of expectation objective settings, with the averaged levels of 13.1, 20.8, and 35.5 t C ha−1 at 75 %, 85 %, and 95 % percentile objectives, respectively. The variable importance from Random Forest indicated that the cropping systems and soil management practices were the unignorable factors controlling the SOC storage potential beyond the climatic conditions and soil properties. Moreover, the shifts of human-induced controlling factors, e.g., cropping systems, also indicated their capability of SOC sequestration potential for partly achieving the “4p1000” initiative (annual growth rate of 0.4 % carbon stocks in the first 30 cm of topsoil). The currently optimal soil management practices for achieving the SOC sequestration potential was the combination of rice-based cropping systems, straw return, and organic fertilizer applied. The data-driven approach coupling with CLSs improved our understanding of the controlling factors on SOC storage potential at regional level with homogenous conditions, enabling evidence-based decision making in promoting carbon sequestration by adopting locally feasible soil management practices.

ACS Style

Wanzhu Ma; Yu Zhan; Songchao Chen; Zhouqiao Ren; Xiaojia Chen; Fangjin Qin; Ruohui Lu; Xiaonan Lv; Xunfei Deng. Organic carbon storage potential of cropland topsoils in East China: Indispensable roles of cropping systems and soil managements. Soil and Tillage Research 2021, 211, 105052 .

AMA Style

Wanzhu Ma, Yu Zhan, Songchao Chen, Zhouqiao Ren, Xiaojia Chen, Fangjin Qin, Ruohui Lu, Xiaonan Lv, Xunfei Deng. Organic carbon storage potential of cropland topsoils in East China: Indispensable roles of cropping systems and soil managements. Soil and Tillage Research. 2021; 211 ():105052.

Chicago/Turabian Style

Wanzhu Ma; Yu Zhan; Songchao Chen; Zhouqiao Ren; Xiaojia Chen; Fangjin Qin; Ruohui Lu; Xiaonan Lv; Xunfei Deng. 2021. "Organic carbon storage potential of cropland topsoils in East China: Indispensable roles of cropping systems and soil managements." Soil and Tillage Research 211, no. : 105052.

Journal article
Published: 22 April 2021 in Geoderma
Reads 0
Downloads 0

Visible-near infrared (vis–NIR) spectroscopy has been widely used to characterize soil information from field to global scales. Before applying a calibrated spectral predictive model to acquire soil information, either independent validation or k-fold cross validation is used to evaluate model performance. However, there is no consensus on which validation strategy is more suitable and robust when evaluating model performance for the studies in different scales. The objective of this study is to evaluate and compare the model performance of two validation strategies coupling different calibration sizes (a ratio of calibration to validation of 2:1, 4:1 and 9:1) and calibration sampling strategies (random sampling (RS), rank, Kennard-Stone (KS), rank-Kennard-Stone (RKS) and conditioned Latin hypercube sampling (cLHS)) across scales. A total of 17,272 vis–NIR spectra of mineral soils from LUCAS data (continental scale) and their soil organic carbon (SOC) and clay contents were used in this study, and the dataset was further split into national (2761 samples in France) and five regional datasets (110 to 248 samples from five French administrative regions). To eliminate the effect of changing validation set on the model performance, a consistent test set (20% of total samples at each scale) was split to evaluate all the combinations involved in two validation strategies. The Lin’s concordance correlation coefficient (CCC) of the cubist model were stable for both SOC and clay for different calibration sizes, calibration sampling and validation strategies for a large calibration size (>1400) at the national and continental scales. A larger calibration size can potentially improve model performance for a small dataset (<300) at the regional scale, and a wider calibration range would result in better model performance. No silver bullet was found among the different calibration sampling strategies at the regional scale. For five French regions (small data set), we found a high variation (95th percentile minus the 5th percentile) in the CCC among the models built from 50 repeated RS (0.10–0.44 for SOC, 0.16–0.52 for clay) and cLHS (0.08–0.40 for SOC, 0.12–0.36 for clay). This finding indicates that a one-time RS or cLHS for selecting the calibration set has high uncertainty in model evaluation for a small dataset and therefore should be used with caution. Therefore, we suggest the following: (1) for a large data set (thousands), either one-time random sampling for independent validation or k-fold cross validation would be appropriate; (2) for a small data set (dozens to hundreds), k-fold cross validation and/or repeated random sampling for independent validation would be more robust for spectral predictive model evaluation.

ACS Style

Songchao Chen; Hanyi Xu; Dongyun Xu; Wenjun Ji; Shuo Li; Meihua Yang; Bifeng Hu; Yin Zhou; Nan Wang; Dominique Arrouays; Zhou Shi. Evaluating validation strategies on the performance of soil property prediction from regional to continental spectral data. Geoderma 2021, 400, 115159 .

AMA Style

Songchao Chen, Hanyi Xu, Dongyun Xu, Wenjun Ji, Shuo Li, Meihua Yang, Bifeng Hu, Yin Zhou, Nan Wang, Dominique Arrouays, Zhou Shi. Evaluating validation strategies on the performance of soil property prediction from regional to continental spectral data. Geoderma. 2021; 400 ():115159.

Chicago/Turabian Style

Songchao Chen; Hanyi Xu; Dongyun Xu; Wenjun Ji; Shuo Li; Meihua Yang; Bifeng Hu; Yin Zhou; Nan Wang; Dominique Arrouays; Zhou Shi. 2021. "Evaluating validation strategies on the performance of soil property prediction from regional to continental spectral data." Geoderma 400, no. : 115159.

Journal article
Published: 06 April 2021 in Science of The Total Environment
Reads 0
Downloads 0

Ranking assessment of potentially contaminated sites (PCS) provides a great quantity of information (namely the risk screening list) that is usually examined by environmental managers, and therefore reduces the cost of risk management in terms of site investigation. Here we propose an integrated assessment methodology to establish a risk screening list of PCS in China using the Choquet integral correlation coefficient (ICC), which takes the uncertainty and interaction of PCS attributes into explicit account. The proposed method globally considers the importance and ordered positions of PCS attributes while reflecting their overall ranking. The model evaluation and actual validation results demonstrate the success in PCS ranking by the proposed method, which is superior to other methods such as the intuitionistic fuzzy multiple attribute decision-making, the technique for order preference by similarity to an ideal solution, and the weighted average. The resulting spatial distribution of Choquet ICC indicates that high-attention PCS in China are mainly located in Guangdong, Jiangsu, Zhejiang, and Shandong Provinces. This study is the first attempt to conduct a ranking assessment of PCS across China. The proposed assessment method based on Choquet ICC offers a step towards establishing a risk screening list of PCS globally.

ACS Style

Yefeng Jiang; Hanlin Wang; Mei Lei; Deyi Hou; Songchao Chen; Bifeng Hu; Mingxiang Huang; Weiwei Song; Zhou Shi. An integrated assessment methodology for management of potentially contaminated sites based on public data. Science of The Total Environment 2021, 783, 146913 .

AMA Style

Yefeng Jiang, Hanlin Wang, Mei Lei, Deyi Hou, Songchao Chen, Bifeng Hu, Mingxiang Huang, Weiwei Song, Zhou Shi. An integrated assessment methodology for management of potentially contaminated sites based on public data. Science of The Total Environment. 2021; 783 ():146913.

Chicago/Turabian Style

Yefeng Jiang; Hanlin Wang; Mei Lei; Deyi Hou; Songchao Chen; Bifeng Hu; Mingxiang Huang; Weiwei Song; Zhou Shi. 2021. "An integrated assessment methodology for management of potentially contaminated sites based on public data." Science of The Total Environment 783, no. : 146913.

Journal article
Published: 31 March 2021 in Environmental Research
Reads 0
Downloads 0

Soil erosion can present a major threat to agriculture due to loss of soil, nutrients, and organic carbon. Therefore, soil erosion modelling is one of the steps used to plan suitable soil protection measures and detect erosion hotspots. A bibliometric analysis of this topic can reveal research patterns and soil erosion modelling characteristics that can help identify steps needed to enhance the research conducted in this field. Therefore, a detailed bibliometric analysis, including investigation of collaboration networks and citation patterns, should be conducted. The updated version of the Global Applications of Soil Erosion Modelling Tracker (GASEMT) database contains information about citation characteristics and publication type. Here, we investigated the impact of the number of authors, the publication type and the selected journal on the number of citations. Generalized boosted regression tree (BRT) modelling was used to evaluate the most relevant variables related to soil erosion modelling. Additionally, bibliometric networks were analysed and visualized. This study revealed that the selection of the soil erosion model has the largest impact on the number of publication citations, followed by the modelling scale and the publication's CiteScore. Some of the other GASEMT database attributes such as model calibration and validation have negligible influence on the number of citations according to the BRT model. Although it is true that studies that conduct calibration, on average, received around 30% more citations, than studies where calibration was not performed. Moreover, the bibliographic coupling and citation networks show a clear continental pattern, although the co-authorship network does not show the same characteristics. Therefore, soil erosion modellers should conduct even more comprehensive review of past studies and focus not just on the research conducted in the same country or continent. Moreover, when evaluating soil erosion models, an additional focus should be given to field measurements, model calibration, performance assessment and uncertainty of modelling results. The results of this study indicate that these GASEMT database attributes had smaller impact on the number of citations, according to the BRT model, than anticipated, which could suggest that these attributes should be given additional attention by the soil erosion modelling community. This study provides a kind of bibliographic benchmark for soil erosion modelling research papers as modellers can estimate the influence of their paper.

ACS Style

Nejc Bezak; Matjaž Mikoš; Pasquale Borrelli; Christine Alewell; Pablo Alvarez; Jamil Alexandre Ayach Anache; Jantiene Baartman; Cristiano Ballabio; Marcella Biddoccu; Artemi Cerdà; Devraj Chalise; Songchao Chen; Walter Chen; Anna Maria De Girolamo; Gizaw Desta Gessesse; Detlef Deumlich; Nazzareno Diodato; Nikolaos Efthimiou; Gunay Erpul; Peter Fiener; Michele Freppaz; Francesco Gentile; Andreas Gericke; Nigussie Haregeweyn; Bifeng Hu; Amelie Jeanneau; Konstantinos Kaffas; Mahboobeh Kiani-Harchegani; Ivan Lizaga Villuendas; Changjia Li; Luigi Lombardo; Manuel López-Vicente; Manuel Esteban Lucas-Borja; Michael Maerker; Chiyuan Miao; Sirio Modugno; Markus Möller; Victoria Naipal; Mark Nearing; Stephen Owusu; Dinesh Panday; Edouard Patault; Cristian Valeriu Patriche; Laura Poggio; Raquel Portes; Laura Quijano; Mohammad Reza Rahdari; Mohammed Renima; Giovanni Francesco Ricci; Jesús Rodrigo-Comino; Sergio Saia; Aliakbar Nazari Samani; Calogero Schillaci; Vasileios Syrris; Hyuck Soo Kim; Diogo Noses Spinola; Paulo Tarso Oliveira; Hongfen Teng; Resham Thapa; Konstantinos Vantas; Diana Vieira; Jae E. Yang; Shuiqing Yin; Demetrio Antonio Zema; Guangju Zhao; Panos Panagos. Soil erosion modelling: A bibliometric analysis. Environmental Research 2021, 197, 111087 .

AMA Style

Nejc Bezak, Matjaž Mikoš, Pasquale Borrelli, Christine Alewell, Pablo Alvarez, Jamil Alexandre Ayach Anache, Jantiene Baartman, Cristiano Ballabio, Marcella Biddoccu, Artemi Cerdà, Devraj Chalise, Songchao Chen, Walter Chen, Anna Maria De Girolamo, Gizaw Desta Gessesse, Detlef Deumlich, Nazzareno Diodato, Nikolaos Efthimiou, Gunay Erpul, Peter Fiener, Michele Freppaz, Francesco Gentile, Andreas Gericke, Nigussie Haregeweyn, Bifeng Hu, Amelie Jeanneau, Konstantinos Kaffas, Mahboobeh Kiani-Harchegani, Ivan Lizaga Villuendas, Changjia Li, Luigi Lombardo, Manuel López-Vicente, Manuel Esteban Lucas-Borja, Michael Maerker, Chiyuan Miao, Sirio Modugno, Markus Möller, Victoria Naipal, Mark Nearing, Stephen Owusu, Dinesh Panday, Edouard Patault, Cristian Valeriu Patriche, Laura Poggio, Raquel Portes, Laura Quijano, Mohammad Reza Rahdari, Mohammed Renima, Giovanni Francesco Ricci, Jesús Rodrigo-Comino, Sergio Saia, Aliakbar Nazari Samani, Calogero Schillaci, Vasileios Syrris, Hyuck Soo Kim, Diogo Noses Spinola, Paulo Tarso Oliveira, Hongfen Teng, Resham Thapa, Konstantinos Vantas, Diana Vieira, Jae E. Yang, Shuiqing Yin, Demetrio Antonio Zema, Guangju Zhao, Panos Panagos. Soil erosion modelling: A bibliometric analysis. Environmental Research. 2021; 197 ():111087.

Chicago/Turabian Style

Nejc Bezak; Matjaž Mikoš; Pasquale Borrelli; Christine Alewell; Pablo Alvarez; Jamil Alexandre Ayach Anache; Jantiene Baartman; Cristiano Ballabio; Marcella Biddoccu; Artemi Cerdà; Devraj Chalise; Songchao Chen; Walter Chen; Anna Maria De Girolamo; Gizaw Desta Gessesse; Detlef Deumlich; Nazzareno Diodato; Nikolaos Efthimiou; Gunay Erpul; Peter Fiener; Michele Freppaz; Francesco Gentile; Andreas Gericke; Nigussie Haregeweyn; Bifeng Hu; Amelie Jeanneau; Konstantinos Kaffas; Mahboobeh Kiani-Harchegani; Ivan Lizaga Villuendas; Changjia Li; Luigi Lombardo; Manuel López-Vicente; Manuel Esteban Lucas-Borja; Michael Maerker; Chiyuan Miao; Sirio Modugno; Markus Möller; Victoria Naipal; Mark Nearing; Stephen Owusu; Dinesh Panday; Edouard Patault; Cristian Valeriu Patriche; Laura Poggio; Raquel Portes; Laura Quijano; Mohammad Reza Rahdari; Mohammed Renima; Giovanni Francesco Ricci; Jesús Rodrigo-Comino; Sergio Saia; Aliakbar Nazari Samani; Calogero Schillaci; Vasileios Syrris; Hyuck Soo Kim; Diogo Noses Spinola; Paulo Tarso Oliveira; Hongfen Teng; Resham Thapa; Konstantinos Vantas; Diana Vieira; Jae E. Yang; Shuiqing Yin; Demetrio Antonio Zema; Guangju Zhao; Panos Panagos. 2021. "Soil erosion modelling: A bibliometric analysis." Environmental Research 197, no. : 111087.

Journal article
Published: 20 March 2021 in Food Chemistry
Reads 0
Downloads 0

The potential of MIRS was investigated to: i) differentiate cooked purees issued from different apples and process conditions, and ii) predict the puree quality characteristics from the spectra of homogenized raw apples. Partial least squares (PLS) regression was tested both, on the real spectra of cooked purees and their reconstructed spectra calculated from the spectra of homogenized raw apples by direct standardization. The cooked purees were well-classified according to apple thinning practices and cold storage durations, and to different heating and grinding conditions. PLS models using the spectra of homogenized raw apples can anticipate the titratable acidity (the residual predictive deviation (RPD) = 2.9), soluble solid content (RPD = 2.8), particle averaged size (RPD = 2.6) and viscosity (RPD ≥ 2.5) of cooked purees. MIR technique can provide sustainable evaluations of puree quality, and even forecast texture and taste of purees based on the prior information of raw materials.

ACS Style

Weijie Lan; Catherine M.G.C. Renard; Benoit Jaillais; Alexandra Buergy; Alexandre Leca; Songchao Chen; Sylvie Bureau. Mid-infrared technique to forecast cooked puree properties from raw apples: A potential strategy towards sustainability and precision processing. Food Chemistry 2021, 355, 129636 .

AMA Style

Weijie Lan, Catherine M.G.C. Renard, Benoit Jaillais, Alexandra Buergy, Alexandre Leca, Songchao Chen, Sylvie Bureau. Mid-infrared technique to forecast cooked puree properties from raw apples: A potential strategy towards sustainability and precision processing. Food Chemistry. 2021; 355 ():129636.

Chicago/Turabian Style

Weijie Lan; Catherine M.G.C. Renard; Benoit Jaillais; Alexandra Buergy; Alexandre Leca; Songchao Chen; Sylvie Bureau. 2021. "Mid-infrared technique to forecast cooked puree properties from raw apples: A potential strategy towards sustainability and precision processing." Food Chemistry 355, no. : 129636.

Journal article
Published: 05 March 2021 in Journal of Cleaner Production
Reads 0
Downloads 0

The concentration of fine particulate matter (PM2.5) has a significant impact on the environment and human health. However, strong spatial heterogeneity and spatiotemporal dependence increases the difficulty of prediction. Moreover, due to the lag of the update of auxiliary variables at national scale in the prediction application, it is still difficult to achieve the timely nationwide PM2.5 prediction at present. To better model and predict real time concentrations and spatial distributions of PM2.5, this study developed a workflow of future PM2.5 concentrations prediction based on long short-term memory (LSTM) model. Using ground-based station PM2.5 data in 2014–2018, the 1 km Multi-Angle Implementation of Atmospheric Correction (MAIAC) aerosol optical depth (AOD) product and other auxiliary data to predict PM2.5 concentrations in the next year and generate a high-resolution national PM2.5 concentration spatial distribution map. The LSTM model outperformed random forest (RF) and Cubist approaches for prediction PM2.5 because of its recurrent neural network structure that can capture time dependence and nonlinear relationships among PM2.5 concentrations and other independent variables, and exhibited a stable accuracy with an R2 of 0.83, by applying the annual time series, with an improvement of 0.04–0.09, compared to daily and monthly data. The results indicated that PM2.5 pollution had gradually decreased in 2019 after application of pollution controls, with annual mean PM2.5 concentrations of 27.33 ± 15.56 μg m−3, although there were still some areas with severe pollution, including the North China Plain, parts of the Loess Plateau, and the Taklimakan Desert. The LSTM model makes it possible to predict fine-scale PM2.5 spatial distributions nationwide in the future and may thus be useful for sustainable management and control of air pollution at a national scale.

ACS Style

Zhige Wang; Yue Zhou; Ruiying Zhao; Nan Wang; Asim Biswas; Zhou Shi. High-resolution prediction of the spatial distribution of PM2.5 concentrations in China using a long short-term memory model. Journal of Cleaner Production 2021, 297, 126493 .

AMA Style

Zhige Wang, Yue Zhou, Ruiying Zhao, Nan Wang, Asim Biswas, Zhou Shi. High-resolution prediction of the spatial distribution of PM2.5 concentrations in China using a long short-term memory model. Journal of Cleaner Production. 2021; 297 ():126493.

Chicago/Turabian Style

Zhige Wang; Yue Zhou; Ruiying Zhao; Nan Wang; Asim Biswas; Zhou Shi. 2021. "High-resolution prediction of the spatial distribution of PM2.5 concentrations in China using a long short-term memory model." Journal of Cleaner Production 297, no. : 126493.

Editorial
Published: 04 March 2021 in Remote Sensing
Reads 0
Downloads 0

Recent advances in remote and proximal sensing technologies provide a valuable source of information for enriching our geo-datasets, which are necessary for soil management and the precision application of farming input resources

ACS Style

Abdul Mouazen; Zhou Shi. Estimation and Mapping of Soil Properties Based on Multi-Source Data Fusion. Remote Sensing 2021, 13, 978 .

AMA Style

Abdul Mouazen, Zhou Shi. Estimation and Mapping of Soil Properties Based on Multi-Source Data Fusion. Remote Sensing. 2021; 13 (5):978.

Chicago/Turabian Style

Abdul Mouazen; Zhou Shi. 2021. "Estimation and Mapping of Soil Properties Based on Multi-Source Data Fusion." Remote Sensing 13, no. 5: 978.

Journal article
Published: 26 February 2021 in Remote Sensing
Reads 0
Downloads 0

As an important parameter to characterize physical and biogeochemical processes, sea surface salinity (SSS) has received extensive attention. Cubist is a data mining model, which can be well-suited to estimate and analyze SSS in the Gulf of Mexico (GOM) because it can reflect the SSS internal heterogeneity in the GOM—overall circular distribution, and the seasonality related to temperature and river discharge changes. Using remote sensing reflectance (Rrs) at 412, 443, 488 (490), 555, and 667 (670) nm and sea surface temperature (SST), a cubist model was developed to estimate SSS with high accuracy with the overall performance demonstrates a root mean square error (RMSE) of 0.27 psu and correlation coefficient of 0.97 of R2. The model divides the GOM area according to model rules into four sub-regions, which include estuary, nearshore, and open sea, reflecting the gradient distribution of SSS. The division of sub-regions and seasonal changes can be explained by the distribution of water bodies, river discharges, and local wind forces since it is obvious that the estuary region reaches the largest low-value area and spreads eastward with the monsoon in the spring when the river flow increases to the highest value. While the east to west wind in the non-summer monsoon period guides the plume westward, and the lowest river discharge in winter corresponds to the smallest low value area. After comparison with other statistical models, the cubist model showed satisfactory results in independent verification of cruise data, proving the estimation capability under different geographical conditions (such as estuaries and open seas) and seasons. Therefore, considering high accuracy and heterogeneity mining, the cubist-based model is an ideal method for coastal SSS estimation and spatial-temporal heterogeneity analysis, and can provide ideas for model construction for coastal areas with similar geographic environments.

ACS Style

Zhiyi Fu; Fangfang Wu; Zhengliang Zhang; Linshu Hu; Feng Zhang; Bifeng Hu; Zhenhong Du; Zhou Shi; Renyi Liu. Sea Surface Salinity Estimation and Spatial-Temporal Heterogeneity Analysis in the Gulf of Mexico. Remote Sensing 2021, 13, 881 .

AMA Style

Zhiyi Fu, Fangfang Wu, Zhengliang Zhang, Linshu Hu, Feng Zhang, Bifeng Hu, Zhenhong Du, Zhou Shi, Renyi Liu. Sea Surface Salinity Estimation and Spatial-Temporal Heterogeneity Analysis in the Gulf of Mexico. Remote Sensing. 2021; 13 (5):881.

Chicago/Turabian Style

Zhiyi Fu; Fangfang Wu; Zhengliang Zhang; Linshu Hu; Feng Zhang; Bifeng Hu; Zhenhong Du; Zhou Shi; Renyi Liu. 2021. "Sea Surface Salinity Estimation and Spatial-Temporal Heterogeneity Analysis in the Gulf of Mexico." Remote Sensing 13, no. 5: 881.

Correction
Published: 20 February 2021 in Remote Sensing
Reads 0
Downloads 0

The authors wish to make the following correction to this paper

ACS Style

Yeneng Lin; Dongyun Xu; Nan Wang; Zhou Shi; Qiuxiao Chen. Correction: Lin, Y., et al. Road Extraction from Very-High-Resolution Remote Sensing Images via a Nested SE-Deeplab Model. Remote Sens. 2020, 12, 2985. Remote Sensing 2021, 13, 783 .

AMA Style

Yeneng Lin, Dongyun Xu, Nan Wang, Zhou Shi, Qiuxiao Chen. Correction: Lin, Y., et al. Road Extraction from Very-High-Resolution Remote Sensing Images via a Nested SE-Deeplab Model. Remote Sens. 2020, 12, 2985. Remote Sensing. 2021; 13 (4):783.

Chicago/Turabian Style

Yeneng Lin; Dongyun Xu; Nan Wang; Zhou Shi; Qiuxiao Chen. 2021. "Correction: Lin, Y., et al. Road Extraction from Very-High-Resolution Remote Sensing Images via a Nested SE-Deeplab Model. Remote Sens. 2020, 12, 2985." Remote Sensing 13, no. 4: 783.

Journal article
Published: 11 February 2021 in Postharvest Biology and Technology
Reads 0
Downloads 0

The heterogeneity of apple fruit was highlighted by near-infrared hyperspectral imaging (NIR-HSI) using a data analysis in two successive steps. First, NIR-HSI images were acquired on the cut surface of six transverse slices per apple, which were then systematically sampled with 5 or 6 cylinders per slice. PCA carried out on the NIR-HSI images allowed to select 141 representative cylinders from the total dataset (1056 samples), in which the contents of dry matter (DMC), total sugars (TSC), fructose, glucose, sucrose, malic acid and polyphenols were quantified by spectrophotometry and chromatography. In a second step, leave-one-out PLS models were developed and successfully used to describe the distribution of DMC (Rcv2 = 0.83, RPD = 2.39) and TSC (Rcv2 = 0.81, RPD = 2.20) in each apple slice. A strong heterogeneity of DMC and TSC was detected inside each fruit. Such a simple and rapid method reduced the needs of numerous chemical characterizations to demonstrate the distribution of quality traits within and between fruit and contributed to better manage the fruit quality measurements.

ACS Style

Weijie Lan; Benoit Jaillais; Catherine M.G.C. Renard; Alexandre Leca; Songchao Chen; Carine Le Bourvellec; Sylvie Bureau. A method using near infrared hyperspectral imaging to highlight the internal quality of apple fruit slices. Postharvest Biology and Technology 2021, 175, 111497 .

AMA Style

Weijie Lan, Benoit Jaillais, Catherine M.G.C. Renard, Alexandre Leca, Songchao Chen, Carine Le Bourvellec, Sylvie Bureau. A method using near infrared hyperspectral imaging to highlight the internal quality of apple fruit slices. Postharvest Biology and Technology. 2021; 175 ():111497.

Chicago/Turabian Style

Weijie Lan; Benoit Jaillais; Catherine M.G.C. Renard; Alexandre Leca; Songchao Chen; Carine Le Bourvellec; Sylvie Bureau. 2021. "A method using near infrared hyperspectral imaging to highlight the internal quality of apple fruit slices." Postharvest Biology and Technology 175, no. : 111497.

Journal article
Published: 25 January 2021 in International Journal of Environmental Research and Public Health
Reads 0
Downloads 0

Potentially toxic elements (PTEs) pollution in the agricultural soil of China, especially in developed regions such as the Yangtze River Delta (YRD) in eastern China, has received increasing attention. However, there are few studies on the long-term assessment of soil pollution by PTEs over large regions. Therefore, in this study, a meta-analysis was conducted to evaluate the current state and temporal trend of PTEs pollution in the agricultural land of the Yangtze River Delta. Based on a review of 118 studies published between 1993 and 2020, the average concentrations of Cd, Hg, As, Pb, Cr, Cu, Zn, and Ni were found to be 0.25 mg kg−1, 0.14 mg kg−1, 8.14 mg kg−1, 32.32 mg kg−1, 68.84 mg kg−1, 32.58 mg kg−1, 92.35 mg kg−1, and 29.30 mg kg−1, respectively. Among these elements, only Cd and Hg showed significant accumulation compared with their background values. The eastern Yangtze River Delta showed a relatively high ecological risk due to intensive industrial activities. The contents of Cd, Pb, and Zn in soil showed an increasing trend from 1993 to 2000 and then showed a decreasing trend. The results obtained from this study will provide guidance for the prevention and control of soil pollution in the Yangtze River Delta.

ACS Style

Shufeng She; Bifeng Hu; Xianglin Zhang; Shuai Shao; Yefeng Jiang; Lianqing Zhou; Zhou Shi. Current Status and Temporal Trend of Potentially Toxic Elements Pollution in Agricultural Soil in the Yangtze River Delta Region: A Meta-Analysis. International Journal of Environmental Research and Public Health 2021, 18, 1033 .

AMA Style

Shufeng She, Bifeng Hu, Xianglin Zhang, Shuai Shao, Yefeng Jiang, Lianqing Zhou, Zhou Shi. Current Status and Temporal Trend of Potentially Toxic Elements Pollution in Agricultural Soil in the Yangtze River Delta Region: A Meta-Analysis. International Journal of Environmental Research and Public Health. 2021; 18 (3):1033.

Chicago/Turabian Style

Shufeng She; Bifeng Hu; Xianglin Zhang; Shuai Shao; Yefeng Jiang; Lianqing Zhou; Zhou Shi. 2021. "Current Status and Temporal Trend of Potentially Toxic Elements Pollution in Agricultural Soil in the Yangtze River Delta Region: A Meta-Analysis." International Journal of Environmental Research and Public Health 18, no. 3: 1033.

Journal article
Published: 16 December 2020 in Remote Sensing
Reads 0
Downloads 0

Soil salinization, one of the most severe global land degradation problems, leads to the loss of arable land and declines in crop yields. Monitoring the distribution of salinized soil and degree of salinization is critical for management, remediation, and utilization of salinized soil; however, there is a lack of thorough assessment of various data sources including remote sensing and landscape characteristics for estimating soil salinity in arid and semi-arid areas. The overall goal of this study was to develop a framework for estimating soil salinity in diverse landscapes by fusing information from satellite images, landscape characteristics, and appropriate machine learning models. To explore the spatial distribution of soil salinity in southern Xinjiang, China, as a case study, we obtained 151 soil samples in a field campaign, which were analyzed in laboratory for soil electrical conductivity. A total of 35 indices including remote sensing classifiers (11), terrain attributes (3), vegetation spectral indices (8), and salinity spectral indices (13) were calculated or derived and correlated with soil salinity. Nine were used to model and estimate soil salinity using four predictive modelling approaches: partial least squares regression (PLSR), convolutional neural network (CNN), support vector machine (SVM) learning, and random forest (RF). Testing datasets were divided into vegetation-covered and bare soil samples and were used for accuracy assessment. The RF model was the best regression model in this study, with R2 = 0.75, and was most effective in revealing the spatial characteristics of salt distribution. Importance analysis and path modeling of independent variables indicated that environmental factors and soil salinity indices including digital elevation model (DEM), B10, and green atmospherically resistant vegetation index (GARI) showed the strongest contribution in soil salinity estimation. This showed a great promise in the measurement and monitoring of soil salinity in arid and semi-arid areas from the integration of remote sensing, landscape characteristics, and using machine learning model.

ACS Style

Nan Wang; Jie Xue; Jie Peng; Asim Biswas; Yong He; Zhou Shi. Integrating Remote Sensing and Landscape Characteristics to Estimate Soil Salinity Using Machine Learning Methods: A Case Study from Southern Xinjiang, China. Remote Sensing 2020, 12, 4118 .

AMA Style

Nan Wang, Jie Xue, Jie Peng, Asim Biswas, Yong He, Zhou Shi. Integrating Remote Sensing and Landscape Characteristics to Estimate Soil Salinity Using Machine Learning Methods: A Case Study from Southern Xinjiang, China. Remote Sensing. 2020; 12 (24):4118.

Chicago/Turabian Style

Nan Wang; Jie Xue; Jie Peng; Asim Biswas; Yong He; Zhou Shi. 2020. "Integrating Remote Sensing and Landscape Characteristics to Estimate Soil Salinity Using Machine Learning Methods: A Case Study from Southern Xinjiang, China." Remote Sensing 12, no. 24: 4118.

Journal article
Published: 25 November 2020 in CATENA
Reads 0
Downloads 0

Soil thickness (ST) plays an important role in regulating soil processes, vegetation growth and land suitability. Therefore, it has been listed as one of twelve basic soil properties to be delivered in GlobalSoilMap project. However, ST prediction has been reported with poor performance in previous studies. Our case study is located in the intensive agriculture Beauce area, central France. In this region, the ST mainly depends on the thickness of loess (TOL) deposits over a calcareous bedrock. We attempted to test the TOL prediction by coupling a large soil dataset (10978 sampling sites) and 117 environmental covariates. After variable selection by recursive feature elimination, quantile regression forests (QRF) was employed for spatial modelling, as it was able to directly provide the 90% prediction intervals (PIs). Averaging a total of 50 models, generated by repeated stratified random sampling, showed a substantial model performance with mean R2 of 0.33, RMSE of 30.48 cm and bias of −1.20 cm. The prediction interval coverage percentage showed that 86.70% of the validation samples fall within the predefined 90% PIs, which also indicated the prediction uncertainty produced by QRF was reasonable. The relative variable importance indicated the importance of airborne gamma-ray radiometric data and Sentinel 2 products in TOL prediction. The produced TOL map with 90% PIs makes sense from a soil science and physiographic point of view. The final product can guide evidence-based decision making for agricultural land management, especially for irrigation in our case study.

ACS Style

Songchao Chen; Anne C. Richer-De-Forges; Vera Leatitia Mulder; Guillaume Martelet; Thomas Loiseau; Sébastien Lehmann; Dominique Arrouays. Digital mapping of the soil thickness of loess deposits over a calcareous bedrock in central France. CATENA 2020, 198, 105062 .

AMA Style

Songchao Chen, Anne C. Richer-De-Forges, Vera Leatitia Mulder, Guillaume Martelet, Thomas Loiseau, Sébastien Lehmann, Dominique Arrouays. Digital mapping of the soil thickness of loess deposits over a calcareous bedrock in central France. CATENA. 2020; 198 ():105062.

Chicago/Turabian Style

Songchao Chen; Anne C. Richer-De-Forges; Vera Leatitia Mulder; Guillaume Martelet; Thomas Loiseau; Sébastien Lehmann; Dominique Arrouays. 2020. "Digital mapping of the soil thickness of loess deposits over a calcareous bedrock in central France." CATENA 198, no. : 105062.

Journal article
Published: 17 November 2020 in Remote Sensing
Reads 0
Downloads 0

Soil pollution by potentially toxic elements (PTEs) has become a core issue around the world. Knowledge of the spatial distribution of PTEs in soil is crucial for soil remediation. Portable X-ray fluorescence spectroscopy (p-XRF) provides a cost-saving alternative to the traditional laboratory analysis of soil PTEs. In this study, we collected 293 soil samples from Fuyang County in Southeast China. Subsequently, we used several geostatistical methods, such as inverse distance weighting (IDW), ordinary kriging (OK), and empirical Bayesian kriging (EBK), to estimate the spatial variability of soil PTEs measured by the laboratory and p-XRF methods. The final maps of soil PTEs were outputted by the model averaging method, which combines multiple maps previously created by IDW, OK, and EBK, using both lab and p-XRF data. The study results revealed that the mean PTE content measured by the laboratory methods was as follows: Zn (127.43 mg kg−1) > Cu (31.34 mg kg−1) > Ni (20.79 mg kg−1) > As (10.65 mg kg−1) > Cd (0.33 mg kg−1). p-XRF measurements showed a spatial prediction accuracy of soil PTEs similar to that of laboratory analysis measurements. The spatial prediction accuracy of different PTEs outputted by the model averaging method was as follows: Zn (R2 = 0.71) > Cd (R2 = 0.68) > Ni (R2 = 0.67) > Cu (R2 = 0.62) > As (R2 = 0.50). The prediction accuracy of the model averaging method for five PTEs studied herein was improved compared with that of the laboratory and p-XRF methods, which utilized individual geostatistical methods (e.g., IDW, OK, EBK). Our results proved that p-XRF was a reliable alternative to the traditional laboratory analysis methods for mapping soil PTEs. The model averaging approach improved the prediction accuracy of the soil PTE spatial distribution and reduced the time and cost of monitoring and mapping PTE soil contamination.

ACS Style

Fang Xia; Bifeng Hu; Youwei Zhu; Wenjun Ji; Songchao Chen; Dongyun Xu; Zhou Shi. Improved Mapping of Potentially Toxic Elements in Soil via Integration of Multiple Data Sources and Various Geostatistical Methods. Remote Sensing 2020, 12, 3775 .

AMA Style

Fang Xia, Bifeng Hu, Youwei Zhu, Wenjun Ji, Songchao Chen, Dongyun Xu, Zhou Shi. Improved Mapping of Potentially Toxic Elements in Soil via Integration of Multiple Data Sources and Various Geostatistical Methods. Remote Sensing. 2020; 12 (22):3775.

Chicago/Turabian Style

Fang Xia; Bifeng Hu; Youwei Zhu; Wenjun Ji; Songchao Chen; Dongyun Xu; Zhou Shi. 2020. "Improved Mapping of Potentially Toxic Elements in Soil via Integration of Multiple Data Sources and Various Geostatistical Methods." Remote Sensing 12, no. 22: 3775.

Research article
Published: 20 September 2020 in Land Degradation & Development
Reads 0
Downloads 0

Research on soil carbon dynamics and content in the Poyang Lake area is important because of their effects on the soil carbon pool and soil carbon cycle. Visible and near infrared (VNIR) diffuse reflectance spectroscopy can provide analytical dense soil data reflecting multiple physical and chemical properties of soil under in situ conditions. The effective use of in situ VNIR in Poyang Lake would greatly improve the collection of high‐resolution and spatially explicit soil data and, thus, hasten the monitoring process. We collected two data sets S1 and S2 in 2016‐2018. A dataset from S1 was used to develop the in situ correction matrix using laboratory and in situ spectra. Spectra from the Chinese soil spectroscopic database (CSSD) and dataset of S1 in dry laboratory condition were used for calibration (CSSD‐local calibration). The dataset from S2 was utilized as the validation dataset. Four in situ correction methods, external parameter orthogonalization (EPO), piecewise direct standardization, direct standardization, and generalized least squares weighting, were used to remove the in situ effect on the spectra. In addition, partial least squares regression and support vector machine (SVM), were used to test the effectiveness of the prediction. We also compared the prediction results by calibration of the CSSD, CSSD‐local and CSSD combined with four in situ samples from the validation datasets with extra‐weighting (CSSD‐EX). The results showed that EPO was the most effective method for removing the in situ effect. EPO and SVM with the calibration of CSSD‐EX yielded the best prediction result with the lowest root mean squared error at 1.71 g kg‐1 and the highest Lin's concordance correlation coefficient at 0.88. The CSSD with EPO and SVM would allow the in situ estimation of soil organic carbon in the Poyang Lake area at a reasonable cost. This article is protected by copyright. All rights reserved.

ACS Style

Meihua Yang; Songchao Chen; Hongyi Li; Xiaomin Zhao; Zhou Shi. Effectiveness of different approaches for in situ measurements of organic carbon using visible and near infrared spectrometry in the Poyang Lake basin area. Land Degradation & Development 2020, 32, 1301 -1311.

AMA Style

Meihua Yang, Songchao Chen, Hongyi Li, Xiaomin Zhao, Zhou Shi. Effectiveness of different approaches for in situ measurements of organic carbon using visible and near infrared spectrometry in the Poyang Lake basin area. Land Degradation & Development. 2020; 32 (3):1301-1311.

Chicago/Turabian Style

Meihua Yang; Songchao Chen; Hongyi Li; Xiaomin Zhao; Zhou Shi. 2020. "Effectiveness of different approaches for in situ measurements of organic carbon using visible and near infrared spectrometry in the Poyang Lake basin area." Land Degradation & Development 32, no. 3: 1301-1311.

Journal article
Published: 14 September 2020 in Remote Sensing
Reads 0
Downloads 0

Automatic road extraction from very-high-resolution remote sensing images has become a popular topic in a wide range of fields. Convolutional neural networks are often used for this purpose. However, many network models do not achieve satisfactory extraction results because of the elongated nature and varying sizes of roads in images. To improve the accuracy of road extraction, this paper proposes a deep learning model based on the structure of Deeplab v3. It incorporates squeeze-and-excitation (SE) module to apply weights to different feature channels, and performs multi-scale upsampling to preserve and fuse shallow and deep information. To solve the problems associated with unbalanced road samples in images, different loss functions and backbone network modules are tested in the model’s training process. Compared with cross entropy, dice loss can improve the performance of the model during training and prediction. The SE module is superior to ResNext and ResNet in improving the integrity of the extracted roads. Experimental results obtained using the Massachusetts Roads Dataset show that the proposed model (Nested SE-Deeplab) improves F1-Score by 2.4% and Intersection over Union by 2.0% compared with FC-DenseNet. The proposed model also achieves better segmentation accuracy in road extraction compared with other mainstream deep-learning models including Deeplab v3, SegNet, and UNet.

ACS Style

Yeneng Lin; Dongyun Xu; Nan Wang; Zhou Shi; Qiuxiao Chen. Road Extraction from Very-High-Resolution Remote Sensing Images via a Nested SE-Deeplab Model. Remote Sensing 2020, 12, 2985 .

AMA Style

Yeneng Lin, Dongyun Xu, Nan Wang, Zhou Shi, Qiuxiao Chen. Road Extraction from Very-High-Resolution Remote Sensing Images via a Nested SE-Deeplab Model. Remote Sensing. 2020; 12 (18):2985.

Chicago/Turabian Style

Yeneng Lin; Dongyun Xu; Nan Wang; Zhou Shi; Qiuxiao Chen. 2020. "Road Extraction from Very-High-Resolution Remote Sensing Images via a Nested SE-Deeplab Model." Remote Sensing 12, no. 18: 2985.

Research review
Published: 07 September 2020 in Global Change Biology
Reads 0
Downloads 0

To respect the Paris agreement targeting a limitation of global warming below 2°C by 2100, and possibly below 1.5 °C, drastic reductions of greenhouse gas emissions are mandatory but not sufficient. Large‐scale deployment of other climate mitigation strategies are also necessary. Among these, increasing soil organic carbon (SOC) stocks is an important lever because carbon in soils can be stored for long periods and land management options to achieve this already exist and have been widely tested. However, agricultural soils are also an important source of nitrous oxide (N2O), a powerful greenhouse gas, and increasing SOC may influence N2O emissions, likely causing an increase in many cases, thus tending to offset the climate change benefit from increased SOC storage. Here, we review the main agricultural management options for increasing SOC stocks. We evaluate the amount of SOC that can be stored as well as resulting changes in N2O emissions to better estimate the climate benefits of these management options. Based on quantitative data obtained from published meta‐analyses and from our current level of understanding, we conclude that the climate mitigation induced by increased SOC storage is generally overestimated if associated N2O emissions are not considered but, with the exception of reduced tillage, is never fully offset. Some options (e.g, biochar or non‐pyrogenic C amendment application) may even decrease N2O emissions.

ACS Style

Bertrand Guenet; Benoit Gabrielle; Claire Chenu; Dominique Arrouays; Jérôme Balesdent; Martial Bernoux; Elisa Bruni; Jean‐Pierre Caliman; Rémi Cardinael; Songchao Chen; Philippe Ciais; Dominique Desbois; Julien Fouche; Stefan Frank; Catherine Henault; Emanuele Lugato; Victoria Naipal; Thomas Nesme; Michael Obersteiner; Sylvain Pellerin; David S. Powlson; Daniel P. Rasse; Frédéric Rees; Jean‐François Soussana; Yang Su; Hanqin Tian; Hugo Valin; Feng Zhou. Can N 2 O emissions offset the benefits from soil organic carbon storage? Global Change Biology 2020, 27, 237 -256.

AMA Style

Bertrand Guenet, Benoit Gabrielle, Claire Chenu, Dominique Arrouays, Jérôme Balesdent, Martial Bernoux, Elisa Bruni, Jean‐Pierre Caliman, Rémi Cardinael, Songchao Chen, Philippe Ciais, Dominique Desbois, Julien Fouche, Stefan Frank, Catherine Henault, Emanuele Lugato, Victoria Naipal, Thomas Nesme, Michael Obersteiner, Sylvain Pellerin, David S. Powlson, Daniel P. Rasse, Frédéric Rees, Jean‐François Soussana, Yang Su, Hanqin Tian, Hugo Valin, Feng Zhou. Can N 2 O emissions offset the benefits from soil organic carbon storage? Global Change Biology. 2020; 27 (2):237-256.

Chicago/Turabian Style

Bertrand Guenet; Benoit Gabrielle; Claire Chenu; Dominique Arrouays; Jérôme Balesdent; Martial Bernoux; Elisa Bruni; Jean‐Pierre Caliman; Rémi Cardinael; Songchao Chen; Philippe Ciais; Dominique Desbois; Julien Fouche; Stefan Frank; Catherine Henault; Emanuele Lugato; Victoria Naipal; Thomas Nesme; Michael Obersteiner; Sylvain Pellerin; David S. Powlson; Daniel P. Rasse; Frédéric Rees; Jean‐François Soussana; Yang Su; Hanqin Tian; Hugo Valin; Feng Zhou. 2020. "Can N 2 O emissions offset the benefits from soil organic carbon storage?" Global Change Biology 27, no. 2: 237-256.

Journal article
Published: 16 August 2020 in Food Control
Reads 0
Downloads 0

Vis-NIRS, MIRS, and a combination of both coupled with PLS and machine learning were applied to i) trace the composed proportions of different apple varieties in formulated purees and ii) predict the quality characteristics of formulated purees from spectral information of initial puree cultivars. The PLS models could estimate proportions of each apple cultivar in puree mixtures using MIR spectra (RMSEP 3.6), especially for Granny Smith (RMSEP = 2.7%, RPD = 11.4). The concentration profiles from multivariate curve resolution-alternative least squares (MCR-ALS) made possible to reconstruct spectra of formulated purees. MIRS technique was evidenced to predict the final puree quality, such as viscosity (RPD>4.0), contents of soluble solids (RPD = 4.1), malic acid (RPD = 4.7) and glucose (RPD = 4.3), based only on the spectral data of composed puree cultivars. Infrared technique should be a powerful tool for puree traceability, even for multicriteria optimization of final products from the characteristics of composed puree cultivars before formulation.

ACS Style

Weijie Lan; Sylvie Bureau; Songchao Chen; Alexandre Leca; Catherine M.G.C. Renard; Benoit Jaillais. Visible, near- and mid-infrared spectroscopy coupled with an innovative chemometric strategy to control apple puree quality. Food Control 2020, 120, 107546 .

AMA Style

Weijie Lan, Sylvie Bureau, Songchao Chen, Alexandre Leca, Catherine M.G.C. Renard, Benoit Jaillais. Visible, near- and mid-infrared spectroscopy coupled with an innovative chemometric strategy to control apple puree quality. Food Control. 2020; 120 ():107546.

Chicago/Turabian Style

Weijie Lan; Sylvie Bureau; Songchao Chen; Alexandre Leca; Catherine M.G.C. Renard; Benoit Jaillais. 2020. "Visible, near- and mid-infrared spectroscopy coupled with an innovative chemometric strategy to control apple puree quality." Food Control 120, no. : 107546.