Predicting soil organic carbon in cultivated land across geographical and spatial scales: Integrating Sentinel-2A and laboratory Vis-NIR spectra

[1]  F. Castaldi,et al.  Assessing the capability of Sentinel-2 time-series to estimate soil organic carbon and clay content at local scale in croplands , 2023, ISPRS Journal of Photogrammetry and Remote Sensing.

[2]  Jianxi Huang,et al.  Rapid early-season maize mapping without crop labels , 2023, Remote Sensing of Environment.

[3]  M. A. Munnaf,et al.  Spectra transfer based learning for predicting and classifying soil texture with short-ranged Vis-NIRS sensor , 2023, Soil and Tillage Research.

[4]  Z. Malenovský,et al.  Predicting plant biomass and species richness in temperate grasslands across regions, time, and land management with remote sensing and deep learning , 2022, Remote Sensing of Environment.

[5]  T. Arkebauer,et al.  Harmonizing solar induced fluorescence across spatial scales, instruments, and extraction methods using proximal and airborne remote sensing: A multi-scale study in a soybean field , 2022, Remote Sensing of Environment.

[6]  U. Heiden,et al.  Improving Soil Organic Carbon Predictions from Sentinel‑2 Soil Composites by Assessing Surface Conditions and Uncertainties , 2022, SSRN Electronic Journal.

[7]  J. Six,et al.  Towards spatially continuous mapping of soil organic carbon in croplands using multitemporal Sentinel-2 remote sensing , 2022, ISPRS Journal of Photogrammetry and Remote Sensing.

[8]  N. Ziadi,et al.  GLOBAL-LOCAL: A new approach for local predictions of soil organic carbon content using large soil spectral libraries , 2022, Geoderma.

[9]  Huanjun Liu,et al.  An advanced soil organic carbon content prediction model via fused temporal-spatial-spectral (TSS) information based on machine learning and deep learning algorithms , 2022, Remote Sensing of Environment.

[10]  Shutaro Kunimasa,et al.  Time Series Clustering with an EM algorithm for Mixtures of Linear Gaussian State Space Models , 2022, Pattern Recognit..

[11]  S. Ayoubi,et al.  Effects of different sources and spatial resolutions of environmental covariates on predicting soil organic carbon using machine learning in a semi-arid region of Iran , 2022, Geoderma Regional.

[12]  A. Maltese,et al.  Latent heat flux variability and response to drought stress of black poplar: A multi-platform multi-sensor remote and proximal sensing approach to relieve the data scarcity bottleneck , 2022, Remote Sensing of Environment.

[13]  Jorge Lucero-Álvarez,et al.  Interpretation of geochemical anomalies and domains using Gaussian mixture models , 2021, Applied Geochemistry.

[14]  Feng Liu,et al.  Mapping high resolution National Soil Information Grids of China. , 2021, Science bulletin.

[15]  Lei Zhang,et al.  Soil organic carbon prediction using phenological parameters and remote sensing variables generated from Sentinel-2 images , 2021 .

[16]  Philippe Lagacherie,et al.  Temporal mosaicking approaches of Sentinel-2 images for extending topsoil organic carbon content mapping in croplands , 2021, Int. J. Appl. Earth Obs. Geoinformation.

[17]  Stefan Hinz,et al.  Review on Convolutional Neural Networks (CNN) in vegetation remote sensing , 2021, ISPRS Journal of Photogrammetry and Remote Sensing.

[18]  V. S. Rodrigues,et al.  Estimating water erosion from the brightness index of orbital images: A framework for the prognosis of degraded pastures. , 2021, The Science of the total environment.

[19]  S. Ustin,et al.  Vis-SWIR spectral prediction model for soil organic matter with different grouping strategies , 2020 .

[20]  Eyal Ben-Dor,et al.  An integrated methodology using open soil spectral libraries and Earth Observation data for soil organic carbon estimations in support of soil-related SDGs , 2020 .

[21]  Huanjun Liu,et al.  Regional soil organic carbon prediction model based on a discrete wavelet analysis of hyperspectral satellite data , 2020, Int. J. Appl. Earth Obs. Geoinformation.

[22]  Huanjun Liu,et al.  Prediction of soil organic matter using multi-temporal satellite images in the Songnen Plain, China , 2019 .

[23]  Daniel Zízala,et al.  Soil Organic Carbon Mapping Using Multispectral Remote Sensing Data: Prediction Ability of Data with Different Spatial and Spectral Resolutions , 2019, Remote. Sens..

[24]  P. Burgess,et al.  Dry deposition of air pollutants on trees at regional scale: A case study in the Basque Country , 2019, Agricultural and Forest Meteorology.

[25]  Sabine Chabrillat,et al.  A remote sensing adapted approach for soil organic carbon prediction based on the spectrally clustered LUCAS soil database , 2019, Geoderma.

[26]  Bruna C. Gallo,et al.  Pedology and soil class mapping from proximal and remote sensed data , 2019, Geoderma.

[27]  A. Richards,et al.  Continental-scale soil carbon composition and vulnerability modulated by regional environmental controls , 2019, Nature Geoscience.

[28]  Dick J. Brus,et al.  Sampling for digital soil mapping: A tutorial supported by R scripts , 2019, Geoderma.

[29]  J. Demattê,et al.  Stratification of a local VIS-NIR-SWIR spectral library by homogeneity criteria yields more accurate soil organic carbon predictions , 2019, Geoderma.

[30]  David B. Lobell,et al.  Crop type mapping without field-level labels: Random forest transfer and unsupervised clustering techniques , 2019, Remote Sensing of Environment.

[31]  Andreas Hueni,et al.  Evaluating the capability of the Sentinel 2 data for soil organic carbon prediction in croplands , 2019, ISPRS Journal of Photogrammetry and Remote Sensing.

[32]  Mohammadmehdi Saberioon,et al.  Soil organic carbon and texture retrieving and mapping using proximal, airborne and Sentinel-2 spectral imaging , 2018, Remote Sensing of Environment.

[33]  Fei Zhang,et al.  New methods for improving the remote sensing estimation of soil organic matter content (SOMC) in the Ebinur Lake Wetland National Nature Reserve (ELWNNR) in northwest China , 2018, Remote Sensing of Environment.

[34]  David P. Roy,et al.  Observations and Recommendations for the Calibration of Landsat 8 OLI and Sentinel 2 MSI for Improved Data Interoperability , 2018, Remote. Sens..

[35]  J. Six,et al.  Links among warming, carbon and microbial dynamics mediated by soil mineral weathering , 2018, Nature Geoscience.

[36]  Derek Rogge,et al.  Building an exposed soil composite processor (SCMaP) for mapping spatial and temporal characteristics of soils with Landsat imagery (1984–2014) , 2018 .

[37]  L. Verchot,et al.  Global Sequestration Potential of Increased Organic Carbon in Cropland Soils , 2017, Scientific Reports.

[38]  Philippe Lagacherie,et al.  Digital soil mapping across the globe , 2017 .

[39]  Abdul Mounem Mouazen,et al.  Predictive performance of mobile vis-near infrared spectroscopy for key soil properties at different geographical scales by using spiking and data mining techniques , 2017 .

[40]  Liang Xu,et al.  Performance of non-parametric algorithms for spatial mapping of tropical forest structure , 2016, Carbon Balance and Management.

[41]  F. Sunar,et al.  SOIL SALINITY MAPPING USING MULTITEMPORAL LANDSAT DATA , 2016 .

[42]  R. Casa,et al.  Evaluation of the potential of the current and forthcoming multispectral and hyperspectral imagers to estimate soil texture and organic carbon , 2016 .

[43]  R. Kerry,et al.  Digital mapping of soil organic carbon at multiple depths using different data mining techniques in Baneh region, Iran , 2016 .

[44]  Zhou Shi,et al.  Prediction of soil organic matter using a spatially constrained local partial least squares regression and the Chinese vis–NIR spectral library , 2015 .

[45]  Chao-Cheng Wu,et al.  Effects of atmospheric correction and pansharpening on LULC classification accuracy using WorldView-2 imagery , 2015 .

[46]  R. Webster,et al.  Baseline map of organic carbon in Australian soil to support national carbon accounting and monitoring under climate change , 2014, Global Change Biology.

[47]  Laura Poggio,et al.  Regional scale mapping of soil properties and their uncertainty with a large number of satellite-derived covariates , 2013 .

[48]  Niu Zheng Luo Cheng-feng Wang Chang-yao Chen Fang A New Algorithm of Object Recognition Based on Spectral Library for TM Images , 2011 .

[49]  Charlie Chen,et al.  Digitally mapping the information content of visible–near infrared spectra of surficial Australian soils , 2011 .

[50]  Shirong Liu,et al.  Assessments of the impacts of Chinese fir plantation and natural regenerated forest on soil organic matter quality at Longmen mountain, Sichuan, China. , 2010 .

[51]  Rattan Lal,et al.  Predicting the spatial variation of the soil organic carbon pool at a regional scale. , 2010 .

[52]  G. Pan,et al.  Increase in soil organic carbon stock over the last two decades in China's Jiangsu Province , 2009 .

[53]  Fang Chen,et al.  Using low‐spectral‐resolution images to acquire simulated hyperspectral images , 2008 .

[54]  Ramón Díaz-Uriarte,et al.  Gene selection and classification of microarray data using random forest , 2006, BMC Bioinformatics.

[55]  R. Lal Soil Carbon Sequestration Impacts on Global Climate Change and Food Security , 2004, Science.

[56]  B. Minasny,et al.  Digital Soil Mapping , 2017 .

[57]  L. Breiman Random Forests , 2001, Encyclopedia of Machine Learning and Data Mining.

[58]  A. Huete,et al.  A Modified Soil Adjusted Vegetation Index , 1994 .

[59]  Zongming Wang,et al.  Remote estimates of soil organic carbon using multi-temporal synthetic images and the probability hybrid model , 2022, Geoderma.

[60]  S. Ustin,et al.  A regional-scale hyperspectral prediction model of soil organic carbon considering geomorphic features , 2021 .

[61]  Gengxing Zhao,et al.  Soil salinity inversion based on differentiated fusion of satellite image and ground spectra , 2021, Int. J. Appl. Earth Obs. Geoinformation.

[62]  N. E. Silvero,et al.  Soil variability and quantification based on Sentinel-2 and Landsat-8 bare soil images: A comparison , 2021 .

[63]  Minghui Wei,et al.  Cluster-based deep convolutional networks for spectral reconstruction from RGB images , 2021, Neurocomputing.

[64]  Sabine Grunwald,et al.  Fusion of Soil and Remote Sensing Data to Model Soil Properties , 2015 .

[65]  Qian Shen,et al.  Algorithms and Schemes for Chlorophyll a Estimation by Remote Sensing and Optical Classification for Turbid Lake Taihu, China , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[66]  Rosa Francaviglia,et al.  Estimation of soil organic matter by geostatistical methods: Use of auxiliary information in agricultural and environmental assessment , 2014 .

[67]  Ye Ze,et al.  Simulation of Remote Sensing Images Based on MIVIS Data , 2000 .

[68]  D. W. Nelson,et al.  A Rapid and Accurate Procedure for Estimation of Organic Carbon in Soils , 1974 .