Predicting soil organic carbon in cultivated land across geographical and spatial scales: Integrating Sentinel-2A and laboratory Vis-NIR spectra
暂无分享,去创建一个
[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 .