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Regional soil organic carbon prediction model based on a discrete wavelet analysis of hyperspectral satellite data

Xiangtian Meng, Yilin Bao, Jiangui Liu, Huanjun Liu, Xinle Zhang, Yu Zhang, Peng Wang, Haitao Tang, Fanchang Kong
2020 International Journal of Applied Earth Observation and Geoinformation  
A B S T R A C T Most studies have the achieved rapid and accurate determination of soil organic carbon (SOC) using laboratory spectroscopy; however, it remains difficult to map the spatial distribution  ...  are small; and 4) the SOC prediction accuracy using hyperspectral satellite data is greatly improved compared with that of previous SOC prediction studies using multispectral satellite data.  ...  These research results provide a new framework for predicting and mapping SOC contents over large regions.  ... 
doi:10.1016/j.jag.2020.102111 fatcat:lxvnzwpezfavticdh2jh7ebyy4

Soil Organic Matter Prediction Model with Satellite Hyperspectral Image Based on Optimized Denoising Method

Xiangtian Meng, Yilin Bao, Qiang Ye, Huanjun Liu, Xinle Zhang, Haitao Tang, Xiaohan Zhang
2021 Remote Sensing  
This paper is relatively novel, in that GF-5 satellite hyperspectral data based on different denoising methods are used to predict SOM, and the results provide a highly robust and novel method for mapping  ...  improve the prediction accuracy of soil organic matter (SOM) content.  ...  Acknowledgments: We thank AJE (https://www.aje.com/, accessed on 20 December 2020) for its linguistic assistance during the preparation of this manuscript.  ... 
doi:10.3390/rs13122273 fatcat:qjkl2itrarbs5hexmjwuluwibu

Satellite Imagery to Map Topsoil Organic Carbon Content over Cultivated Areas: An Overview

Emmanuelle Vaudour, Asa Gholizadeh, Fabio Castaldi, Mohammadmehdi Saberioon, Luboš Borůvka, Diego Urbina-Salazar, Youssef Fouad, Dominique Arrouays, Anne C. Richer-de-Forges, James Biney, Johanna Wetterlind, Bas Van Wesemael
2022 Remote Sensing  
We did not find any studies either on deep learning methods or on all-performance evaluations and uncertainty analysis of spatial model predictions.  ...  Most satellite-derived SOC spectral prediction models used limited preprocessing and were based on bare soil pixel retrieval after Normalized Difference Vegetation Index (NDVI) thresholding.  ...  The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  ... 
doi:10.3390/rs14122917 fatcat:itronihprjdivjtcor5ii4dley

Hyperspectral Estimation of Soil Organic Matter Content using Different Spectral Preprocessing Techniques and PLSR Method

Lanzhi Shen, Maofang Gao, Jingwen Yan, Zhao-Liang Li, Pei Leng, Qiang Yang, Si-Bo Duan
2020 Remote Sensing  
model based on NDR.  ...  Hyperspectral remote sensing is one of the most efficient ways of estimating the SOM content.  ...  Acknowledgments: The authors would like to thank their colleagues and students from the Institute of Agricultural Resources and Regional Planning, Beijing, China, for collecting and processing the soil  ... 
doi:10.3390/rs12071206 fatcat:kzz4deoowrhuhfusgy5k5gusye

2019 Index IEEE Transactions on Geoscience and Remote Sensing Vol. 57

2019 IEEE Transactions on Geoscience and Remote Sensing  
., Insect Biological Parameter Estimation Based on the Invariant Target Parameters of the Scattering Matrix; TGRS Aug. 2019 6212-6225 Hu, C., see Zhang, M., TGRS Sept. 2019 6666-6674 Hu, C., Zhang,  ...  Region of Interest; TGRS Oct. 2019 7995-8010 Hu, T., see Kang, Z., TGRS Jan. 2019 181-193 Hu, T., Wu, Y., Zheng, G., Zhang, D., Zhang, Y., and Li, Y., Tropical Cyclone Center  ...  ., +, TGRS Nov. 2019 8867-8878 Discrete wavelet transforms A Fast Cross-Range Scaling Algorithm for ISAR Images Based on the 2-D Discrete Wavelet Transform and Pseudopolar Fourier Transform.  ... 
doi:10.1109/tgrs.2020.2967201 fatcat:kpfxoidv5bgcfo36zfsnxe4aj4

Recent Advances of Hyperspectral Imaging Technology and Applications in Agriculture

Bing Lu, Phuong D. Dao, Jiangui Liu, Yuhong He, Jiali Shang
2020 Remote Sensing  
Meanwhile, the acquisition, processing, and analysis of hyperspectral imagery still remain a challenging research topic (e.g., large data volume, high data dimensionality, and complex information analysis  ...  In comparison with multispectral imaging, hyperspectral imaging is a more advanced technique that is capable of acquiring a detailed spectral response of target features.  ...  [49] predicted soil organic carbon (SOC) using both spectroradiometer data and a Hyperion hyperspectral image, and they found that using Hyperion data resulted in a lower accuracy compared with results  ... 
doi:10.3390/rs12162659 fatcat:bfoe3xuja5b27b7wuu5ak7tdpq

Using Remote Sensing and an Unmanned Aerial System for Weed Management in Agricultural Crops: A Review

Muhammad Huzaifah Mohd Roslim, Abdul Shukor Juraimi, Nik Norasma Che'Ya, Nursyazyla Sulaiman, Muhammad Noor Hazwan Abd Manaf, Zaid Ramli, Mst. Motmainna
2021 Agronomy  
The integration of drones, artificial intelligence, and various sensors, which include hyperspectral, multi-spectral, and RGB (red-green-blue), ensure the possibility of a better outcome in managing weed  ...  It is a multi-disciplinary science that includes spectroscopy, optics, computer, photography, satellite launching, electronics, communication, and several other fields.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/agronomy11091809 fatcat:wt5aasqwhzbgznqfwp6d34qabq

Remote Sensing of Ecosystem Health: Opportunities, Challenges, and Future Perspectives

Zhaoqin Li, Dandan Xu, Xulin Guo
2014 Sensors  
Currently, remote sensing of ecosystem health is only based on one ecosystem attribute: vigor, organization, or resilience.  ...  However, the Radarsat-2 constellation, upcoming new optical sensors on Worldview-3 and Sentinel-2 satellites, and improved technologies for the acquisition and processing of hyperspectral, multi-angle  ...  the NSERC supplement of the College of Graduate Studies and Research of University of Saskatchewan.  ... 
doi:10.3390/s141121117 pmid:25386759 pmcid:PMC4279526 fatcat:fxqiulthzrfbneml4ubk7q3iv4

2019 Index IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol. 12

2019 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
Pang, C., +, JSTARS Sept. 2019 3412-3422 Discrete wavelet transforms Wavelet-Based Multicomponent Denoising Profile for the Classification of Hyperspectral Images.  ...  Ferreira, A., +, JSTARS Dec. 2019 4773-4786 Geo-Object-Based Soil Organic Matter Mapping Using Machine Learning Algorithms With Multi-Source Geo-Spatial Data.  ... 
doi:10.1109/jstars.2020.2973794 fatcat:sncrozq3fjg4bgjf4lnkslbz3u

2014 Index IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol. 7

2014 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
., Bronstert, A., and Foerster, S  ...  ., +, JSTARS July 2014 2942-2956 Discrete wavelet transforms Discrete Wavelet Transform Approach for the Estimation of Crop Residue Mass From Spectral Reflectance.  ...  Agathos, A., +, JSTARS June 2014 2281-2296 Organic compounds Carbon Stocks in Peri-Urban Areas: A Case Study of Remote Sensing Capabilities.  ... 
doi:10.1109/jstars.2015.2397347 fatcat:ib3tjwsjsnd6ri6kkklq5ov37a

Forest Leaf Mass per Area (LMA) through the Eye of Optical Remote Sensing: A Review and Future Outlook

Tawanda W. Gara, Parinaz Rahimzadeh-Bajgiran, Roshanak Darvishzadeh
2021 Remote Sensing  
Although studies on remote sensing of LMA and related constituents have been conducted for over three decades, a comprehensive review of remote sensing of LMA—a key driver of leaf and canopy reflectance—has  ...  Our review reveals that although progress has been made, scaling LMA to regional and global scales remains a challenge.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/rs13173352 fatcat:ddx6j4fvrbhprf2pchdeohlnji

Image registration and fusion for NASA remotely sensed imagery

J. Le Moigne, J.A. Smith
2000 Proceedings of the Third International Conference on Information Fusion  
Furthermore, with the new trend of smaller missions, most sensors will be carried on separate platforms, resulting in a tremendous amount of data that must be combined.  ...  In meeting some of the Earth System Science objectives, the combination of all these data at various resolutions-spatial, radiometric and temporalwill allow a better understanding of Earth and space science  ...  Sophisticated data assimilation methods are used to combine model predictions and satellite data for overall improved estimates or to fill missing gaps in the data records.  ... 
doi:10.1109/ific.2000.862667 fatcat:cp62jfxmdrbd3m6l5gwdf6xcqm

Machine Learning in Agriculture: A Comprehensive Updated Review

Lefteris Benos, Aristotelis C. Tagarakis, Georgios Dolias, Remigio Berruto, Dimitrios Kateris, Dionysis Bochtis
2021 Sensors  
Finally, a variety of sensors, attached on satellites and unmanned ground and aerial vehicles, have been utilized as a means of getting reliable input data for the data analyses.  ...  A subset of artificial intelligence, namely machine learning, has a considerable potential to handle numerous challenges in the establishment of knowledge-based farming systems.  ...  spectral, soil and weather data Winter wheat yield prediction at a regional level Combination of LSTM and CNN R 2 = 0.75, RMSE = 732 kgha −1 ; [169] Potato Hyperspectral data from UAV Yield prediction  ... 
doi:10.3390/s21113758 pmid:34071553 fatcat:moehdvs6efdpxpklidutmw2ary

Predicting Soil Organic Carbon Content Using Hyperspectral Remote Sensing in a Degraded Mountain Landscape in Lesotho

Freddy Bangelesa, Elhadi Adam, Jasper Knight, Inos Dhau, Marubini Ramudzuli, Thabiso M. Mokotjomela
2020 Applied and Environmental Soil Science  
Results show that random forest regression can most accurately predict the soil organic carbon contents on an independent dataset using the field spectroscopy data.  ...  Soil spectra were collected on the land surface under field conditions and then on soil in the laboratory, in order to assess the accuracy of field spectroscopy-based models.  ...  Introduction Soil organic carbon (SOC) is an important property related to soil biological, physical, and chemical characteristics and constitutes a major component of the global carbon cycle [1] .  ... 
doi:10.1155/2020/2158573 fatcat:aitd3h3mbvbmrlcv5h5knlgvhe

A Review of Advanced Technologies and Development for Hyperspectral-Based Plant Disease Detection in the Past Three Decades

Ning Zhang, Guijun Yang, Yuchun Pan, Xiaodong Yang, Liping Chen, Chunjiang Zhao
2020 Remote Sensing  
stresses discrimination, plant disease early warning, and satellite-based hyperspectral technology are the primary challenges and pave the way for a targeted response.  ...  On the basis of simply describing the types of pathogens and host–pathogen interaction processes, this review expounds the great advantages of hyperspectral technologies in plant disease detection.  ...  Conflicts of Interest: The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results  ... 
doi:10.3390/rs12193188 fatcat:zzpcf6zd4fgc3hywofdbzbub3m
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