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Regional soil organic carbon prediction model based on a discrete wavelet analysis of hyperspectral satellite data
2020
International Journal of Applied Earth Observation and Geoinformation
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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
2021
Remote Sensing
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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
2022
Remote Sensing
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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
2020
Remote Sensing
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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
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., 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
2020
Remote Sensing
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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
2021
Agronomy
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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
2014
Sensors
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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
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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
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., 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
2021
Remote Sensing
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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
2000
Proceedings of the Third International Conference on Information Fusion
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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
2021
Sensors
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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
2020
Applied and Environmental Soil Science
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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
2020
Remote Sensing
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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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