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Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objectives: On 5th September 2022, an Ms 6.8 earthquake struck the Luding County, Ganzi Prefecture, Sichuan Province, China. This earthquake triggered extensive geological hazards in the moun‐ tainous area, leading to serious casualties. Rapidly and accurately obtaining the spatial distribution of the in‐ duced geological hazards is crucial for emergency decision-making and rescue after an earthquake. Methods: Based on the global coseismic landslide database and deep learning algorithm, this paper built a near real-time prediction model of spatial distribution probability of coseismic landslides, and obtained the prediction results of the geological hazards induced by the Luding earthquake within 2 hours after the event. Through the post-earthquake unmanned aerial vehicle （UAV） and satellite remote sensing images, ma‐ chine learning and deep learning algorithms were used to realize the automated recognition of large-scale geological hazards. A total of 3 633 earthquake-induced landslides with an area of 13.78 km2 were interpreted. Finally, the model was optimized by integrating these landslide data, and the prediction results of coseismic landslides with a broader area and higher accuracy were achieved. Results: The results show that the coseis‐ mic landslide prediction model can realize a rapid capture of spatial distribution of post-earthquake geologi‐ cal hazards, filling the blank period before the acquisition of post-earthquake remote sensing images and providing support for post-disaster emergency rescue. Conclusions: Intelligent identification technologies based on UAV and satellite remote sensing images are effective means to rapidly obtain the vital informa‐ tion of large-scale geological hazards. The achievements obtained in this paper played an important role in the emergency rescue after the Luding earthquake. [ABSTRACT FROM AUTHOR]

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The rapid acquisition of soil water content (SWC) in field crop root zone is significant for drought supervision and precision irrigation. The UAV multispectral remote sensing system has the advantages of obtaining high spatial-temporal resolution of crop phenotype data, and has a wide application prospect in soil moisture monitoring. In order to obtain SWC accurately and timely at a farm scale, in this paper, the field maize with different water treatments is taken as the research object, and the multispectral remote sensing monitoring of summer maize is carried out by using the UAV remote sensing platform, and the soil water content of different soil depth in maize root zone is collected synchronously. Based on the UAV multispectral remote sensing image data sets of jointing stage, tasseling-silking stage and milky-maturity stage of summer maize in 2018, the soil background is removed by support vector machine classification, the spectral reflection of maize canopy is extracted, and the 10 vegetation indices are calculated, then the sensitivity analysis of soil water content in different depth is carried out by using full subset screening method for different wave bands and vegetation indices, and the soil water content in different depth is analyzed respectively, ridge regression and extreme learning machine are used to construct quantitative estimation models of soil water content at 0-20, 20-45 and 45-60 cm soil depth after full subset selection. The test area is located in Zhaojun Town, Dalate Banner, Ordos, Inner Mongolia, China(40°26'0.29" N, 109°36'25.99" E, elevation 1 010 m). The sowing time of maize is on May 11, 2018, the emergence time is on May 18, and the harvest time is on September 8, 2018. The total growth period is 114 days. The UAV multispectral remote sensing images and ground data collection dates are July 8, July 12, July 17, July 21, July 26, August 2, August 28 and September 7, 2018. It is collected once a day and tested 8 times in the whole growth period. July 8-21 is the jointing stage, July 26-August 2 is the tasseling-silking stage, August 28-september 7 is the milk-maturity stage. The flight altitude of the UAV is 70 m, and the flight time is 11:00-13:00 local time (11:44-13:44 Beijing time). Firstly, the UAV multispectral canopy images of field maize with 5 different irrigation treatments (TRTs) are acquired through the six-rotor UAV equipped with a RedEdge multispectral camera ( MicaSense, USA), and the multispectral images of diffuse reflector (reflectivity 58%, size 3×3 m) are collected at the same height to perform radiometric correction in the meantime, and then the spectral reflectances of the field maize are acquired. Secondly, the support vector machine (SVM) is used to eliminate the multispectral image of soil background in ENVI and ArcGIS software, then the maize canopy spectral reflectance is extracted and 10 vegetation indices (VIs), such as Normalized Difference Vegetation Index (NDVI), Normalized Green Difference Vegetation Index (GNDVI) and Transformed Chlorophyll Absorption In Reflectance Index(TCARI), etc, are calculated. Finally, the full subset selection method based on Bayesian Information Criterion (BIC) is used to analyze the sensitivity of SWC at different depths for different spectra bands and vegetation indices in R3.5.1 software, and then Ridge Regression (RR) as well as Extreme Learning Machine (ELM) are used to construct a quantitative estimation model of SWC at soil depths of 0-20, 20-45 and 45-60 cm at different growth stages, respectively. The results show that the full subset selection method based on BIC can effectively select the optimal spectral subset, and the selected variables generally pass the significance test and the independent variables number is small; the effects of the ELM model outperformed the RR model almost under all the same conditions; the optimal monitoring soil depth of maize at jointing stage, tasseling-silking stage is 0-20 cm, and the optimal monitoring soil depth of milk-maturity stage is 20-45 cm; the ELM inversion model at 20-45 cm soil depth at milk-maturity stage has the best effect, the decision coefficients of modeling set and verification set are 0.825 and 0.750, respectively, the root mean square error are 1.00% and 1.32%, respectively, and the normalized root mean square error are 10.85% and 13.55%, respectively. The combination of full subset selection method and machine learning can improve the inversion accuracy and robustness of SWC. This study provides a new way for rapid and accurate monitoring of SWC in farmland and precise irrigation. [ABSTRACT FROM AUTHOR]

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)