Yayu Yang
Yayu Yang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
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Research Interests
Yayu Yang has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Changkun Wang
State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
1 shared publication - Chenchao Xiao
Land Satellite Remote Sensing Application Center, Ministry of Natural Resources, Beijing 100048, China
1 shared publication - Hongzhao Tang
Land Satellite Remote Sensing Application Center, Ministry of Natural Resources of China, Beijing 100048, China
1 shared publication - Kun Shang
Land Satellite Remote Sensing Application Center, Ministry of Natural Resources, Beijing 100048, China
1 shared publication
Latest Publications
- Open Access
- 4 February 2022
- Published by MDPI AG in ISPRS International Journal of Geo-Information
- Vol. 11 (2) , 111
Spectral Index for Mapping Topsoil Organic Matter Content Based on ZY1-02D Satellite Hyperspectral Data in Jiangsu Province, China
Estimation of soil organic matter content (SOMC) is essential for soil quality evaluation. Compared with traditional multispectral remote sensing for SOMC mapping, the distribution of SOMC in a certain area can be obtained quickly by using hyperspectral remote sensing data. The Advanced Hyper-Spectral Imager (AHSI) onboard the ZY1-02D satellite can simultaneously obtain spectral information in 166 bands from visible (400 nm) to shortwave infrared (2500 nm), providing an important data source for SOMC mapping. In this study, SOMC-related spectral indices (SIs) suitable for this satellite were analyzed and evaluated in Shuyang County, Jiangsu Province. A series of SIs were constructed for the bare soil and vegetation-covered (mainly rice crops and tree seedlings) areas by combining spectral transformations (such as reciprocal and square root) and dual-band index formulas (such as ratio and difference), respectively. The optimal SIs were determined based on Pearson’s correlation coefficient () and satellite data quality, and applied to SOMC level mapping and estimation. The results show that: (1) The SI with the highest in the bare soil area is the ratio index of original reflectance at 654 and 679 nm (OR-RI(654,679)), whereas the SI in the vegetation area is the square root of the difference between the reciprocal reflectance at 551 and 1998 nm (V-RR-DSI(551,1998)); (2) the spatial distribution trend of regional SOMC results obtained by linear regression models of OR-RI(654,679) and V-RR-DSI(551,1998) is consistent with the samples; (3) based on the optimal SIs, support vector machine and tree ensembles were used to predict the SOMC of bare soil and vegetation-covered areas of Shuyang County, respectively. The determination coefficient of the soil–vegetation combined prediction results is 0.775, the root mean square error is 3.72 g/kg, and the residual prediction deviation is 2.12. The results show that the proposed SIs for ZY1-02D satellite hyperspectral data are of great potential for SOMC mapping.- 4 February 2022
- Published by MDPI AG in ISPRS International Journal of Geo-Information
- Vol. 11 (2) , 111
Open AccessSpectral Index for Mapping Topsoil Organic Matter Content Based on ZY1-02D Satellite Hyperspectral Data in Jiangsu Province, China
Estimation of soil organic matter content (SOMC) is essential for soil quality evaluation. Compared with traditional multispectral remote sensing for SOMC mapping, the distribution of SOMC in a certain area can be obtained quickly by using hyperspectral remote sensing data. The Advanced Hyper-Spectral Imager (AHSI) onboard the ZY1-02D satellite can simultaneously obtain spectral information in 166 bands from visible (400 nm) to shortwave infrared (2500 nm), providing an important data source for SOMC mapping. In this study, SOMC-related spectral indices (SIs) suitable for this satellite were analyzed and evaluated in Shuyang County, Jiangsu Province. A series of SIs were constructed for the bare soil and vegetation-covered (mainly rice crops and tree seedlings) areas by combining spectral transformations (such as reciprocal and square root) and dual-band index formulas (such as ratio and difference), respectively. The optimal SIs were determined based on Pearson’s correlation coefficient () and satellite data quality, and applied to SOMC level mapping and estimation. The results show that: (1) The SI with the highest in the bare soil area is the ratio index of original reflectance at 654 and 679 nm (OR-RI(654,679)), whereas the SI in the vegetation area is the square root of the difference between the reciprocal reflectance at 551 and 1998 nm (V-RR-DSI(551,1998)); (2) the spatial distribution trend of regional SOMC results obtained by linear regression models of OR-RI(654,679) and V-RR-DSI(551,1998) is consistent with the samples; (3) based on the optimal SIs, support vector machine and tree ensembles were used to predict the SOMC of bare soil and vegetation-covered areas of Shuyang County, respectively. The determination coefficient of the soil–vegetation combined prediction results is 0.775, the root mean square error is 3.72 g/kg, and the residual prediction deviation is 2.12. The results show that the proposed SIs for ZY1-02D satellite hyperspectral data are of great potential for SOMC mapping.