Runyan Zou
6 Papers
3 Citations
Runyan Zou is an academic researcher. The author has contributed to research in topics: Environmental science & Biology. The author has an hindex of 1, co-authored 1 publications.
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Papers
Integration of multimodal data for large-scale rapid agricultural land evaluation using machine learning and deep learning approaches
Liangdan Li,Luo Liu,Yiping Peng,Yingyue Su,Yueming Hu,Runyan Zou +5 more
TL;DR: This study integrates multimodal data to evaluate agricultural land quality in Guangdong Province, China, using machine learning (RF) and deep learning (DNN) models, achieving high accuracy (R2=0.91) and low cost, with RF outperforming DNN in paddy field and dry land evaluations.
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A New Method for Estimating Soil Fertility Using Extreme Gradient Boosting and a Backpropagation Neural Network
TL;DR: Wang et al. as mentioned in this paper used the backpropagation neural network (BPNN) algorithm to assess soil fertility using satellite image-based evaluation studies, and the BPNN model provided reliable estimates of soil fertility, with the determination coefficient (R2) of 0.66 and a root mean square error (RMSE).
7
Spatial Variation and Influencing Factors of Trace Elements in Farmland in a Lateritic Red Soil Region of China
TL;DR: In this article, the spatial variability of boron (B), manganese (Mn), molybdenum (Mo), copper (Cu), and zinc (Zn) in farmland soil in a typical red soil region were mapped using a geographically weighted regression (GWR) method.
Multifunctional Evaluation and Analysis of Synergistic Relationships: A Cognitive Framework for the Sustainable Use of Cropland in China
Runyan Zou,Yuanyuan Peng,Hao Yang,Yueming Hu,Luo Liu,Xiaoyun Mao +5 more
TL;DR: This study proposes a cognitive framework to evaluate and analyze cropland resources in China, focusing on Guangzhou, and identifies synergistic relationships between production, ecological, and living functions, providing insights for sustainable cropland management.
1
The Role of High Nature Value Farmland for Landscape and Soil Pollution Assessment in a Coastal Delta in China Based on High-Resolution Indicators
TL;DR: Wang et al. as discussed by the authors developed high-resolution (2 m × 2 m) indicators for the identification of potential high nature value farmland (HNVf) based on GF1B remote sensing imaging, including the land cover (LC), normalized difference vegetation index (NDVI), Shannon diversity (SH), and Simpsons index (SI).