Estimation of Maize Yield in Yitong County based on Multi-source Remote Sensing Data from 2007 to 2017

Wang, Yibo; Wang, Xue; Tan, Kun; Chen, Yu; Xu, Kailei

doi:10.1109/multi-temp.2019.8866845

Cited by 1 publication

(1 citation statement)

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“…With the rapid development of aerospace and remote sensing technology, remote sensing imagery data have gradually become an important means for monitoring terrestrial ecological environments due to their recording characteristics of long-term, macroscopic, and periodic monitoring [15,16]. In this sense, the range limitation of field-testing methods can be solved by establishing estimation models with the combination of remote sensing data, providing a fast and effective way to monitor largescale vegetation NPP [14,17,18]. The vegetation NPP estimation models can be broadly classified into statistical, parametric, and process models [19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Quantifying the Spatiotemporal Variation of NPP of Different Land Cover Types and the Contribution of Its Associated Factors in the Songnen Plain

Lin,

Li,

Jiang

et al. 2023

Forests

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Net primary productivity (NPP) of vegetation is considered an important indicator for ecological stability and is the main object for analyzing the factors influencing the terrestrial carbon cycle. Recent studies have made clear the changes in the NPP of vegetation and its influencing factors at various scales. However, the variations in NPP based on different land cover types under various natural conditions, along with their driving factors, remain not well understood. In this study, spatial overlay analysis was used to investigate the link among climatic, soil moisture (SM), and topographic parameters and NPP of various land cover types after analyzing the spatial and temporal trends of NPP in the Songnen Plain from 2001 to 2020. Additionally, the contribution of each influence factor to the NPP of different land cover types was calculated using the elastic net regression model. The elastic net regression model eliminates the multicollinearity among the influencing factors while maintaining the model stability, and the R2 of all lands is greater than 0.62, which can effectively quantify the contribution of each influencing factor to NPP. The results show a continuously increasing trend of the overall NPP in the research area over the selected 20 years, and NPP increased most significantly in forest land (FOR). Precipitation (PRE) and NPP showed high correlations in all the different land cover types, while the correlations between NPP and other influencing factors were significantly different. In addition, we found that perennials led to a more significant degree of NPP enhancement, and the effect of topographic conditions on NPP was mainly reflected in differences in moisture conditions due to surface runoff. From the results of the modeling calculations, the cumulative contribution of PRE to NPP ranks first in all land types and is the most vital influencing factor of NPP in the Songnen Plain. SM was an important influence, but the contribution of NPP was greater in land classes with shallow root systems. The results of the study revealed the positive transformation relationship of NPP among land cover types in ecologically fragile areas, which provides a reference for ecological restoration and rationalization of land use structure in zones such as intertwined agricultural and pastoral zones.

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