A CASA-Based, MODIS-Constrained Framework for Consistent Annual NPP Simulation in Alpine Complex Environments: A Case Study of the Gannan Plateau.
Saved in:
| Title: | A CASA-Based, MODIS-Constrained Framework for Consistent Annual NPP Simulation in Alpine Complex Environments: A Case Study of the Gannan Plateau. |
|---|---|
| Authors: | Zhang, Dingyun1 (AUTHOR), Li, Yunfei1 (AUTHOR), Gou, Xiaohua1 (AUTHOR) xhgou@lzu.edu.cn |
| Source: | Remote Sensing. Aug2026, Vol. 18 Issue 15, p2456. 24p. |
| Subjects: | MODIS (Spectroradiometer), Primary productivity (Biology), Carbon cycle, Remote sensing, Mountain ecology, Regression analysis, Ecosystem dynamics |
| Abstract: | Highlights: What are the main findings? The MODIS-constrained CASA–GWR framework improved pooled pixel-level agreement between annual NPP estimates and the MODIS-constrained target during the 2014–2020 temporal transfer period, with R2 increasing from 0.438 to 0.708 and RMSE decreasing from 91.4 to 68.7 g C m−2 yr−1. The GWR residual-alignment layer reduced residual spatial organization and produced a moderate, directionally consistent improvement in product-level agreement with GLASS, which was used as an independent product-level benchmark rather than ground truth. What are the implications of the main findings? The framework provides a process-model-preserving approach for integrating CASA simulations with remote-sensing observational constraints without replacing CASA's mechanistic structure. The scenario application should be interpreted as a methodological scenario transfer experiment for exploring SSP pathway divergence, rather than as validation of future ecological processes. Net primary productivity (NPP) is a core diagnostic variable of terrestrial carbon cycling, yet consistent annual NPP simulation remains challenging in alpine heterogeneous regions where topography, hydrothermal gradients, vegetation structure, and nutrient constraints interact. Remote-sensing products such as MODIS provide valuable observational constraints, whereas light-use-efficiency models such as CASA retain process transparency and scenario transfer capability. This study develops a CASA-based, MODIS-constrained framework for annual NPP simulation over the Gannan Plateau. The framework preserves a locally parameterized CASA baseline and adds a geographically weighted regression (GWR) residual-alignment layer trained on CASA–MODIS residuals during 2005–2013. The fitted correction relationship was then applied to the 2014–2020 temporal transfer period and evaluated in a 2030s SSP scenario transfer experiment. MODIS was treated as the correction target rather than ground truth, and GLASS was adopted as an independent product-level benchmark. During 2014–2020, the GWR-corrected product showed improved pooled pixel-level agreement with the MODIS-constrained target relative to parameter-localized CASA, with R2 increasing from 0.438 to 0.708 and RMSE decreasing from 91.4 to 68.7 g C m−2 yr−1. Residual Moran's I also decreased, indicating weaker residual spatial organization after correction. Product-level comparison with GLASS showed a moderate, directionally consistent improvement relative to uncorrected CASA, although this comparison was not interpreted as ground-truth validation. The 2030s scenario transfer experiment indicated that the correction layer changed the spatial expression of NPP divergence among SSP pathways. Overall, the proposed framework provides a process-model-preserving and observation-constrained approach for improving agreement between annual NPP estimates and the MODIS-constrained target in alpine heterogeneous regions, while its applicability remains subject to product uncertainty, spatial dependence, and future nonstationarity. [ABSTRACT FROM AUTHOR] |
| Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. 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.) | |
| Database: | Engineering Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
| Abstract: | Highlights: What are the main findings? The MODIS-constrained CASA–GWR framework improved pooled pixel-level agreement between annual NPP estimates and the MODIS-constrained target during the 2014–2020 temporal transfer period, with R2 increasing from 0.438 to 0.708 and RMSE decreasing from 91.4 to 68.7 g C m−2 yr−1. The GWR residual-alignment layer reduced residual spatial organization and produced a moderate, directionally consistent improvement in product-level agreement with GLASS, which was used as an independent product-level benchmark rather than ground truth. What are the implications of the main findings? The framework provides a process-model-preserving approach for integrating CASA simulations with remote-sensing observational constraints without replacing CASA's mechanistic structure. The scenario application should be interpreted as a methodological scenario transfer experiment for exploring SSP pathway divergence, rather than as validation of future ecological processes. Net primary productivity (NPP) is a core diagnostic variable of terrestrial carbon cycling, yet consistent annual NPP simulation remains challenging in alpine heterogeneous regions where topography, hydrothermal gradients, vegetation structure, and nutrient constraints interact. Remote-sensing products such as MODIS provide valuable observational constraints, whereas light-use-efficiency models such as CASA retain process transparency and scenario transfer capability. This study develops a CASA-based, MODIS-constrained framework for annual NPP simulation over the Gannan Plateau. The framework preserves a locally parameterized CASA baseline and adds a geographically weighted regression (GWR) residual-alignment layer trained on CASA–MODIS residuals during 2005–2013. The fitted correction relationship was then applied to the 2014–2020 temporal transfer period and evaluated in a 2030s SSP scenario transfer experiment. MODIS was treated as the correction target rather than ground truth, and GLASS was adopted as an independent product-level benchmark. During 2014–2020, the GWR-corrected product showed improved pooled pixel-level agreement with the MODIS-constrained target relative to parameter-localized CASA, with R2 increasing from 0.438 to 0.708 and RMSE decreasing from 91.4 to 68.7 g C m−2 yr−1. Residual Moran's I also decreased, indicating weaker residual spatial organization after correction. Product-level comparison with GLASS showed a moderate, directionally consistent improvement relative to uncorrected CASA, although this comparison was not interpreted as ground-truth validation. The 2030s scenario transfer experiment indicated that the correction layer changed the spatial expression of NPP divergence among SSP pathways. Overall, the proposed framework provides a process-model-preserving and observation-constrained approach for improving agreement between annual NPP estimates and the MODIS-constrained target in alpine heterogeneous regions, while its applicability remains subject to product uncertainty, spatial dependence, and future nonstationarity. [ABSTRACT FROM AUTHOR] |
|---|---|
| ISSN: | 20724292 |
| DOI: | 10.3390/rs18152456 |
