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Land-Cover-Stratified Validation and Uncertainty Prioritization for SSP-Based NDVI Projection at 1 km Resolution in Northeast China.

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Title: Land-Cover-Stratified Validation and Uncertainty Prioritization for SSP-Based NDVI Projection at 1 km Resolution in Northeast China.
Authors: Rashad, Eslam1,2,3 (AUTHOR), Liu, Yujie1,2,4 (AUTHOR) liuyujie@igsnrr.ac.cn, Liu, Junjie1,2,3 (AUTHOR), Pan, Tao1,2,4 (AUTHOR), Refaee, Ahmed2,5,6 (AUTHOR)
Source: Remote Sensing. Jul2026, Vol. 18 Issue 13, p2203. 32p.
Subjects: Model validation, Normalized difference vegetation index, Climate change models, Machine learning
Geographic Terms: Manchuria (China)
Abstract: Highlights: What are the main findings? The study proposes a class-aware framework for SSP-based NDVI projection that integrates projection-oriented model selection, land-cover-stratified validation, uncertainty characterization, and validation-priority ranking. Regional NDVI is projected to increase modestly by 2040, but land-cover-stratified validation showed that global model performance masked weak predictive support for Water bodies and Unused land. What are the implications of the main findings? Future NDVI projections should be evaluated beyond global accuracy metrics because aggregate performance can hide important class-level uncertainty and prediction errors. The framework identifies Water bodies, Unused land, and Construction land as priority classes for targeted validation, supporting more transparent and reliable interpretation of vegetation projections. At 1 km resolution, NDVI projections for heterogeneous landscapes can appear spatially coherent in aggregate while concealing substantial class-level prediction weaknesses, a limitation that has received limited systematic attention in the NDVI projection literature. This study applies a four-component assessment workflow to Northeast China (NEC) for 2040 under SSP1-2.6, SSP2-4.5, and SSP5-8.5, integrating multi-stage model selection, land-cover-stratified validation, quantile-regression-based uncertainty characterization, and validation-priority ranking. Among three candidate tree-based models evaluated using spatial block cross-validation, temporal holdout validation, long-jump extrapolation, and climatic perturbation tests, LightGBM showed the most balanced and consistent performance, with spatial CV R2 = 0.654 ± 0.123, temporal holdout R2 = 0.710, and long-jump R2 = 0.671, and was therefore selected for the 2040 projection. Projected regional mean NDVI increased modestly from 0.393 in 2020 to 0.414–0.417 across scenarios, with limited divergence among SSP pathways at this near-term horizon. Class-stratified validation of the 2020 holdout prediction revealed that global model performance masked strong class-level heterogeneity, with R2 values ranging from 0.576 for Construction land to −0.886 for Unused land. Water bodies and Unused land exhibited negative R2 values, indicating weak class-level predictive support relative to a simple class-mean benchmark. Residual decomposition showed that Water bodies combined high random error with elevated systematic deviation, whereas Unused land was mainly characterized by systematic bias, suggesting different needs for class-specific model improvement. The Uncertainty Risk Index (URI), derived from 95% prediction intervals, was highest in Construction land and lowest in Cropland across all scenarios. Integrating historical residuals with future URI-identified Water bodies, Unused land, and Construction land as the highest-priority classes for future targeted validation. These priorities arise from both limited class representation and intrinsic NDVI-related complexity, including low vegetation signal, mixed-pixel effects, and heterogeneous land-surface composition. These results demonstrate that land-cover-stratified error decomposition and uncertainty-informed priority ranking reveal class-specific projection limitations that aggregate accuracy metrics can conceal. [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.)
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  Data: Highlights: What are the main findings? The study proposes a class-aware framework for SSP-based NDVI projection that integrates projection-oriented model selection, land-cover-stratified validation, uncertainty characterization, and validation-priority ranking. Regional NDVI is projected to increase modestly by 2040, but land-cover-stratified validation showed that global model performance masked weak predictive support for Water bodies and Unused land. What are the implications of the main findings? Future NDVI projections should be evaluated beyond global accuracy metrics because aggregate performance can hide important class-level uncertainty and prediction errors. The framework identifies Water bodies, Unused land, and Construction land as priority classes for targeted validation, supporting more transparent and reliable interpretation of vegetation projections. At 1 km resolution, NDVI projections for heterogeneous landscapes can appear spatially coherent in aggregate while concealing substantial class-level prediction weaknesses, a limitation that has received limited systematic attention in the NDVI projection literature. This study applies a four-component assessment workflow to Northeast China (NEC) for 2040 under SSP1-2.6, SSP2-4.5, and SSP5-8.5, integrating multi-stage model selection, land-cover-stratified validation, quantile-regression-based uncertainty characterization, and validation-priority ranking. Among three candidate tree-based models evaluated using spatial block cross-validation, temporal holdout validation, long-jump extrapolation, and climatic perturbation tests, LightGBM showed the most balanced and consistent performance, with spatial CV R2 = 0.654 ± 0.123, temporal holdout R2 = 0.710, and long-jump R2 = 0.671, and was therefore selected for the 2040 projection. Projected regional mean NDVI increased modestly from 0.393 in 2020 to 0.414–0.417 across scenarios, with limited divergence among SSP pathways at this near-term horizon. Class-stratified validation of the 2020 holdout prediction revealed that global model performance masked strong class-level heterogeneity, with R2 values ranging from 0.576 for Construction land to −0.886 for Unused land. Water bodies and Unused land exhibited negative R2 values, indicating weak class-level predictive support relative to a simple class-mean benchmark. Residual decomposition showed that Water bodies combined high random error with elevated systematic deviation, whereas Unused land was mainly characterized by systematic bias, suggesting different needs for class-specific model improvement. The Uncertainty Risk Index (URI), derived from 95% prediction intervals, was highest in Construction land and lowest in Cropland across all scenarios. Integrating historical residuals with future URI-identified Water bodies, Unused land, and Construction land as the highest-priority classes for future targeted validation. These priorities arise from both limited class representation and intrinsic NDVI-related complexity, including low vegetation signal, mixed-pixel effects, and heterogeneous land-surface composition. These results demonstrate that land-cover-stratified error decomposition and uncertainty-informed priority ranking reveal class-specific projection limitations that aggregate accuracy metrics can conceal. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Group: Ab
  Data: <i>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.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.3390/rs18132203
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 32
        StartPage: 2203
    Subjects:
      – SubjectFull: Model validation
        Type: general
      – SubjectFull: Normalized difference vegetation index
        Type: general
      – SubjectFull: Climate change models
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Manchuria (China)
        Type: general
    Titles:
      – TitleFull: Land-Cover-Stratified Validation and Uncertainty Prioritization for SSP-Based NDVI Projection at 1 km Resolution in Northeast China.
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            NameFull: Rashad, Eslam
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            NameFull: Liu, Yujie
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            – D: 01
              M: 07
              Text: Jul2026
              Type: published
              Y: 2026
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