Xinyao Xie

The increasing frequency of drought, heat stress and extreme precipitation is intensifying risks to agricultural systems in the Southern Slopes of the Himalaya (SSH) under climate change. Understanding crop loss responses across cropping systems is critical for regional food security. Yet, which extremes dominate productivity loss and how their risk patterns differ across systems remain unclear. Across the three climate extremes considered, loss probability dominated crop productivity risk in 77% of the study area. Double cropping showed a probability dominated vulnerability pattern, whereas single cropping exhibited a magnitude amplified pattern. Drought remained the dominant regional risk source, with its contribution declining as extreme precipitation and heat stress became more important. Furthermore, loss probability had lower thresholds than loss magnitude, indicating a nonlinear two stage process from risk triggering to damage amplification. Thus, agricultural risk in the SSH is structurally driven by cropping systems and multiple climate extremes.

Current machine learning-based gross primary productivity (GPP) upscaling error correction approaches exhibit two critical limitations: (1) failure to account for nonuniform density distributions of sub-pixel heterogeneity factors during upscaling and (2) dependence on subjective classification thresholds for characterizing factor variations. These shortcomings reduce accuracy and limit transferability. To address these issues, we propose an equidistant sampling method with optimized step size and intervals that precisely quantifies nonuniform density distributions and enhances correction precision. We validate our approach by applying it to correct 480 m resolution GPP simulations generated from an eco-hydrological model, with performance evaluation against 30 m resolution benchmarks using determination coefficient (R²) and root mean square error (RMSE). The proposed method demonstrates a significant improvement over previous elevation-based correction research (baseline R² = 0.48, RMSE = 285 gCm⁻²yr⁻¹), achieving a 0.27 increase in R² and 91.22 gCm⁻²yr⁻¹ reduction in RMSE. For comparative analysis, we implement k-means clustering as an alternative geostatistical method, which shows lesser improvements (ΔR² = 0.21, ΔRMSE = −63.54 gCm⁻²yr⁻¹). Crucially, when using identical statistical interval counts, our optimized-step equidistant sampling method consistently surpasses k-means clustering in performance metrics. The optimal-step equidistant sampling method, paired with appropriate interval selection, offers an efficient solution that maintains high correction accuracy while minimizing computational costs. Controlled variable experiments further revealed that the most significant factors affecting GPP upscaling error correction are land cover, altitude, slope, and TNI, trailed by LAI, whereas slope orientation, SVF, and TWI hold equal relevance.

Fine-resolution estimation of gross primary production (GPP) is essential for advancing our knowledge of ecosystem carbon cycling. However, most existing global GPP products are constrained by coarse temporal resolutions (typically ≥ 8 days), limiting their capacity to capture short-term variations in ecosystem productivity. This paper presents a global new GPP dataset for 2001–2020, based on a modified radiation scalar two-leaf LUE (RTL-LUE) model. The RTL-LUE GPP dataset is generated at an hourly temporal resolution and a spatial resolution of 0.1°, driven by the hourly climate data from ERA5-land, GLASS leaf area index, MODIS land cover, and NOAA atmospheric CO₂ concentration. During 2001–2020, this dataset provides a slightly lower global total GPP (124.77 PgC/yr) than the 8-day TL-LUE GPP dataset (126.92 PgC/yr), primarily due to its ability to capture short-term extreme stresses more effectively. Notably, this dataset reveals that annual GPP variability can reach up to approximately 0.10 g C/m² /h at hourly scales. The RTL-LUE GPP demonstrates robust performance at 184 towers, thereby improving insights into the temporal dynamics of carbon fluxes.

Fine and new DEM of riparian zone is emergently required for hydrological and environmental modeling. With the effects of water fluctuation and submersion, acquiring DEM in the riparian zone is difficult. In this study, a self-adaptive ROC (Receiver Operating Characteristic) method was proposed to generate the DEM of riparian zone with the aid of water fluctuation (called as ROC DEM). The water extent acts as an altimeter to measure the altitude with corresponding water level. The elevation of each pixel was the optimal threshold to determine whether it was covered with water or not. With the fluctuation in water, the time-series labels of land or water and the corresponding water level of each pixel were recorded. Finally, the elevation of each pixel was obtained by acquiring the most optimal classification threshold with the ROC algorithm. The proposed method skillfully transformed the problem of acquiring the elevation of the riparian zone into a 2-class classification problem. The riparian zone in the Dongting Lake was considered as the study area to test the proposed method with the time-series Sentinel-1 SAR images and the corresponding water level. The proposed method is feasible to obtain the DEM and its results is more consistent with the actual topography comparing to other DEM products. The values of R2 between the ROC DEM and GLAS and field altitude reach to 0.7 and 0.9 respectively. This paper presents an alternate method for acquiring the topography in the riparian zone and tidal flat.

Accurate estimation of Gross Primary Productivity (GPP) is crucial for understanding terrestrial carbon cycles and assessing ecosystem health. Light Use Efficiency (LUE) models, which are widely used for the generation of regional or global GPP products, often rely on Fraction of Absorbed Photosynthetically Active Radiation (FAPAR). However, most existing FAPAR products with moderate to coarse spatial resolutions introduce uncertainties in GPP estimations across heterogeneous landscapes. In this work, the MODerate resolution Imaging Spectroradiometer (MODIS) FAPAR product at the 500-m resolution, along with a newly HIgh spatial resolution Global LAnd Surface Satellite (Hi-GLASS) FAPAR dataset at the 30-m resolution, was used to drive a LUE model for GPP estimations at 188 eddy covariance (EC) sites. Then, they were compared and evaluated based on the EC GPP measurements. Results showed that Hi-GLASS FAPAR provided the GPP estimates with more detailed spatial information compared to MODIS FAPAR. Moreover, the Hi-GLASS FAPAR significantly improved GPP estimations, with an overall R2 increase from 0.54 (MODIS) to 0.63 (Hi-GLASS) and an RMSE decrease from 3.04 to 2.70 gC m−2 day−1. Additionally, 75% of the selected sites exhibited enhanced R² values with Hi-GLASS FAPAR, demonstrating its application potential in GPP estimations across different vegetation types. Specifically, crop sites exhibited the most notable improvements, with an R² increase of 0.16 and an RMSE decrease of 0.70 gC m−2 day−1. These findings highlight the advantages of high-resolution FAPAR data in capturing spatial heterogeneity and improving the accuracy of GPP estimations, and underscore its potential for refined ecosystem monitoring.

Over the past four decades, continuous afforestation in the river valley of the Yarlung Zangbo Middle Reach has effectively migrated the land degradation and enhanced the local eco‐environment. Different orientations of north and south banks (i.e., sun‐facing and shade‐facing) generate microclimates, but the diverse growth patterns of the planted forests along different banks remain unknown. In this study, long‐term Landsat observations were introduced to investigate the microclimatic effects on the growth patterns of planted forests and their subsequent effects on the land restoration during 1988–2020. Results showed that the south bank experienced a higher afforestation‐induced greening trend than the north bank, as indicated by normalized difference vegetation index (NDVI) rates of 0.0117 and 0.0081 year⁻¹, respectively. The growth of planted forests on the north bank was more sensitive to the distance from riverway than that on the south bank, with the NDVI gradients of −0.0708 and −0.0540 km⁻¹, highlighting a server drought stress on the north bank. Benefited from the continuous afforestation, 79% and 95% of sandy lands on the north and south banks had been restored by 2020. In summary, the microclimatic difference between banks, attributed to different solar illuminations and drought stresses, induce the diverse ecological restoration conditions. These findings offer important insights that future afforestation strategies should consider the microclimatic effect to maximize ecological benefits.

Light use efficiency (LUE) models, along with satellite‐based vegetation maps and climatic reanalysis products as drivers, are effective tools for estimating large‐scale gross primary productivity (GPP). However, the climate‐induced uncertainty in LUE‐based GPP remains poorly understood, particularly the temporal scaling uncertainty from ignored climatic fluctuations. Here, two 1‐hourly reanalysis products, along with site‐based observations, were employed to drive a two‐leaf LUE model at 194 eddy‐covariance (EC) sites. The observation‐driven and reanalysis‐driven GPP at the 1‐hourly resolution were evaluated against EC GPP to illustrate the uncertainty from reanalysis products, with mean absolute deviation (MAD) and Nash‐Sutcliffe efficiency (NSE) as criterion. Moreover, the climate‐induced temporal scaling uncertainty was characterized by comparing distributed GPP (modeled with 1‐hourly resolution climatic drivers) and lumped GPP (modeled with 6‐hourly resolution climatic drivers). At the 1‐hourly resolution, results demonstrated that the reanalysis‐driven GPP showed a weaker relationship with EC GPP (MAD = 0.14 gC m ⁻² h ⁻¹ , NSE = 0.48) than the observation‐driven GPP (MAD = 0.12 gC m ⁻² h ⁻¹ , NSE = 0.60), confirming the nonnegligible climate‐induced uncertainty from reanalysis products. Additionally, the climate‐induced uncertainty arising from gridded radiation was found to be significantly larger than that associated with temperature and vapor pressure deficit (VPD). At the 6‐hourly resolution, both the observation‐driven and reanalysis‐driven lumped GPP exhibited a lower relationship with EC GPP (MAD = 0.63 gC m ⁻² 6h ⁻¹ , NSE = 0.54) than the corresponding distributed GPP (MAD = 0.57 gC m ⁻² 6h ⁻¹ , NSE = 0.59), demonstrating that the climate‐induced temporal scaling uncertainty in 6‐hourly GPP estimates was significantly apparent. This study emphasizes the imperative to refine reanalysis products for more precise capture of short‐term fluctuations and to reduce scaling uncertainties in coarse temporal resolution GPP estimates.