Satellite-observed greening in arid regions is often interpreted as ecological restoration success, yet this assessment may conflate natural recovery with agricultural expansion. We developed an Arid Remote Sensing Ecological Index (ARSEI) incorporating a Comprehensive Salinity Index (CSI) to address systematic biases in the traditional RSEI when applied to irrigated drylands. ARSEI scores were validated against MODIS Net Primary Production (NPP) ($R^2>0.75$ at the regional scale), confirming its reliability in capturing ecosystem productivity, while CSI effectively maps the upper-bound of surface salinization potential dictated by intrinsic soil properties. Applied to China’s Mu Us Sandy Land (2000–2024), the ARSEI reveals that 2327 km² of sandy land—54% of current cropland—was converted to agriculture, creating “assessment-induced false greening” signals. While the traditional RSEI increased monotonically (+135%), the ARSEI shows a nuanced pattern with plateau (2010–2015) and decline (2015–2020) phases, reflecting salinization risks masked by high crop NDVI. Optimal Parameters-Based Geographical Detector analysis demonstrates that Land Cover × Precipitation interactions (q = 0.28) drive spatial heterogeneity through irrigation-mediated water redistribution. The ARSEI provides a dialectical evaluation framework: acknowledging agricultural greening’s economic benefits while monitoring subsurface degradation risks. This study offers a critical methodological advance for sustainable land assessment in global drylands undergoing agricultural intensification.

Long-term ecological monitoring is essential for sustainable management in fragile regions. This study assessed four decades (1986–2024) of ecological evolution in the Ji River Basin—a transitional loess-gully ecosystem in China's Yellow River Basin—using the Remote Sensing Ecological Index (RSEI). We integrated multi-temporal Landsat images via Google Earth Engine to construct a 40-year RSEI time series, coupling greenness (NDVI), wetness (WET), heat (LST), and dryness (NDBSI) through principal component analysis (PC1 explained >82% variance). Three evolutionary phases emerged: initial degradation (1986–1996) driven by slope cropland expansion; stabilization (1996–2006) coinciding with early Grain for Green policies; and sustained recovery (2006–2024) as high-quality zones expanded 85%. Resilience mapping delineated three functional zones: High-Resilience refugia (16.84%) in highlands with stable RSEI (CV300m) and restoration projects as primary post-2006 drivers, while urbanization constrained lowland recovery. This study demonstrates RSEI's effectiveness for monitoring transitional loess ecosystems and provides a replicable framework for sustainable basin management, contributing to SDG 15 implementation in semi-arid regions globally.

Long-term ecological monitoring is essential for sustainable management in fragile regions. This study assessed four decades (1986–2024) of ecological evolution in the Ji River Basin—a 1276.64 km² transitional loess–gully ecosystem in China’s Yellow River Basin—using the Remote Sensing Ecological Index (RSEI). We integrated multi-temporal Landsat images via Google Earth Engine to construct a 40-year RSEI time series. The index couples greenness (NDVI), wetness (WET), heat (LST), and dryness (NDBSI) through principal component analysis, with PC1 explaining > 82% of the variance. Three evolutionary phases were identified: initial degradation (1986–1996), driven by slope cropland expansion; stabilization (1996–2006), coinciding with early ‘Grain for Green’ policies; and sustained recovery (2006–2024), characterized by the expansion of high-quality zones. We developed a novel resilience zoning framework integrating local spatial consistency, terrain constraints, and functional state (mean RSEI 2016–2024), which delineated three zones: high-resilience refugia (19.37%), moderate-resilience matrix (75.54%), and low-resilience corridors (5.09%). Mid-slope positions (TPI: 1.220–1.510) within moderate-resilience zones demonstrated optimal restoration efficiency, challenging conventional uniform approaches. The findings advocate spatially differentiated strategies—investing in transitional zones, retrofitting degraded corridors, and monitoring stable refugia—to advance the implementation of Sustainable Development Goal 15 in semi-arid regions globally.

This study develops a workflow for forest carbon accounting in the West Qinling Mountains, China (2000–2025), integrating CLCD data, key-year forest subtype mapping, and InVEST-style carbon pools. Forest subtypes were classified with Random Forest under spatial block cross-validation (overall accuracy = 59.76%, Cohen’s Kappa = 0.3974, Macro-F1 = 0.5944). Under the baseline workflow, total carbon stock increased from 876.40 to 954.45 million Mg C, yielding a net gain of 78.05 million Mg C (+8.91%). The Hamed–Rao modified Mann–Kendall test identified a significant increase (Sen’s slope = 2.41 million Mg C year⁻¹, $p=2.99\times {10}^{-5}$). A bounded parameter sensitivity analysis produced a 2025 total carbon range of 887.07–1021.84 million Mg C, whereas Scheme A yielded a more conservative net gain of 60.19 million Mg C. County-level patterns were broadly similar across scenarios, but fine-grained ranking remained uncertain. Because the annual series is a hybrid product, with annual forest/non-forest updates but a subtype structure refreshed only at key years, the results should not be interpreted as an independently reconstructed annual record of subtype transitions. The workflow therefore provides an uncertainty-aware regional accounting framework rather than a fully observed annual reconstruction of historical forest composition.

Mountain transition zones are highly sensitive to environmental change, yet the nonlinear coupling between topography and hydroclimate in controlling vegetation Net Primary Productivity (NPP) remains insufficiently constrained. Here, we reconstructed a 2003–2025 annual NPP time series for the West Qinling Mountains using a Carnegie–Ames–Stanford Approach (CASA)-based workflow that integrated Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation products, fifth-generation European Centre for Medium-Range Weather Forecasts reanalysis for land (ERA5-Land) meteorological data, Shuttle Radar Topography Mission (SRTM) topography, and an Aridity Index (AI) dataset. Product-based validation against the annual MOD17A3HGF dataset indicated strong agreement, with a 23-year mean spatial Spearman correlation of 0.823 and a mean annual Pearson correlation of 0.773. The reconstructed dataset showed that 98.50% of the study area experienced increasing NPP, including 50.26% with significant increases, and the domain-wide mean Sen slope reached approximately 3.08 g C m⁻² yr⁻¹. Factor detection further showed that radiation (q = 0.253), elevation (q = 0.252), and temperature (q = 0.249) were the dominant single controls, whereas Aridity–Temperature (q = 0.367) and Elevation–Aridity (q = 0.367) represented the strongest interactions. The concentration of the strongest gains in gentle-slope and moderate-aridity settings suggests that vegetation recovery is maximized where topographic buffering and water-energy balance are jointly optimized. These results strengthen the interpretation of NPP dynamics in mountainous climate-transition environments and provide a basis for spatially targeted ecological restoration, regional carbon-budget assessment, and climate adaptation planning.