Multi-source remote sensing and machine learning reveal spatiotemporal variations and drivers of NPP in the Tianshan Mountains, China

doi:10.1016/j.jaridl.2026.01.006

Journal of Arid Land

2026, Vol. 18

Issue (1): 56-83 DOI: 10.1016/j.jaridl.2026.01.006 CSTR: 32276.14.JAL.20250380

Research article

Multi-source remote sensing and machine learning reveal spatiotemporal variations and drivers of NPP in the Tianshan Mountains, China

LI Jiani¹^,²^,³^,⁴, XU Denghui¹^,²^,³^,⁴, XU Zhonglin¹^,²^,³^,⁴, WANG Yao¹^,⁵^,^*(

), YANG Jianjun¹^,²^,³^,⁴

¹College of Ecology and Environment, Xinjiang University, Urumqi 830017, China
²Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi 830017, China
³Xinjiang Jinghe Observation and Research Station of Temperate Desert Ecosystem, Ministry of Education, Urumqi 830017, China
⁴Technology Innovation Center for Ecological Monitoring and Restoration of Desert-Oasis, Urumqi 830001, China
⁵Institute of Desert Meteorology, China Meteorological Administration, Urumqi 830002, China

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Abstract

Arid mountain ecosystems are highly sensitive to hydrothermal stress and land use intensification, yet where net primary productivity (NPP) degradation is likely to persist and what drives it remain unclear in the Tianshan Mountains of Northwest China. We integrated multi-source remote sensing with the Carnegie-Ames-Stanford Approach (CASA) model to estimate NPP during 2000-2020, assessed trend persistence using the Hurst exponent, and identified key drivers and nonlinear thresholds with Extreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP). Total NPP averaged 55.74 Tg C/a and ranged from 48.07 to 65.91 Tg C/a from 2000 to 2020, while regional mean NPP rose from 138.97 to 160.69 g C/(m²•a). Land use transfer analysis showed that grassland expanded mainly at the expense of unutilized land and that cropland increased overall. Although NPP increased across 64.11% of the region during 2000-2020, persistence analysis suggested that 53.93% of the Tianshan Mountains was prone to continued NPP decline, including 36.41% with significant projected decline and 17.52% with weak projected decline; these areas formed degradation hotspots concentrated in the central and northern Tianshan Mountains. In contrast, potential improvement was limited (strong persistent improvement: 4.97%; strong anti-persistent improvement: 0.36%). Driver attribution indicated that land use dominated NPP variability (mean absolute SHAP value=29.54%), followed by precipitation (16.03%) and temperature (11.05%). SHAP dependence analyses showed that precipitation effects stabilized at 300.00-400.00 mm, and temperature exhibited an inverted U-shaped response with a peak near 0.00°C. These findings indicated that persistent degradation risk arose from hydrothermal constraints interacting with land use conversion, highlighting the need for threshold-informed, spatially targeted management to sustain carbon sequestration in arid mountain ecosystems.

Key words： net primary productivity (NPP) Carnegie-Ames-Stanford Approach (CASA) Hurst exponent land use change Extreme Gradient Boosting (XGBoost) SHapley Additive exPlanations (SHAP) hydrothermal thresholds

Received: 25 September 2025 Published: 31 January 2026

Corresponding Authors: ^*WANG Yao (E-mail: wangyao@idm.cn)

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Cite this article:

LI Jiani, XU Denghui, XU Zhonglin, WANG Yao, YANG Jianjun. Multi-source remote sensing and machine learning reveal spatiotemporal variations and drivers of NPP in the Tianshan Mountains, China. Journal of Arid Land, 2026, 18(1): 56-83.

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http://jal.xjegi.com/10.1016/j.jaridl.2026.01.006 OR http://jal.xjegi.com/Y2026/V18/I1/56

Fig. 1 Elevation (a) and land use type spatial distribution (b) in the Tianshan Mountains in Xinjiang Uygur Autonomous Region, China. DEM, digital elevation model.

Table 1 Data sources

Fig. 2 Flowchart of the analytical workflow. MODIS, Moderate Resolution Imaging Spectroradiometer; NDVI, normalized difference vegetation index; NPP, net primary productivity; CASA, Carnegie-Ames-Stanford Approach; CV, coefficient of variation; XGBoost, Extreme Gradient Boosting; SHAP, SHapley Additive exPlanations.

Table S1 Consistency of Built% and Crop% among land-cover products at the 6 km grid scale

Fig. S1 Inter-product scatter plots of Built% and Crop% at the 6 km grid. The 1:1 line and ±5.00% bands are shown. (a), Built% compared between IGBP and CLCD; (b), Built% compared between IGBP and LUCC; (c), Built% compared between CLCD and LUCC; (d), Crop% compared between IGBP and CLCD; (e), Crop% compared between IGBP and LUCC; (f), Crop% compared between CLCD and LUCC. IGBP refers to the International Geosphere-Biosphere Programme (IGBP) land-cover classification scheme used by the MODIS land-cover product (MCD12Q1), LUCC refers to the Land Use and Cover Change (LUCC) dataset, and CLCD refers to the China Land Cover Dataset (CLCD).

Fig. S2 Differences between the National Tibetan Plateau Data Center (NTPDC) temperature/precipitation (1 km) and TerraClimate temperature/precipitation (4 km resampled to 1 km) in 2020. (a), pixel-wise temperature difference; (b), pixel-wise precipitation difference; (c), grid-level temperature difference aggregated to the analysis grid; (d), grid-level precipitation difference aggregated to the analysis grid.

Table 2 Significance level and corresponding parameters for NPP trend

Table 3 Classification of future trends and corresponding parameters

Fig. 3 Area (a-c), amplitude (d), and percentage (e) variations of different land use types in the Tianshan Mountains from 2000 to 2020

Fig. 4 Comparison of CASA NPP with MODIS NPP (a) and NPP of field sampling data (b)

Table S2 Year-specific consistency between TerraClimate and National Tibetan Plateau Data Center (NTPDC) meteorological datasets at the 6 km grid

Fig. 5 Temporal variation of NPP in the Tianshan Mountains during 2000-2020. (a), total NPP; (b), annual mean NPP of different land use types; (c), average NPP in representative years; (d), area proportion of NPP classes. "Others" in Figure 5b include water body and unutilized land.

Table 4 Transmission matrix of NPP variations between 2000 and 2020 (Unit: ×10⁹ g C)

Fig. 6 Spatial patterns of NPP and its variability and trends in the Tianshan Mountains during 2000-2020. (a), multi-year mean NPP; (b), CV of annual NPP; (c), Theil-Sen trend slope of NPP; (d), significance level of NPP trends based on the Mann-Kendall test. ESR, extremely significant reduction; SR, significant reduction; NSC, no significant change; SI, significant increase; ESI, extremely significant increase. Inserted pie charts in Figure 6c and d show the area proportions of the corresponding categories.

Fig. 7 Spatial distribution of the Hurst exponent (a) and projected future NPP trend types (b) in the Tianshan Mountains. SPD, strong persistent degradation; WPD, weak persistent degradation; WAPD, weak anti-persistent degradation; SAPD, strong anti-persistent degradation; SPI, strong persistent improvement; WAPI, weak anti-persistent improvement; MAPI, moderate anti-persistent improvement; SAPI, strong anti-persistent improvement; NSC, no significant change.

Fig. 8 SHAP-based attribution of factors influencing NPP in the Tianshan Mountains. (a), SHAP summary (beeswarm) plot for the 15 predictors; (b), SHAP feature importance ranked by mean absolute SHAP values. X1, land use; X2, temperature; X3, precipitation; X4, DEM; X5, slope; X6, aspect; X7, population density; X8, gross domestic productivity (GDP) density; X9, nighttime light index; X10, human footprint; X11, grazing intensity; X12, distance to road; X13, distance to highway; X14, distance to water body; X15, distance to government center.

Fig. 9 SHAP dependence plots showing nonlinear responses of NPP to the top six drivers in the Tianshan Mountains during 2000-2020. (a), land use (X1); (b), precipitation (X3); (c), temperature (X2); (d), population density (X7); (e), DEM (X4); (f), grazing intensity (X11). Points show SHAP values for each sample, and the red curve indicates the locally estimated scatterplot smoothing (LOESS)-smoothed trend.

Fig. 10 Interaction plots of factors influencing NPP. The color gradient represents the feature values of the driving factors, of which red indicates high values, while blue represents low values.

Table S3 Confusion matrix between human disturbance four-type hydrothermal-regime clustering (HD4) and the rule-based classification (6 km grids; n=10,368)

Fig. S3 Spatial distribution of human disturbance four-type hydrothermal-regime clustering (HD4; a) and rule-based classification in 2020. I corresponds to grids dominated by high-altitude or desert landscapes with minimal human impact; II represents urban-industrial settings characterized by high imperviousness and elevated population density and GDP; III denotes agricultural-irrigated grids dominated by cropland and irrigation; and IV corresponds to pastoral-grazing settings where grassland use and grazing pressure are prevalent. The HD4 derives the four types via clustering based on hydrothermal conditions and human-pressure proxies, whereas the rule-based scheme assigns the same types using predefined thresholds of Built%, Crop%, grazing intensity, population density, and GDP.

Fig. S4 Near-term ecosystem productivity risk in the Tianshan Mountains in 2024 at the 2 km grid. (a), synergy hotspot; (b), high-risk grid distribution; (c), NPP low tail. Synergy hotspots are grids with NPP in the lowest 30.00% of all 2 km grids, together with hydroclimatic stress (precipitation in the lowest 40.00% or temperature in the highest 40.00%) and elevated human pressure (population density in the highest 30.00% or a built-up proportion of at least 10.00%). High-risk grids are synergy hotspots where hydroclimatic conditions further deviate from the identified safe ranges. Specifically, precipitation falls below 300.00 mm or exceeds 400.00 mm, or temperature falls below -5.00°C or exceeds 5.00°C. NPP low tail includes grids with NPP in the lowest 30.00% only, regardless of hydroclimatic or human-pressure conditions. The 30.00%, 40.00%, and 30.00% thresholds are percentile-based cutoffs computed across all 2 km grids, whereas the 10.00% built-up cutoff is an absolute threshold.


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