Aboveground net primary production spatio-temporal changes in the bioclimates of Alborz mountains based on multi-sensor satellite data

Abstract Aboveground Net Primary Production (ANPP) is a key indicator for assessing the health of rangeland ecosystems. This study estimated ANPP in the central Alborz rangelands of Iran from 2000 to 2020 based on satellite data (MOD13Q1.061, Sentinel-2 L1C), ground-based measurements, along with me...

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Main Authors: Marzieh Asgari, Reza Jafari, Mostafa Tarkesh Esfahani, Mahshid Souri
Format: Article
Language:English
Published: Nature Portfolio 2025-07-01
Series:Scientific Reports
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Online Access:https://doi.org/10.1038/s41598-025-08923-1
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Summary:Abstract Aboveground Net Primary Production (ANPP) is a key indicator for assessing the health of rangeland ecosystems. This study estimated ANPP in the central Alborz rangelands of Iran from 2000 to 2020 based on satellite data (MOD13Q1.061, Sentinel-2 L1C), ground-based measurements, along with meteorological data through the Carnegie Ames Stanford Approach model. ANPP trends were ascertained across various bioclimates and vegetation types. A total of 240 sampling sites were selected to measure ANPP using a systematic-random design. Sentinel-2 ANPP values ranged between 2.4 and 44.6 gC/m2 in the study area. The model evaluation, based on the coefficient of determination, indicated a strong relationship between Sentinel-2 derived ANPP and ground data (R² = 0.86, P < 0.01). A significant relationship was also observed between ANPP estimates from the Sentinel-2 and MODIS sensors (R² = 0.8, P < 0.01). The climatic conditions and type of vegetation have a significant impact on rangeland production. The highest annual average ANPP, estimated using MODIS, was 60.57 gC/m2, witnessed in the Psathyrostachys fragilis-Agropyron tauri vegetation type within a humid and cold climate. In contrast, the lowest ANPP, 39.07 gC/m2, was recorded for the Seidlitzia rosmarinus-Artemisia sieberi type in a hyper-arid and cold climate. Generally, the findings demonstrated that integrating modeling approaches with satellite imagery enables robust estimation and analysis of rangeland production dynamics across diverse bioclimates and vegetation types.
ISSN:2045-2322