Integrating Sentinel-2 Imagery and PHYGROW Model for Biomass Estimation in Arid Rangelands of Northern Jordan

A study conducted in a northern Jordanian arid Mediterranean grassland between 2017 and 2021 examined the relationship between remotely sensed Normalized Difference Vegetation Index (NDVI) and modeled standing crop biomass. The research sought to determine the utility of high-resolution (10-meter) Sentinel-2 imagery, coupled with the PHYGROW model, for biomass estimation in this challenging environment, and to assess the potential of NDVI as a cost-effective alternative to traditional ground-based methods. Data were aggregated into 10-day intervals for temporal analysis. Results indicated a significant positive correlation (p < 0.001) between NDVI and standing crop (kg/ha), described by the linear model: Standing crop = 60.40 + 3567.56 × NDVI (R² = 0.52). This finding suggests that NDVI offers a reliable and time effective approach to biomass estimation in such settings.

The strong positive correlation between NDVI and standing crop highlights the potential of remote sensing for large-scale rangeland health monitoring. Tracking NDVI changes over time provides insight into vegetation responses to climate, grazing, and conservation efforts. This understanding supports decision-making for sustainable grazing, water management, and conservation strategies. Future research should validate these findings on larger scales and explore integrating NDVI with other data, like soil moisture, to refine predictive models and improve accuracy. The study advocates adopting NDVI-based monitoring in arid rangeland management.