Monitoring the available forage using Sentinel 2-derived NDVI data for sustainable rangeland management

İleri, Onur; Koç, Ali

doi:10.1016/j.jaridenv.2022.104727

Cited by 7 publications

(1 citation statement)

References 35 publications

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“…Durante et al ( 2014 ) and Gao et al ( 2020 ) demonstrated in different studies that bands sensitive to C : N ratio in vegetation are in the SWIR region. Advanced multispectral sensors (Rapid eye, Sentinel 2, and Worldview3) with an improved spatial and temporal resolution with specialized red edge bands have proved invaluable in vegetation studies (İleri & Koç, 2022 ; Sibanda et al, 2017 ; Vasudeva et al, 2021 ). There is a paucity of information on the use of these sensors to predict C : N ratio, as the red and red edge region may improve the accuracy of C : N ratio prediction in rangelands.…”

Section: Challenges and Opportunities In Estimating Foliar ...mentioning

confidence: 99%

The role of remote sensing in tropical grassland nutrient estimation: a review

Arogoundade

Mutanga

Odindi

et al. 2023

Environ Monit Assess

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The carbon (C) and nitrogen (N) ratio is a key indicator of nutrient utilization and limitations in rangelands. To understand the distribution of herbivores and grazing patterns, information on grass quality and quantity is important. In heterogeneous environments, remote sensing offers a timely, economical, and effective method for assessing foliar biochemical ratios at varying spatial and temporal scales. Hence, this study provides a synopsis of the advancement in remote sensing technology, limitations, and emerging opportunities in mapping the C:N ratio in rangelands. Specifically, the paper focuses on multispectral and hyperspectral sensors and investigates their properties, absorption features, empirical and physical methods, and algorithms in predicting the C:N ratio in grasslands. Literature shows that the determination of the C:N ratio in grasslands is not in line with developments in remote sensing technologies. Thus, the use of advanced and freely available sensors with improved spectral and spatial properties such as Sentinel 2 and Landsat 8/9 with sophisticated algorithms may provide new opportunities to estimate C:N ratio in grasslands at regional scales, especially in developing countries. Spectral bands in the near-infrared, shortwave infrared, red, and red edge were identified to predict the C:N ratio in plants. New indices developed from recent multispectral satellite imagery, for example, Sentinel 2 aided by cutting-edge algorithms, can improve the estimation of foliar biochemical ratios. Therefore, this study recommends that future research should adopt new satellite technologies with recent development in machine learning algorithms for improved mapping of the C:N ratio in grasslands.

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