Evaluation of vegetation indices and imaging spectroscopy to estimate foliar nitrogen across disparate biomes

Farella, Martha M.; Barnes, Mallory L.; Breshears, David D.; Mitchell, Jessica J.; Leeuwen, Willem J. D. van; Gallery, Rachel E.

doi:10.1002/ecs2.3992

Cited by 4 publications

(3 citation statements)

References 93 publications

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“…The estimation of foliar biochemicals has been extensively done using vegetation indices, which combine different spectral reflectance bands. Traditional broadband indices (Normalized Difference Vegetation Index, Modified Simple Ratio, and Soil Adjusted Vegetation Index) have been explored to quantify the biochemical content of plants (Farella et al, 2022 ; Rahman et al, 2020 ). In particular, Gao et al ( 2020 ), Rahman et al ( 2020 ), and Lihong et al ( 2006 ) utilized spectral indices from traditional broadband indices (NDVI, SAVI) in the prediction of the C : N ratio.…”

Section: Influential Spectral Variables In Estimating C ...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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Section: Influential Spectral Variables In Estimating C ...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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“…Vegetation indices were developed based on biophysical and biochemical signals of various absorption features that aid in detecting and mapping vegetation species. Bands selected were derived from previous research based on differences in reflectance and absorption features that represent various plant species [62,63].…”

Section: Indicesmentioning

confidence: 99%

Multi-Temporal LiDAR and Hyperspectral Data Fusion for Classification of Semi-Arid Woody Cover Species

et al. 2022

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Mapping the spatial distribution of woody vegetation is important for monitoring, managing, and studying woody encroachment in grasslands. However, in semi-arid regions, remotely sensed discrimination of tree species is difficult primarily due to the tree similarities, small and sparse canopy cover, but may also be due to overlapping woody canopies as well as seasonal leaf retention (deciduous versus evergreen) characteristics. Similar studies in different biomes have achieved low accuracies using coarse spatial resolution image data. The objective of this study was to investigate the use of multi-temporal, airborne hyperspectral imagery and light detection and ranging (LiDAR) derived data for tree species classification in a semi-arid desert region. This study produces highly accurate classifications by combining multi-temporal fine spatial resolution hyperspectral and LiDAR data (~1 m) through a reproducible scripting and machine learning approach that can be applied to larger areas and similar datasets. Combining multi-temporal vegetation indices and canopy height models led to an overall accuracy of 95.28% and kappa of 94.17%. Five woody species were discriminated resulting in producer accuracies ranging from 86.12% to 98.38%. The influence of fusing spectral and structural information in a random forest classifier for tree identification is evident. Additionally, a multi-temporal dataset slightly increases classification accuracies over a single data collection. Our results show a promising methodology for tree species classification in a semi-arid region using multi-temporal hyperspectral and LiDAR remote sensing data.

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“…Nitrogen is an important element in the plant, and is present in chlorophyll, amino acids and hence protein, nucleic acids, plant tissue, etc (Buchholz, 2022). Traditionally, N level can be obtained via lab chemical processing, however, this process takes time and increases the operational costs (Farella et al, 2022). Furthermore, the sampling results only applies for that set of leaf samples and does not represent the entire crop field.…”

Section: Introductionmentioning

confidence: 99%

Canopy Nitrogen Estimation on Cotton Plant Using Satellite Imagery

Chew¹,

Wiratama²,

Goh³

2023

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. The optimization of nitrogen (N) management is becoming a key challenge to enhance crop yield production while protecting the environment. Analysis of canopy N content in crop plants is used as insights for fertilization management, in which actions can be taken to optimize N fertilizer usage. Traditionally, lab chemical processing is used to measure the crop plant’s nutrient content. However, the collection of leaf samples from the field is labour intensive, and it would be costly to increase sampling frequency. Thus, this approach may not be the most optimal for large plantations. Remote sensing applications in agriculture have been widely studied. This study aims to evaluate the potential of using Sentinel 2 imagery to predict canopy N content, as an alternative wide scale method as compared to traditional methods. A cotton plantation with about 50 square km area in the state of Mato Grosso, Brazil, was used as the case study. About 180 samples across the cotton plantation were collected between March and April 2022 and the N contents of the crop plants were measured using lab chemical processes. Sentinel 2 images within 15 days of the sampling dates were retrieved from ESA’s Copernicus Open Access Hub. This study proposes a Random Forest (RF) regression algorithm for the generation of an N prediction model. About 52 vegetation indices (VIs) were extracted as the features for model training, such as Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI). RF model allows easy measurement of the relative importance of each feature with respect to the prediction to achieve a good performance. Validation is done by using mean absolute error (MAE) and mean absolute percentage error (MAPE) to evaluate the prediction accuracy against the ground truth, which resulted to be 3.418 g/kg and 9.29% respectively. Finally, this study analyses the performance of the canopy N prediction model and assesses its ability as an alternative to traditional lab chemical sampling processes.

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Evaluation of vegetation indices and imaging spectroscopy to estimate foliar nitrogen across disparate biomes

Cited by 4 publications

References 93 publications

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

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

Multi-Temporal LiDAR and Hyperspectral Data Fusion for Classification of Semi-Arid Woody Cover Species

Canopy Nitrogen Estimation on Cotton Plant Using Satellite Imagery

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