Dataset Information

Chlorophyll content estimation in an open-canopy conifer forest with Sentinel-2A and hyperspectral imagery in the context of forest decline.

ABSTRACT: With the advent of Sentinel-2, it is now possible to generate large-scale chlorophyll content maps with unprecedented spatial and temporal resolution, suitable for monitoring ecological processes such as vegetative stress and/or decline. However methodological gaps exist for adapting this technology to heterogeneous natural vegetation and for transferring it among vegetation species or plan functional types. In this study, we investigated the use of Sentinel-2A imagery for estimating needle chlorophyll (Ca+b) in a sparse pine forest undergoing significant needle loss and tree mortality. Sentinel-2A scenes were acquired under two extreme viewing geometries (June vs. December 2016) coincident with the acquisition of high-spatial resolution hyperspectral imagery, and field measurements of needle chlorophyll content and crown leaf area index. Using the high-resolution hyperspectral scenes acquired over 61 validation sites we found the CI chlorophyll index R750/R710 and Macc index (which uses spectral bands centered at 680?nm, 710?nm and 780?nm) had the strongest relationship with needle chlorophyll content from individual tree crowns (r2?=?0.61 and r2?=?0.59, respectively; p?0.7 for June and >0.4 for December; p?

SUBMITTER: Zarco-Tejada PJ

PROVIDER: S-EPMC6472624 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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Chlorophyll content estimation in an open-canopy conifer forest with Sentinel-2A and hyperspectral imagery in the context of forest decline.

Zarco-Tejada P J PJ Hornero A A Beck P S A PSA Kattenborn T T Kempeneers P P Hernández-Clemente R R

Remote sensing of environment 20190301

With the advent of Sentinel-2, it is now possible to generate large-scale chlorophyll content maps with unprecedented spatial and temporal resolution, suitable for monitoring ecological processes such as vegetative stress and/or decline. However methodological gaps exist for adapting this technology to heterogeneous natural vegetation and for transferring it among vegetation species or plan functional types. In this study, we investigated the use of Sentinel-2A imagery for estimating needle chlo ...[more]

PMID: 31007289

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Project description:Accurate estimations of the vertical leaf nitrogen (N) distribution within a rice canopy is helpful for understanding the nutrient supply and demand of various functional leaf layers of rice and for improving the predictions of rice productivity. A two-year field experiment using different rice varieties, N rates, and planting densities was performed to investigate the vertical distribution of the leaf nitrogen concentration (LNC, %) within the rice canopy, the relationship between the LNC in different leaf layers (LNCLi, i = 1, 2, 3, 4), and the relationship between the LNCLi and the LNC at the canopy level (LNCCanopy). A vertical distribution model of the LNC was constructed based on the relative canopy height. Furthermore, the relationship between different vegetation indices (VIs) and the LNCCanopy, the LNCLi, and the LNC vertical distribution model parameters were studied. We also compared the following three methods for estimating the LNC in different leaf layers in rice canopy: (1) estimating the LNCCanopy by VIs and then estimating the LNCLi based on the relationship between the LNCLi and LNCCanopy; (2) estimating the LNC in any leaf layer of the rice canopy by VIs, inputting the result into the LNC vertical distribution model to obtain the parameters of the model, and then estimating the LNCLi using the LNC vertical distribution model; (3) estimating the model parameters by using VIs directly and then estimating the LNCLi by the LNC vertical distribution model. The results showed that the LNC in the bottom of rice canopy was more susceptible to different N rates, and changes in the LNC with the relative canopy height could be simulated by an exponential model. Vegetation indices could estimate the LNC at the top of rice canopy. R705/(R717+R491) (R2 = 0.763) and the renormalized difference vegetation index (RDVI) (1340, 730) (R2 = 0.747) were able to estimate the parameter "a" of the LNC vertical distribution model in indica rice and japonica rice, respectively. In addition, method (2) was the best choice for estimating the LNCLi (R2 = 0.768, 0.700, 0.623, and 0.549 for LNCL1, LNCL2, LNCL3, and LNCL4, respectively). These results provide technical support for the rapid, accurate, and non-destructive identification of the vertical distribution of nitrogen in rice canopies.

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Dataset Information

Chlorophyll content estimation in an open-canopy conifer forest with Sentinel-2A and hyperspectral imagery in the context of forest decline.

Publications

Chlorophyll content estimation in an open-canopy conifer forest with Sentinel-2A and hyperspectral imagery in the context of forest decline.

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