Retrieval of Leaf area index and stress conditions for Sundarban mangroves using Sentinel-2 data

Manna, Sudip; Raychaudhuri, Barun

doi:10.1080/01431161.2019.1655174

Cited by 14 publications

(13 citation statements)

References 44 publications

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“…Nevertheless, despite of the strongly significant correlation, it is based only on 14 data points, implying that relationships have to be handled with care. It is a higher correlation than retrieved in previous studies, where near-remotely sensed NDVI data were compared to NDVI from Sentinel-2 and Landsat 8 [59,60]. A likely reason for the very strong correlation is that this study was carried out in a very open subarctic woodland (in parts nearly treeless and then considered as heath) where understory vegetation contributes very much to the NDVI detected by the satellites.…”

Section: Discussionmentioning

confidence: 70%

Monitoring Winter Stress Vulnerability of High-Latitude Understory Vegetation Using Intraspecific Trait Variability and Remote Sensing Approaches

Ritz

Bjerke

Tømmervik

2020

Sensors

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In this study, we focused on three species that have proven to be vulnerable to winter stress: Empetrum nigrum, Vaccinium vitis-idaea and Hylocomium splendens. Our objective was to determine plant traits suitable for monitoring plant stress as well as trait shifts during spring. To this end, we used a combination of active and passive handheld normalized difference vegetation index (NDVI) sensors, RGB indices derived from ordinary cameras, an optical chlorophyll and flavonol sensor (Dualex), and common plant traits that are sensitive to winter stress, i.e. height, specific leaf area (SLA). Our results indicate that NDVI is a good predictor for plant stress, as it correlates well with height (r = 0.70, p < 0.001) and chlorophyll content (r = 0.63, p < 0.001). NDVI is also related to soil depth (r = 0.45, p < 0.001) as well as to plant stress levels based on observations in the field (r = −0.60, p < 0.001). Flavonol content and SLA remained relatively stable during spring. Our results confirm a multi-method approach using NDVI data from the Sentinel-2 satellite and active near-remote sensing devices to determine the contribution of understory vegetation to the total ecosystem greenness. We identified low soil depth to be the major stressor for understory vegetation in the studied plots. The RGB indices were good proxies to detect plant stress (e.g. Channel G%: r = −0.77, p < 0.001) and showed high correlation with NDVI (r = 0.75, p < 0.001). Ordinary cameras and modified cameras with the infrared filter removed were found to perform equally well.

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Section: Discussionmentioning

confidence: 70%

Monitoring Winter Stress Vulnerability of High-Latitude Understory Vegetation Using Intraspecific Trait Variability and Remote Sensing Approaches

Ritz

Bjerke

Tømmervik

2020

Sensors

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“…There is a good quantitative relationship between the NDVI and LAI [ 40 , 41 , 42 ]. First, we calculated the NDVI from the seven selected Sentinel-2 images.…”

Section: Resultsmentioning

confidence: 99%

“…Highly accurate soil moisture values were derived without the effects of vegetation coverage based on Sentinel 1 data and the water cloud model [ 41 , 43 ]. The red and infrared bands of Sentinel-2 images that were used to derive LAI had a spatial resolution of 10 m and a temporal resolution of 5 d. Compared with the commonly used LAI derived from MODIS or Landsat products, LAI derived from Sentinel-2 images had higher accuracy [ 40 , 41 , 44 ].…”

Section: Discussionmentioning

confidence: 99%

Estimation of Winter Wheat Yield in Arid and Semiarid Regions Based on Assimilated Multi-Source Sentinel Data and the CERES-Wheat Model

Liu

Zhou

et al. 2021

Sensors

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The farmland area in arid and semiarid regions accounts for about 40% of the total area of farmland in the world, and it continues to increase. It is critical for global food security to predict the crop yield in arid and semiarid regions. To improve the prediction of crop yields in arid and semiarid regions, we explored data assimilation-crop modeling strategies for estimating the yield of winter wheat under different water stress conditions across different growing areas. We incorporated leaf area index (LAI) and soil moisture derived from multi-source Sentinel data with the CERES-Wheat model using ensemble Kalman filter data assimilation. According to different water stress conditions, different data assimilation strategies were applied to estimate winter wheat yields in arid and semiarid areas. Sentinel data provided LAI and soil moisture data with higher frequency (<14 d) and higher precision, with root mean square errors (RMSE) of 0.9955 m2 m−2 and 0.0305 cm3 cm−3, respectively, for data assimilation-crop modeling. The temporal continuity of the CERES-Wheat model and the spatial continuity of the remote sensing images obtained from the Sentinel data were integrated using the assimilation method. The RMSE of LAI and soil water obtained by the assimilation method were lower than those simulated by the CERES-Wheat model, which were reduced by 0.4458 m2 m−2 and 0.0244 cm3 cm−3, respectively. Assimilation of LAI independently estimated yield with high precision and efficiency in irrigated areas for winter wheat, with RMSE and absolute relative error (ARE) of 427.57 kg ha−1 and 6.07%, respectively. However, in rain-fed areas of winter wheat under water stress, assimilating both LAI and soil moisture achieved the highest accuracy in estimating yield (RMSE = 424.75 kg ha−1, ARE = 9.55%) by modifying the growth and development of the canopy simultaneously and by promoting soil water balance. Sentinel data can provide high temporal and spatial resolution data for deriving LAI and soil moisture in the study area, thereby improving the estimation accuracy of the assimilation model at a regional scale. In the arid and semiarid region of the southeastern Loess Plateau, assimilation of LAI independently can obtain high-precision yield estimation of winter wheat in irrigated area, while it requires assimilating both LAI and soil moisture to achieve high-precision yield estimation in the rain-fed area.

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“…where f is a conceptual function that assigns a value from the DART model, Θ = (θ (1) , θ (2) , θ (3) , θ (4) ) is the set of the unknown parameters [θ (5) corresponding to the soil pixel reflectance, it is not taken into account as it is already pre-calculated for each pixel using the soil classification] and ε models the cumulative error due to uncertainty assessment in mock-up design and observation perturbation. It follows a normal (Gaussian) distribution…”

Section: Inversion Procedures 1) Lut Databasementioning

confidence: 99%

“…In addition, the short revisit interval at moderate latitudes of this satellite offers important temporal information on short-term changes in vegetation across broad regions. Recent research using Sentinel-2 data revealed the sensor's ability to monitor biophysical parameters such as chlorophyll content [3] and leaf area index [4], [5]. Therefore, Sentinel-2 data are deemed relevant for vegetation monitoring, especially for heterogeneous and complex canopies [6].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Retrieval of Olive Tree Properties Using Bayesian Model and Sentinel-2 Images

Abdelmoula¹,

Kallel²,

Roujean³

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The goal of this study is to provide a fine detection and monitoring of olive orchard trees over large areas to anticipate any damage. We developed an original method to assess the spatio-temporal dynamics of biophysical parameters in the olive orchards using satellite observations and radiative transfer (RT) models. Sentinel-2 time series data collected over a four-year period were fused with Planet images from the same time period to enhance the temporal trends in olive orchards in the Sfax region located in southern Tunisia. These images also served to extract soil spectrum variations required by 3-D discrete Anisotropic Radiative Transfer (DART) model to account for canopy background effect. As backward model, we developed an original technique based on the Markov Chain Monte Carlo (MCMC) method that has the advantage of being able to model sensor noise and account for spatial and temporal regularization. It allows retrieving key parameters such as Leaf area index (LAI), chlorophyll (Cab) content, water (Cw) content and mesophyll structure (N). Taking advantage of (i) the Sentinel-2 images downscaled to a moderate resolution of 80 m to ensure representative pixels of the local mixing (i.e. trees and soil); (ii) the appropriate soil signature derived from high spatial and spectral resolution image; and (iii) the accuracy of the direct and inverse modeling, it was possible to retrieve the plant properties even when LAI values are less than 0.14. Indeed, our inversion results show that the estimated parameters are strongly correlated especially with the LAI field measurements with R 2 = 0.9937.

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Retrieval of Leaf area index and stress conditions for Sundarban mangroves using Sentinel-2 data

Cited by 14 publications

References 44 publications

Monitoring Winter Stress Vulnerability of High-Latitude Understory Vegetation Using Intraspecific Trait Variability and Remote Sensing Approaches

Monitoring Winter Stress Vulnerability of High-Latitude Understory Vegetation Using Intraspecific Trait Variability and Remote Sensing Approaches

Estimation of Winter Wheat Yield in Arid and Semiarid Regions Based on Assimilated Multi-Source Sentinel Data and the CERES-Wheat Model

Dynamic Retrieval of Olive Tree Properties Using Bayesian Model and Sentinel-2 Images

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