scholarly journals Remote Sensing of Grassland Biophysical Parameters in the Context of the Sentinel-2 Satellite Mission

2016 ◽  
Vol 2016 ◽  
pp. 1-16 ◽  
Author(s):  
Karolina Sakowska ◽  
Radoslaw Juszczak ◽  
Damiano Gianelle
Keyword(s):  
Photosynthetically Active Radiation ◽  
Solar Spectrum ◽  
Added Value ◽  
Estimation Accuracy ◽  
Least Squares Regression ◽  
Satellite Mission ◽  
Biophysical Parameters ◽  
Active Radiation ◽  
Reflectance Data ◽  
Sentinel 2

This study investigates the potential of the Sentinel-2 satellite for monitoring the seasonal changes in grassland total canopy chlorophyll content (CCC), fraction of photosynthetically active radiation absorbed by the vegetation canopy (FAPAR), and fraction of photosynthetically active radiation absorbed only by its photosynthesizing components (GFAPAR). Reflectance observations were collected on a continuous basis during growing seasons by means of a newly developed ASD-WhiteRef system. Two models using Sentinel-2 simulated data (linear regression-vegetation indices (VIs) approach and multiple regression (MR) reflectance approach) were tested to estimate vegetation biophysical parameters. To assess whether the use of full solar spectrum reflectance data is able to provide an added value in CCC and GFAPAR estimation accuracy, a third model based on partial least squares regression (PLSR) and the ASD-WhiteRef reflectance data was tested. The results showed that FAPAR remained quite stable during the reproduction and senescence stages, and no significant relationships between FAPAR and VIs were found. On the other hand, GFAPAR showed clearer seasonal trends. The comparison of the three models revealed no significant differences in the accuracies of CCC and GFAPAR predictions and demonstrated a strong contribution of SWIR bands to the explained variability of investigated parameters. The promising results highlight the potential of the Sentinel-2 satellite for retrieving biophysical parameters from space.

scholarly journals The application of APEX images in the assessment of the state of non-forest vegetation in the Karkonosze Mountains

2016 ◽  
Vol 20 (1) ◽  
pp. 21-27 ◽  
Author(s):  
Anna M. Jarocińska ◽  
Monika Kacprzyk ◽  
Adriana Marcinkowska-Ochtyra ◽  
Adrian Ochtyra ◽  
Bogdan Zagajewski ◽  
...  
Keyword(s):  
Photosynthetically Active Radiation ◽  
Vegetation Indices ◽  
Good Condition ◽  
Biosphere Reserve ◽  
Plant Structure ◽  
Biophysical Parameters ◽  
Area Index ◽  
Active Radiation ◽  
Natural Grasslands ◽  
Absorbed Photosynthetically Active Radiation

Abstract Information about vegetation condition is needed for the effective management of natural resources and the estimation of the effectiveness of nature conservation. The aim of the study was to analyse the condition of non-forest mountain communities: synanthropic communities and natural grasslands. UNESCO’s M&B Karkonosze Transboundary Biosphere Reserve was selected as the research area. The analysis was based on 40 field test polygons and APEX hyperspectral images. The field measurements allowed the collection of biophysical parameters - Leaf Area Index (LAI), fraction of Absorbed Photosynthetically Active Radiation (fAPAR) and chlorophyll content - which were correlated with vegetation indices calculated using the APEX images. Correlations were observed between the vegetation indices (general condition, plant structure) and total area of leaves (LAI), as well as fraction of Absorbed Photosynthetically Active Radiation (fAPAR). The outcomes show that the non-forest communities in the Karkonosze are in good condition, with the synanthropic communities characterised by better condition compared to the natural communities.

scholarly journals Characterisation of Functional-Trait Dynamics at High Spatial Resolution in a Mediterranean Forest from Sentinel-2 and Ground-Truth Data

2018 ◽  
Vol 10 (12) ◽  
pp. 1874
Author(s):  
Santiago Schauman ◽  
Aleixandre Verger ◽  
Iolanda Filella ◽  
Josep Peñuelas
Keyword(s):  
Land Cover ◽  
Spatial Heterogeneity ◽  
Photosynthetically Active Radiation ◽  
Functional Trait ◽  
Mediterranean Forest ◽  
Active Radiation ◽  
Vegetation Activity ◽  
Spatial Autoregressive ◽  
Topographic Aspect ◽  
Sentinel 2

The characterisation of functional-trait dynamics of vegetation from remotely sensed data complements the structural characterisation of ecosystems. In this study we characterised for the first time the spatial heterogeneity of the intra-annual dynamics of the fraction of absorbed photosynthetically active radiation (FAPAR) as a functional trait of the vegetation in Prades Mediterranean forest in Catalonia, Spain. FAPAR was derived from the Multispectral Instrument (MSI) on the Sentinel-2 satellite and validated by comparison with the ground measurements acquired in June 2017 at the annual peak of vegetation activity. The validation results showed that most of points were distributed along the 1:1 line, with no bias nor scattering: R2 = 0.93, p < 0.05; with a root mean square error of 0.03 FAPAR (4.3%). We classified the study area into nine vegetation groups with different dynamics of FAPAR using a methodology that is objective and repeatable over time. This functional classification based on the annual magnitude (FAPAR-M) and the seasonality (FAPAR-CV) from the data on one year (2016–2017) complements structural classifications. The internal heterogeneity of the FAPAR dynamics in each land-cover type is attributed to the environmental and to the specific species composition variability. A spatial autoregressive (SAR) model for the main type of land cover, evergreen holm oak forest (Quercus ilex), indicated that topographic aspect, slope, height, and the topographic aspect x slope interaction accounted for most of the spatial heterogeneity of the functional trait FAPAR-M, thus improving our understanding of the explanatory factors of the annual absorption of photosynthetically active radiation by the vegetation canopy for this ecosystem.

scholarly journals Attenuation of ultraviolet radiation and photosynthetically active radiation in six Yunnan Plateau lakes of China based on seasonal field investigations

2020 ◽  
Author(s):  
Weilu Wang ◽  
Xuan Yang ◽  
Licheng Huang ◽  
Jiang Qin ◽  
Qichao Zhou
Keyword(s):  
Ultraviolet Radiation ◽  
Photosynthetically Active Radiation ◽  
Trophic State ◽  
Solar Spectrum ◽  
Lake Dianchi ◽  
Factors Affecting ◽  
Active Radiation ◽  
Yunnan Plateau ◽  
Field Investigations ◽  
Lake Fuxian

Solar radiation is a primary driver affecting several physical, chemical and biological processes in lake ecosystems. The attenuation of sunlight in water is directly controlled by optically active substances. Here, the seasonal and interlake heterogeneities of the diffuse attenuation coefficients (Kd(λ)) of ultraviolet radiation (UVR) and photosynthetically active radiation (PAR) were studied based on field investigations in six Yunnan Plateau lakes (i.e., Chenghai, Dianchi, Erhai, Fuxian, Lugu and Yangzong) of China, October 2014‒July 2016. The results revealed that Kd(λ) generally increased with decreasing wavelength and increasing trophic state and that Kd(UVR) presented higher interlake heterogeneity than Kd(PAR). The interlake heterogeneity surpassed the seasonal heterogeneity of Kd(λ), whereas the intralake seasonal heterogeneity, which is related to the lake trophic state and solar spectrum, was obvious. Although the main factors affecting Kd(λ) were chromophoric dissolved organic matter (CDOM) and phytoplankton in general, the interlake heterogeneity was found. In eutrophic, turbid shallow Lake Dianchi, CDOM primarily affected UV-B, whereas total suspended solids (TSS) and/or phytoplankton had important effects on Kd(UV-B), Kd(UV-A) and Kd(PAR). CDOM, TSS and phytoplankton influenced the Kd(UV-B), Kd(UV-A) and Kd(PAR) in the deep mesotrophic Lake Chenghai and Lake Erhai, but the main particulate factors were different between these two lakes. In the deep, oligotrophic clear Lake Fuxian and Lake Lugu, only the significant effect of CDOM on Kd(UVR) in Lake Fuxian was detected. Additionally, the factors affecting Kd(λ) in Lake Yangzong were atypical, possibly due to the artificial addition of massive amounts of ferric chloride.

scholarly journals Towards Predictive Modeling of Sorghum Biomass Yields Using Fraction of Absorbed Photosynthetically Active Radiation Derived from Sentinel-2 Satellite Imagery and Supervised Machine Learning Techniques

Agronomy ◽  
2019 ◽  
Vol 9 (4) ◽  
pp. 203 ◽  
Author(s):  
Ephrem Habyarimana ◽  
Isabelle Piccard ◽  
Marcello Catellani ◽  
Paolo De Franceschi ◽  
Michela Dall’Agata
Keyword(s):  
Machine Learning ◽  
Predictive Modeling ◽  
Aboveground Biomass ◽  
Photosynthetically Active Radiation ◽  
Supervised Machine Learning ◽  
Policy Makers ◽  
Active Radiation ◽  
Biomass Yields ◽  
Absorbed Photosynthetically Active Radiation ◽  
Sentinel 2

Sorghum crop is grown under tropical and temperate latitudes for several purposes including production of health promoting food from the kernel and forage and biofuels from aboveground biomass. One of the concerns of policy-makers and sorghum growers is to cost-effectively predict biomass yields early during the cropping season to improve biomass and biofuel management. The objective of this study was to investigate if Sentinel-2 satellite images could be used to predict within-season biomass sorghum yields in the Mediterranean region. Thirteen machine learning algorithms were tested on fortnightly Sentinel-2A and Sentinel-2B estimates of the fraction of Absorbed Photosynthetically Active Radiation (fAPAR) in combination with in situ aboveground biomass yields from demonstrative fields in Italy. A gradient boosting algorithm implementing the xgbtree method was the best predictive model as it was satisfactorily implemented anywhere from May to July. The best prediction time was the month of May followed by May–June and May–July. To the best of our knowledge, this work represents the first time Sentinel-2-derived fAPAR is used in sorghum biomass predictive modeling. The results from this study will help farmers improve their sorghum biomass business operations and policy-makers and extension services improve energy planning and avoid energy-related crises.

scholarly journals Top-of-Atmosphere Retrieval of Multiple Crop Traits Using Variational Heteroscedastic Gaussian Processes within a Hybrid Workflow

2021 ◽  
Vol 13 (8) ◽  
pp. 1589
Author(s):  
José Estévez ◽  
Katja Berger ◽  
Jorge Vicent ◽  
Juan Pablo Rivera-Caicedo ◽  
Matthias Wocher ◽  
...  
Keyword(s):  
Mean Squared Error ◽  
Solar Spectrum ◽  
Vegetation Coverage ◽  
Estimation Accuracy ◽  
Leaf Water Content ◽  
Canopy Reflectance ◽  
Retrieval Models ◽  
Area Index ◽  
Spatial And Temporal Scales ◽  
Reflectance Data

In support of cropland monitoring, operational Copernicus Sentinel-2 (S2) data became available globally and can be explored for the retrieval of important crop traits. Based on a hybrid workflow, retrieval models for six essential biochemical and biophysical crop traits were developed for both S2 bottom-of-atmosphere (BOA) L2A and S2 top-of-atmosphere (TOA) L1C data. A variational heteroscedastic Gaussian process regression (VHGPR) algorithm was trained with simulations generated by the combined leaf-canopy reflectance model PROSAILat the BOA scale and further combined with the Second Simulation of a Satellite Signal in the Solar Spectrum (6SV) atmosphere model at the TOA scale. Established VHGPR models were then applied to S2 L1C and L2A reflectance data for mapping: leaf chlorophyll content (Cab), leaf water content (Cw), fractional vegetation coverage (FVC), leaf area index (LAI), and upscaled leaf biochemical compounds, i.e., LAI ∗ Cab (laiCab) and LAI ∗ Cw (laiCw). Estimated variables were validated using in situ reference data collected during the Munich-North-Isar field campaigns within growing seasons of maize and winter wheat in the years 2017 and 2018. For leaf biochemicals, retrieval from BOA reflectance slightly outperformed results from TOA reflectance, e.g., obtaining a root mean squared error (RMSE) of 6.5 μμg/cm2 (BOA) vs. 8 μμg/cm2 (TOA) in the case of Cab. For the majority of canopy-level variables, instead, estimation accuracy was higher when using TOA reflectance data, e.g., with an RMSE of 139 g/m2 (BOA) vs. 113 g/m2 (TOA) for laiCw. Derived maps were further compared against reference products obtained from the ESA Sentinel Application Platform (SNAP) Biophysical Processor. Altogether, the consistency between L1C and L2A retrievals confirmed that crop traits can potentially be estimated directly from TOA reflectance data. Successful mapping of canopy-level crop traits including information about prediction confidence suggests that the models can be transferred over spatial and temporal scales and, therefore, can contribute to decision-making processes for cropland management.

Sign in / Sign up

Export Citation Format

Share Document