scholarly journals Sun-induced Chlorophyll fluorescence and PRI improve remote sensing GPP estimates under varying nutrient availability in a typical Mediterranean savanna ecosystem

2015 ◽  
Vol 12 (14) ◽  
pp. 11891-11934 ◽  
Author(s):  
O. Perez-Priego ◽  
J. Guan ◽  
M. Rossini ◽  
F. Fava ◽  
T. Wutzler ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Chlorophyll Fluorescence ◽  
Vegetation Index ◽  
Near Infrared ◽  
Gross Primary Production ◽  
High Spectral Resolution ◽  
N Availability ◽  
P Availability ◽  
N Content ◽  
Near Surface

Abstract. This study investigates the performances of different optical indices to estimate gross primary production (GPP) of herbaceous stratum in a Mediterranean savanna with different Nitrogen (N) and Phosphorous (P) availability. Sun-induced chlorophyll Fluorescence yield computed at 760 nm (Fy760), scaled-photochemical reflectance index (sPRI), MERIS terrestrial-chlorophyll index (MTCI) and Normalized difference vegetation index (NDVI) were computed from near-surface field spectroscopy measurements collected using high spectral resolution spectrometers covering the visible near-infrared regions. GPP was measured using canopy-chambers on the same locations sampled by the spectrometers. We hypothesized that light-use efficiency (LUE) models driven by remote sensing quantities (RSM) can better track changes in GPP caused by nutrient supplies compared to those driven exclusively by meteorological data (MM). Particularly, we compared the performances of different RSM formulations – relying on the use of Fy760 or sPRI as proxy for LUE and NDVI or MTCI as fraction of absorbed photosynthetically active radiation (fAPAR) – with those of classical MM. Results showed significantly higher GPP in the N fertilized experimental plots during the growing period. These differences in GPP disappeared in the drying period when senescence effects masked out potential differences due to plant N content. Consequently, although MTCI was tightly related to plant N content (r2 = 0.86, p < 0.01), it was poorly related to GPP (r2 = 0.45, p < 0.05). On the contrary sPRI and Fy760 correlated well with GPP during the whole measurement period. Results revealed that the relationship between GPP and Fy760 is not unique across treatments but it is affected by N availability. Results from a cross validation analysis showed that MM (AICcv = 127, MEcv = 0.879) outperformed RSM (AICcv = 140, MEcv = 0.8737) when soil moisture was used to constrain the seasonal dynamic of LUE. However, residual analyses demonstrated that MM is predictively inaccurate whenever no climatic variable explicitly reveals nutrient-related changes in the LUE parameter. These results put forward that RSM is a valuable means to diagnose nutrient-induced effects on the photosynthetic activity.

scholarly journals Sun-induced chlorophyll fluorescence and photochemical reflectance index improve remote-sensing gross primary production estimates under varying nutrient availability in a typical Mediterranean savanna ecosystem

2015 ◽  
Vol 12 (21) ◽  
pp. 6351-6367 ◽  
Author(s):  
O. Perez-Priego ◽  
J. Guan ◽  
M. Rossini ◽  
F. Fava ◽  
T. Wutzler ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Chlorophyll Fluorescence ◽  
Primary Production ◽  
Gross Primary Production ◽  
High Spectral Resolution ◽  
N Availability ◽  
P Availability ◽  
N Content ◽  
Near Surface ◽  
Photochemical Reflectance Index

Abstract. This study investigates the performances of different optical indices to estimate gross primary production (GPP) of herbaceous stratum in a Mediterranean savanna with different nitrogen (N) and phosphorous (P) availability. Sun-induced chlorophyll fluorescence yield computed at 760 nm (Fy760), scaled photochemical reflectance index (sPRI), MERIS terrestrial-chlorophyll index (MTCI) and normalized difference vegetation index (NDVI) were computed from near-surface field spectroscopy measurements collected using high spectral resolution spectrometers covering the visible near-infrared regions. GPP was measured using canopy chambers on the same locations sampled by the spectrometers. We tested whether light-use efficiency (LUE) models driven by remote-sensing quantities (RSMs) can better track changes in GPP caused by nutrient supplies compared to those driven exclusively by meteorological data (MM). Particularly, we compared the performances of different RSM formulations – relying on the use of Fy760 or sPRI as a proxy for LUE and NDVI or MTCI as a fraction of absorbed photosynthetically active radiation (fAPAR) – with those of classical MM. Results showed higher GPP in the N-fertilized experimental plots during the growing period. These differences in GPP disappeared in the drying period when senescence effects masked out potential differences due to plant N content. Consequently, although MTCI was closely related to the mean of plant N content across treatments (r2 = 0.86, p < 0.01), it was poorly related to GPP (r2 = 0.45, p < 0.05). On the contrary sPRI and Fy760 correlated well with GPP during the whole measurement period. Results revealed that the relationship between GPP and Fy760 is not unique across treatments, but it is affected by N availability. Results from a cross-validation analysis showed that MM (AICcv = 127, MEcv = 0.879) outperformed RSM (AICcv =140, MEcv = 0.8737) when soil moisture was used to constrain the seasonal dynamic of LUE. However, residual analyses demonstrated that GPP predictions with MM are inaccurate whenever no climatic variable explicitly reveals nutrient-related changes in the LUE parameter. These results suggest that RSM is a valuable means to diagnose nutrient-induced effects on the photosynthetic activity.

scholarly journals First observations of global and seasonal terrestrial chlorophyll fluorescence from space

2011 ◽  
Vol 8 (3) ◽  
pp. 637-651 ◽  
Author(s):  
J. Joiner ◽  
Y. Yoshida ◽  
A. P. Vasilkov ◽  
Y. Yoshida ◽  
L. A. Corp ◽  
...  
Keyword(s):  
Chlorophyll Fluorescence ◽  
Vegetation Index ◽  
Near Infrared ◽  
Fluorescence Yield ◽  
High Spectral Resolution ◽  
Fourier Transform Spectrometer ◽  
Terrestrial Vegetation ◽  
Crossing Time ◽  
Global Coverage ◽  
Synchronous Orbit

Abstract. Remote sensing of terrestrial vegetation fluorescence from space is of interest because it can potentially provide global coverage of the functional status of vegetation. For example, fluorescence observations may provide a means to detect vegetation stress before chlorophyll reductions take place. Although there have been many measurements of fluorescence from ground- and airborne-based instruments, there has been scant information available from satellites. In this work, we use high-spectral resolution data from the Thermal And Near-infrared Sensor for carbon Observation – Fourier Transform Spectrometer (TANSO-FTS) on the Japanese Greenhouse gases Observing SATellite (GOSAT) that is in a sun-synchronous orbit with an equator crossing time near 13:00 LT. We use filling-in of the potassium (K) I solar Fraunhofer line near 770 nm to derive chlorophyll fluorescence and related parameters such as the fluorescence yield at that wavelength. We map these parameters globally for two months (July and December 2009) and show a full seasonal cycle for several different locations, including two in the Amazonia region. We also compare the derived fluorescence information with that provided by the MODIS Enhanced Vegetation Index (EVI). These comparisons show that for several areas these two indices exhibit different seasonality and/or relative intensity variations, and that changes in fluorescence frequently lead those seen in the EVI for those regions. The derived fluorescence therefore provides information that is related to, but independent of the reflectance.

scholarly journals The Links between Canopy Solar-Induced Chlorophyll Fluorescence and Gross Primary Production Responses to Meteorological Factors in the Growing Season in Deciduous Broadleaf Forest

2021 ◽  
Vol 13 (12) ◽  
pp. 2363
Author(s):  
Xiangfen Cheng ◽  
Yu Zhou ◽  
Meijun Hu ◽  
Feng Wang ◽  
Hui Huang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Chlorophyll Fluorescence ◽  
Primary Production ◽  
Forest Canopy ◽  
Meteorological Factors ◽  
Gross Primary Production ◽  
Terrestrial Ecosystems ◽  
Growing Season ◽  
Near Surface ◽  
The Relationship

Solar-induced chlorophyll fluorescence (SIF) is a hopeful indicator, which along with remote sensing, is used to measure the photosynthetic efficiency and gross primary production (GPP) of vegetation in regional terrestrial ecosystems. Studies have found a significant linear correlation between SIF and GPP in a variety of ecosystems. However, this relationship has mainly been established using SIF and GPP data derived from satellite remote sensing and continuous ground-based observations, respectively, which are difficult to accurately match. To overcome this, some studies have begun to use tower-based automatic observation instruments to study the changes of near-surface SIF and GPP. This study conducts continuous simultaneous observation of SIF, carbon flux, and meteorological factors on the forest canopy of a cork oak plantation during the growing season to explore how meteorological factors impact on canopy SIF and its relationship with GPP. This research found that the canopy SIF has obvious diurnal and day-to-day variations during the growing season but overall is relatively stable. Furthermore, SIF is greatly affected by incident radiation in different weather conditions and can change daily. Meteorological factors have a major role in the relationship between SIF and GPP; overall, the relationship shows a significant linear regression on the 30 min scale, but weakens when aggregating to the diurnal scale. Photosynthetically active radiation (PAR) drives SIF on a daily basis and changes the relationship between SIF and GPP on a seasonal timescale. As PAR increases, the daily slopes of the linear regressions between SIF and GPP decrease. On the 30 min timescale, both SIF and GPP increase with PAR until it reaches 1250 μmol·m−2·s−1; subsequently, SIF continues to increase while GPP decreases and they show opposite trends. Soil moisture and vapor pressure deficit influence SIF and GPP, respectively. Our findings demonstrate that meteorological factors affect the relationship between SIF and GPP, thereby enhancing the understanding of the mechanistic link between chlorophyll fluorescence and photosynthesis.

scholarly journals First observations of global and seasonal terrestrial chlorophyll fluorescence from space

2010 ◽  
Vol 7 (6) ◽  
pp. 8281-8318 ◽  
Author(s):  
J. Joiner ◽  
Y. Yoshida ◽  
A. P. Vasilkov ◽  
Y. Yoshida ◽  
L. A. Corp ◽  
...  
Keyword(s):  
Chlorophyll Fluorescence ◽  
Vegetation Index ◽  
Near Infrared ◽  
High Spectral Resolution ◽  
Fourier Transform Spectrometer ◽  
Terrestrial Vegetation ◽  
Infrared Sensor ◽  
Crossing Time ◽  
Global Coverage ◽  
Synchronous Orbit

Abstract. Remote sensing of terrestrial vegetation fluorescence from space is of interest because it can potentially provide global coverage of the functional status of vegetation. For example, fluorescence observations may provide a means to detect vegetation stress before chlorophyll reductions take place. Although there have been many measurements of fluorescence from ground- and airborne-based instruments, there has been scant information available from satellites. In this work, we use high-spectral resolution data from the Thermal And Near-infrared Sensor for carbon Observation – Fourier Transform Spectrometer (TANSO-FTS) on the Japanese Greenhouse gases Observing SATellite (GOSAT) that is in a sun-synchronous orbit with an equator crossing time near 13:00 LT. We use filling-in of the potassium (K) I solar Fraunhofer line near 770 nm to derive chlorophyll fluorescence and related parameters such as the fluorescence quantum yield at that wavelength. We map these parameters globally for two months (July and December 2009) and show a full seasonal cycle for several different locations, including two in the Amazonia region. We also compare the derived fluorescence information with that provided by the MODIS Enhanced Vegetation Index (EVI). These comparisons show that for several areas these two indices exhibit different seasonality and/or relative intensity variations, and that changes in fluorescence frequently lead those seen in the EVI for those regions. The derived fluorescence therefore provides information that is related to, but independent of the reflectance.

scholarly journals Can vegetation index track the interannual variation in gross primary production of temperate deciduous forests?

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Fan Liu ◽  
Chuankuan Wang ◽  
Xingchang Wang
Keyword(s):  
Primary Production ◽  
Vegetation Index ◽  
Near Infrared ◽  
Deciduous Forest ◽  
Gross Primary Production ◽  
Growing Season ◽  
Temperate Deciduous Forest ◽  
Near Infrared Reflectance ◽  
Modis Ndvi ◽  
Temperate Deciduous

Abstract Background Vegetation indices (VIs) by remote sensing are widely used as simple proxies of the gross primary production (GPP) of vegetation, but their performances in capturing the inter-annual variation (IAV) in GPP remain uncertain. Methods We evaluated the performances of various VIs in tracking the IAV in GPP estimated by eddy covariance in a temperate deciduous forest of Northeast China. The VIs assessed included the normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI), and the near-infrared reflectance of vegetation (NIRv) obtained from tower-radiometers (broadband) and the Moderate Resolution Imaging Spectroradiometer (MODIS), respectively. Results We found that 25%–35% amplitude of the broadband EVI tracked the start of growing season derived by GPP (R2: 0.56–0.60, bias < 4 d), while 45% (or 50%) amplitudes of broadband (or MODIS) NDVI represented the end of growing season estimated by GPP (R2: 0.58–0.67, bias < 3 d). However, all the VIs failed to characterize the summer peaks of GPP. The growing-season integrals but not averaged values of the broadband NDVI, MODIS NIRv and EVI were robust surrogates of the IAV in GPP (R2: 0.40–0.67). Conclusion These findings illustrate that specific VIs are effective only to capture the GPP phenology but not the GPP peak, while the integral VIs have the potential to mirror the IAV in GPP.

Early stress detection in grapevine – can remotely-sensed sun-induced chlorophyll fluorescence do the job?

2021 ◽  
Author(s):  
Georg Wohlfahrt ◽  
Albin Hammerle ◽  
Barbara Rainer ◽  
Florian Haas
Keyword(s):  
Chlorophyll Fluorescence ◽  
Vegetation Index ◽  
Near Infrared ◽  
Census Data ◽  
Meteorological Data ◽  
Vegetation Indices ◽  
Wavelength Region ◽  
Remote Sensing Data ◽  
Warning Signs ◽  
Mitigation Measures

&lt;p&gt;Ongoing changes in climate (both in the means and the extremes) are increasingly challenging grapevine production in the province of South Tyrol (Italy). Here we ask the question whether sun-induced chlorophyll fluorescence (SIF) observed remotely from space can detect early warning signs of stress in grapevine and thus help guide mitigation measures.&lt;/p&gt;&lt;p&gt;Chlorophyll fluorescence refers to light absorbed by chlorophyll molecules that is re-emitted in the red to far-red wavelength region. Previous research at leaf and canopy scale indicated that SIF correlates with the plant photosynthetic uptake of carbon dioxide as it competes for the same energy pool.&lt;/p&gt;&lt;p&gt;To address this question, we use time series of two down-scaled SIF products (GOME-2 and OCO-2, 2007/14-2018) as well as the original OCO-2 data (2014-2019). As a benchmark, we use several vegetation indices related to canopy greenness, as well as a novel near-infrared radiation-based vegetation index (2000-2019). Meteorological data fields are used to explore possible weather-related causes for observed deviations in remote sensing data. Regional DOC grapevine census data (2000-2019) are used as a reference for the analyses.&lt;/p&gt;

scholarly journals SIFSpec: Measuring Solar-Induced Chlorophyll Fluorescence Observations for Remote Sensing of Photosynthesis

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 3009 ◽  
Author(s):  
Shanshan Du ◽  
Liangyun Liu ◽  
Xinjie Liu ◽  
Jian Guo ◽  
Jiaochan Hu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Chlorophyll Fluorescence ◽  
Seasonal Variations ◽  
Photosynthetic Activity ◽  
Gross Primary Production ◽  
Atmospheric Correction ◽  
Gansu Province ◽  
Optical Measurements ◽  
Calibration Data ◽  
Flux Measurements

Solar-induced chlorophyll fluorescence (SIF) is regarded as a proxy for photosynthesis in terrestrial vegetation. Tower-based long-term observations of SIF are very important for gaining further insight into the ecosystem-specific seasonal dynamics of photosynthetic activity, including gross primary production (GPP). Here, we present the design and operation of the tower-based automated SIF measurement (SIFSpec) system. This system was developed with the aim of obtaining synchronous SIF observations and flux measurements across different terrestrial ecosystems, as well as to validate the increasing number of satellite SIF products using in situ measurements. Details of the system components, instrument installation, calibration, data collection, and processing are introduced. Atmospheric correction is also included in the data processing chain, which is important, but usually ignored for tower-based SIF measurements. Continuous measurements made across two growing cycles over maize at a Daman (DM) flux site (in Gansu province, China) demonstrate the reliable performance of SIF as an indicator for tracking the diurnal variations in photosynthetically active radiation (PAR) and seasonal variations in GPP. For the O2–A band in particular, a high correlation coefficient value of 0.81 is found between the SIF and seasonal variations of GPP. It is thus concluded that, in coordination with continuous eddy covariance (EC) flux measurements, automated and continuous SIF observations can provide a reliable approach for understanding the photosynthetic activity of the terrestrial ecosystem, and are also able to bridge the link between ground-based optical measurements and airborne or satellite remote sensing data.

scholarly journals Improving Field-Scale Wheat LAI Retrieval Based on UAV Remote-Sensing Observations and Optimized VI-LUTs

2019 ◽  
Vol 11 (20) ◽  
pp. 2456 ◽  
Author(s):  
Wanxue Zhu ◽  
Zhigang Sun ◽  
Yaohuan Huang ◽  
Jianbin Lai ◽  
Jing Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Precision Agriculture ◽  
Vegetation Index ◽  
Management Practices ◽  
Near Infrared ◽  
Lookup Table ◽  
Field Scale ◽  
Area Index ◽  
Biophysical Parameter ◽  
Retrieval Accuracy

Leaf area index (LAI) is a key biophysical parameter for monitoring crop growth status, predicting crop yield, and quantifying crop variability in agronomic applications. Mapping the LAI at the field scale using multispectral cameras onboard unmanned aerial vehicles (UAVs) is a promising precision-agriculture application with specific requirements: The LAI retrieval method should be (1) robust so that crop LAI can be estimated with similar accuracy and (2) easy to use so that it can be applied to the adjustment of field management practices. In this study, three UAV remote-sensing missions (UAVs with Micasense RedEdge-M and Cubert S185 cameras) were carried out over six experimental plots from 2018 to 2019 to investigate the performance of reflectance-based lookup tables (LUTs) and vegetation index (VI)-based LUTs generated from the PROSAIL model for wheat LAI retrieval. The effects of the central wavelengths and bandwidths for the VI calculations on the LAI retrieval were further examined. We found that the VI-LUT strategy was more robust and accurate than the reflectance-LUT strategy. The differences in the LAI retrieval accuracy among the four VI-LUTs were small, although the improved modified chlorophyll absorption ratio index-lookup table (MCARI2-LUT) and normalized difference vegetation index-lookup table (NDVI-LUT) performed slightly better. We also found that both of the central wavelengths and bandwidths of the VIs had effects on the LAI retrieval. The VI-LUTs with optimized central wavelengths (red = 612 nm, near-infrared (NIR) = 756 nm) and narrow bandwidths (~4 nm) improved the wheat LAI retrieval accuracy (R2 ≥ 0.75). The results of this study provide an alternative method for retrieving crop LAI, which is robust and easy use for precision-agriculture applications and may be helpful for designing UAV multispectral cameras for agricultural monitoring.

scholarly journals Remote Sensing Landslide Recognition Based on Convolutional Neural Network

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Yu Wang ◽  
Xiaofei Wang ◽  
Junfan Jian
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Convolutional Neural Network ◽  
Large Scale ◽  
Vegetation Index ◽  
Near Infrared ◽  
Spatial Information ◽  
Spectral Characteristics ◽  
Remote Sensing Images ◽  
Landslide Event

Landslides are a type of frequent and widespread natural disaster. It is of great significance to extract location information from the landslide in time. At present, most articles still select single band or RGB bands as the feature for landslide recognition. To improve the efficiency of landslide recognition, this study proposed a remote sensing recognition method based on the convolutional neural network of the mixed spectral characteristics. Firstly, this paper tried to add NDVI (normalized difference vegetation index) and NIRS (near-infrared spectroscopy) to enhance the features. Then, remote sensing images (predisaster and postdisaster images) with same spatial information but different time series information regarding landslide are taken directly from GF-1 satellite as input images. By combining the 4 bands (red + green + blue + near-infrared) of the prelandslide remote sensing images with the 4 bands of the postlandslide images and NDVI images, images with 9 bands were obtained, and the band values reflecting the changing characteristics of the landslide were determined. Finally, a deep learning convolutional neural network (CNN) was introduced to solve the problem. The proposed method was tested and verified with remote sensing data from the 2015 large-scale landslide event in Shanxi, China, and 2016 large-scale landslide event in Fujian, China. The results showed that the accuracy of the method was high. Compared with the traditional methods, the recognition efficiency was improved, proving the effectiveness and feasibility of the method.

scholarly journals Monitoring the Impact of Environmental Manipulation on Peatland Surface by Simple Remote Sensing Indices

2018 ◽  
Vol 23 ◽  
pp. 00030 ◽  
Author(s):  
Anshu Rastogi ◽  
Subhajit Bandopadhyay ◽  
Marcin Stróżecki ◽  
Radosław Juszczak
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Vegetation Index ◽  
Near Infrared ◽  
Near Infrared Reflectance ◽  
Area Index ◽  
Environmental Manipulation ◽  
Chlorophyll Index ◽  
The Impact ◽  
Remote Sensing Indices

The behaviour of nature depends on the different components of climates. Among these, temperature and rainfall are two of the most important components which are known to change plant productivity. Peatlands are among the most valuable ecosystems on the Earth, which is due to its high biodiversity, huge soil carbon storage, and its sensitivity to different environmental factors. With the rapid growth in industrialization, the climate change is becoming a big concern. Therefore, this work is focused on the behaviour of Sphagnum peatland in Poland, subjected to environment manipulation. Here it has been shown how a simple reflectance based technique can be used to assess the impact of climate change on peatland. The experimental setup consists of four plots with two kind of manipulations (control, warming, reduced precipitation, and a combination of warming and reduced precipitation). Reflectance data were measured twice in August 2017 under a clear sky. Vegetation indices (VIs) such as Normalized Difference Vegetation Index (NDVI), Photochemical Reflectance Index (PRI), near-infrared reflectance of vegetation (NIRv), MERIS terrestrial chlorophyll index (MTCI), Green chlorophyll index (CIgreen), Simple Ration (SR), and Water Band Index (WBI) were calculated to trace the impact of environmental manipulation on the plant community. Leaf Area Index of vascular plants was also measured for the purpose to correlate it with different VIs. The observation predicts that the global warming of 1°C may cause a significant change in peatland behaviour which can be tracked and monitored by simple remote sensing indices.

Sign in / Sign up

Export Citation Format

Share Document