Mapping the Intertidal Microphytobenthos Gross Primary Production, Part II: Merging Remote Sensing and Physical-Biological Coupled Modeling

Phenological information can be obtained from different sources of data. For instance, from remote sensing data or products and from models driven by weather variables. The former typically allows analyzing land surface phenology whereas the latter provide plant phenological information. Analyzing relationships between both sources of data allows us to understand the impact of climate change on vegetation over space and time. For example, the onset of spring is advanced or delayed by changes in the climate. These alterations affect plant productivity and animal migrations.Spring onset monitoring is supported by the Extended Spring Index (SI-x), which are a suite of regression-based models for key indicator plant species. These models (Schwartz et al. in 2013) are based on daily maximum and minimum temperature from the first day of the year (January 1st). The primary products of these models are the timing of first leaf and first bloom, but they also provide derivative products such as the timing of last freeze day and the risk of frost damage day (damage index) for each year. This information helps to understand if vegetation could have suffered from environmental stressors such as droughts or a late frost events. The effects of environmental stressors in vegetation could be captured by the false spring index, which relates the first leaf day and the last freeze day. Moreover, this information could be used to understand plant productivity as well as to evaluate the economic impact of climate change.Previous works studied the relationship between remote sensing and plant level products by means of spatial-temporal analysis between Gross Primary Production (GPP) and a spring onset index. However, they did not consider the possible impact of false spring effect in these relationships. Here, we present a spatial-temporal analysis between GPP and the damage index to better understand the effect of false springs (in annual gross photosynthesis data). The analysis is done for the period 2000 to 2015 over the contiguous US and at spatial resolution of 1 km. We used the MODIS annual sum of GPP and the damage and false spring indices derived from the SI-x models.

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Combining remote sensing and eddy covariance data to monitor the gross primary production of an estuarine wetland ecosystem in East China

Environmental Science Processes & Impacts ◽

10.1039/c5em00061k ◽

2015 ◽

Vol 17 (4) ◽

pp. 753-762

Author(s):

Mingquan Wu ◽

Shakir Muhammad ◽

Fang Chen ◽

Zheng Niu ◽

Changyao Wang

Keyword(s):

Remote Sensing ◽

Primary Production ◽

Eddy Covariance ◽

Gross Primary Production ◽

Model Performance ◽

Wetland Ecosystem ◽

Wetland Ecosystems ◽

New Model ◽

Estuarine Wetland ◽

Better Than

A new model performance better than the MODIS GPP product for wetland ecosystems was proposed and validated.

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MODELLING GROSS PRIMARY PRODUCTION OF TROPICAL FOREST BY REMOTE SENSING

Revista Brasileira de Climatologia ◽

10.5380/abclima.v22i0.50460 ◽

2018 ◽

Vol 22 ◽

Cited By ~ 1

Author(s):

Maísa Caldas Souza Velasque ◽

Marcelo Sacardi Biudes ◽

Nadja Gomes Machado ◽

Victor Hugo De Morais Danelichen ◽

George Louis Vourlitis ◽

...

Keyword(s):

Remote Sensing ◽

Primary Production ◽

Tropical Forest ◽

Gross Primary Production

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Estimation of the gross primary production of an old‐growth temperate mixed forest using eddy covariance and remote sensing

International Journal of Remote Sensing ◽

10.1080/01431160802372143 ◽

2008 ◽

Vol 30 (2) ◽

pp. 463-479 ◽

Cited By ~ 16

Author(s):

J. B. Wu ◽

X. M. Xiao ◽

D. X. Guan ◽

T. T. Shi ◽

C. J. Jin ◽

...

Keyword(s):

Remote Sensing ◽

Primary Production ◽

Eddy Covariance ◽

Gross Primary Production ◽

Mixed Forest ◽

Old Growth ◽

Temperate Mixed Forest

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Variability in carbon exchange and light utilization among boreal forest stands: implications for remote sensing of net primary production

Canadian Journal of Forest Research ◽

10.1139/x97-222 ◽

1998 ◽

Vol 28 (3) ◽

pp. 375-389 ◽

Cited By ~ 1

Author(s):

Scott J Goetz ◽

Stephen D Prince

Keyword(s):

Remote Sensing ◽

Primary Production ◽

Boreal Forest ◽

Populus Tremuloides ◽

Net Primary Production ◽

Gross Primary Production ◽

Total Carbon ◽

Simple Relationship ◽

Carbon Exchange ◽

Forest Stands

Variability in carbon exchange, net primary production (NPP), and light-use efficiency were explored for 63 boreal forest stands in northeastern Minnesota using an ecophysiological model. The model was initialized with extensive field measurements of Populus tremuloides Michx. and Picea mariana (Mill.) BSP stand properties. The results showed that the proportion of total carbon assimilation expended in autotrophic respiration (i.e., the respiration to assimilation ratio, R/A) was significantly different for the two tree species and this explained much of the variability in the amount of net production per unit absorbed photosynthetically active radiation (APAR), referred to as PAR utilization ( epsilonn). This is the first known study to directly link variability in respiratory costs to epsilonn. Total assimilation per unit APAR ( epsilong) was much less variable than epsilonn and was not significantly different between species. Greater stomatal control on some moisture stressed sites accounted for most of the variability in epsilong. The lack of a simple relationship between light harvesting and net carbon gain indicates that estimation of net primary production with satellite remote sensing requires additional information on respiration costs; however, evidence for convergence in epsilong can be used to simplify the remote sensing of gross primary production over large areas.

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Assessment of the gross primary production dynamics of a Mediterranean holm oak forest by remote sensing time series analysis

Agroforestry Systems ◽

10.1007/s10457-015-9786-x ◽

2015 ◽

Vol 89 (3) ◽

pp. 491-510 ◽

Cited By ~ 4

Author(s):

Víctor Cicuéndez ◽

Javier Litago ◽

Margarita Huesca ◽

Manuel Rodriguez-Rastrero ◽

Laura Recuero ◽

...

Keyword(s):

Remote Sensing ◽

Time Series ◽

Time Series Analysis ◽

Primary Production ◽

Gross Primary Production ◽

Holm Oak ◽

Oak Forest ◽

Series Analysis ◽

Production Dynamics ◽

Sensing Time

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Sensitivity of Estimated Total Canopy SIF Emission to Remotely Sensed LAI and BRDF Products

Journal of Remote Sensing ◽

10.34133/2021/9795837 ◽

2021 ◽

Vol 2021 ◽

pp. 1-18

Author(s):

Zhaoying Zhang ◽

Yongguang Zhang ◽

Jing M. Chen ◽

Weimin Ju ◽

Mirco Migliavacca ◽

...

Keyword(s):

Remote Sensing ◽

Chlorophyll Fluorescence ◽

Energy Balance ◽

Primary Production ◽

Gross Primary Production ◽

Structural Effects ◽

Canopy Interception ◽

Monitoring Instrument ◽

Uncertainty Level ◽

Scope Model

Remote sensing of solar-induced chlorophyll fluorescence (SIF) provides new possibilities to estimate terrestrial gross primary production (GPP). To mitigate the angular and canopy structural effects on original SIF observed by sensors (SIFobs), it is recommended to derive total canopy SIF emission (SIFtotal) of leaves within a canopy using canopy interception (i0) and reflectance of vegetation (RV). However, the effects of the uncertainties in i0 and RV on the estimation of SIFtotal have not been well understood. Here, we evaluated such effects on the estimation of GPP using the Soil-Canopy-Observation of Photosynthesis and the Energy balance (SCOPE) model. The SCOPE simulations showed that the R2 between GPP and SIFtotal was clearly higher than that between GPP and SIFobs and the differences in R2 (ΔR2) tend to decrease with the increasing levels of uncertainties in i0 and RV. The resultant ΔR2 decreased to zero when the uncertainty level in i0 and RV was ~30% for red band SIF (RSIF, 683 nm) and ~20% for far-red band SIF (FRSIF, 740 nm). In addition, as compared to the TROPOspheric Monitoring Instrument (TROPOMI) SIFobs at both red and far-red bands, SIFtotal derived using any combination of i0 (from MCD15, VNP15, and CGLS LAI products) and RV (from MCD34, MCD19, and VNP43 BRDF products) showed comparable improvements in estimating GPP. With this study, we suggest a way to advance our understanding in the estimation of a more physiological relevant SIF datasets (SIFtotal) using current satellite products.

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Improving Remote Sensing-based Estimation of Mangrove Forest Gross Primary Production by Quantifying Environmental Stressors: Sea Surface Temperature, Salinity, and Photosynthetic Active Radiation

10.21203/rs.3.rs-817946/v1 ◽

2021 ◽

Author(s):

Yuhan Zheng ◽

Wataru Takeuchi

Keyword(s):

Remote Sensing ◽

Primary Production ◽

Surface Temperature ◽

Sea Surface Temperature ◽

Gross Primary Production ◽

Sea Surface ◽

Photosynthetic Active Radiation ◽

Mangrove Forests ◽

Active Radiation ◽

Mangrove Ecosystems

Abstract Mangrove ecosystems play an important role in global carbon budget, however, the quantitative relationships between environmental drivers and productivity in these forests remain poorly understood. This study presented a remote sensing (RS)-based productivity model to estimate the light use efficiency (LUE) and gross primary production (GPP) of mangrove forests in China. Firstly, LUE model considered the effects of tidal inundation and therefore involved sea surface temperature (SST) and salinity as environmental scalars. Secondly, the downscaling effect of photosynthetic active radiation (PAR) on the mangrove LUE was quantified according to different PAR values. Thirdly, the maximum LUE varied with temperature and was therefore determined based on the response of daytime net ecosystem exchange and PAR at different temperatures. Lastly, GPP was estimated by combining the LUE model with the fraction of absorbed photosynthetically active radiation from Sentinel-2 images. The results showed that the LUE model developed for mangrove forests has higher overall accuracy (RMSE = 0.0051, R2 = 0.64) than the terrestrial model (RMSE = 0.0220, R2 = 0.24). The main environmental stressor for the photosynthesis of mangrove forests in China was PAR. The estimated GPP was, in general, in agreement with the in-situ measurement from the two carbon flux towers. Compared to the MODIS GPP product, the derived GPP had higher accuracy, with RMSE improving from 39.09 to 19.05 g C/m2/8 days in 2012, and from 33.76 to 19.51 g C/m2/8 days in 2015. The spatiotemporal distributions of the mangrove GPP revealed that GPP was most strongly controlled by environmental conditions, especially temperature and PAR, as well as the distribution of mangroves. These results demonstrate the potential of the RS-based productivity model for scaling up GPP in mangrove forests, a key to explore the carbon cycle of mangrove ecosystems at national and global scales.

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