A MODIS-based ecosystem respiration model and its application in optimizing vegetation photosynthesis and respiration model: A case study of two terrestrial ecosystems in Northern China

2020 ◽  
Author(s):  
Yong Hao ◽  
Haimei Jiang ◽  
Haotian Ye
Keyword(s):  
Ecosystem Respiration ◽  
Remote Sensing Data ◽  
Terrestrial Ecosystems ◽  
Grassland Ecosystem ◽  
Linear Temperature ◽  
Growing Seasons ◽  
Respiration Model ◽  
Photosynthesis And Respiration ◽  
Semi Arid ◽  
Cropland Ecosystem

<p>Turbulent flux data observed in surface layer during growing seasons at Xilinhaote National Climatic Observatory and Jinzhou Agroecosystem Observatory and remote sensing data were analyzed to acquire main environmental factors and biological factors which drive the ecosystem respiration (R<sub>eco</sub>). Then the key driven factors of R<sub>eco</sub> were selected to optimize a semi-empirical ecosystem respiration model. Based on the new ecosystem respiration model, respiration part of Vegetation Photosynthesis and Respiration Model (VPRM) was optimized and its simulation effect of net ecosystem exchange (NEE) was validated in a semi-arid grassland ecosystem and a maize cropland ecosystem.</p><p>Compared to the linear temperature model, the nocturnal R<sub>eco</sub> simulated by the new ecosystem respiration model agreed remarkably better with the observed R<sub>eco</sub> (at Xilinhaote site, R<sup>2</sup> increased from 0.08 to 0.61 in 2010-2012; at Jinzhou site, R<sup>2</sup> increased from 0.13 to 0.55 in 2010). And the new ecosystem respiration model showed similar performance in predicting nocturnal R<sub>eco</sub> (at Xilinhaote site, R<sup>2</sup> increased from 0.32 to 0.57 in 2013; at Jinzhou site, R<sup>2</sup> increased from 0.33 to 0.61 in 2011).</p><p>This study also indicates that optimization of the respiration part of VPRM can improve the simulation effect of NEE during nighttime of the growing seasons in a semi-arid grassland ecosystem and a maize cropland ecosystem, R<sup>2</sup> between the modeled NEE and the observed NEE increased from 0.30 to 0.57 in the semi-arid grassland ecosystem and<sup> </sup>increased from 0.03 to 0.48 in the maize cropland ecosystem. However, in the whole time of the growing seasons, little difference was found between the modelled NEE by the original VPRM model and that by our modified VPRM model, probably for the reason that daytime NEE is mainly dominated by vegetation photosynthesis.</p>

A Study of MODIS-Based Ecosystem Respiration Model in a Semi-Arid Grassland of Inner Mongolia

2020 ◽  
Author(s):  
Haimei Jiang ◽  
Haotian Ye ◽  
Yong Hao
Keyword(s):  
Land Surface ◽  
Inner Mongolia ◽  
Vegetation Index ◽  
Ecosystem Respiration ◽  
Water Index ◽  
Growing Seasons ◽  
Land Surface Water ◽  
Respiration Model ◽  
The Difference ◽  
Semi Arid

<p>Eddy covariance data from Xilinhaote National Climatological Observatory in Xilin Gol League during growing seasons of 2010—2013 as well as MODIS data were used to validate an ecosystem respiration model based on enhanced vegetation index (EVI), land surface water index (LSWI) and land surface temperature (LST) in a semi-arid grassland of Inner Mongolia. The limitations of this remote sensing respiration model were also discussed. The results indicate that this model can successfully simulate the variations of nocturnal ecosystem respiration (Reco) in the growing seasons and between different years. The simulated nocturnal Reco also agreed remarkably with the observed Reco (R2=0.90, RMSE=0.02 mgCO2/(m2·s)). Moreover, the observed nocturnal Reco showed a good linear correlation with EVIs×Ws (R2=0.63), in which EVIs and Ws are response functions of EVI and LSWI on photosynthesis, respectively. The response of nocturnal Reco to LST was also found following the L-T equation (R2=0.39). In addition, the difference between responses of nocturnal Reco to EVIs×Ws and LST in the early, middle and late stages of the growing season is indicated as one principal source of the deviations of model results.</p>

scholarly journals Mapping irrigated areas using multi-sensor remote sensing data in a Mediterranean environment

2017 ◽  
Author(s):  
Pasquale Nino ◽  
Silvia Vanino ◽  
Flavio Lupia ◽  
Guido D'Urso ◽  
Carlo De Michele ◽  
...  
Keyword(s):  
Spatial Scales ◽  
Remote Sensing Data ◽  
Arid Environment ◽  
Earth Observation ◽  
Irrigation District ◽  
Landsat 8 ◽  
Vegetation Development ◽  
Vegetation Growth ◽  
Growing Seasons ◽  
Semi Arid

Water managers need map of irrigated areas (defined as the identification of their location and their areal extent) to plan a rational use of water under limited availability and to prevent the unauthorized withdrawals. Many authors have shown that the Earth Observation techniques are an effective tool for mapping irrigated areas worldwide at different spatial scales (global/regional/and local). This study presents a methodology for mapping irrigated areas in semi-arid environment based on Earth Observation techniques and by fully exploiting datasets freely available processed by open source software and tools. Data acquired with the Landsat 8 Operational Land Imager (OLI) and the new Sentinel 2A MultiSpectral Instrument (MSI) sensors were integrated to obtain cloud free dense time series allowing to monitor the vegetation development throughout the growing seasons. Irrigated areas were identified by analysing the growing patterns under water deficit conditions from NDVI values under the assumption that, in arid and semi-arid environment (like the Mediterranean Region), high trend of vegetation growth are compatible only with irrigation. The method was applied inside the Cixerri Consortium Irrigation District located in South of Sardinia (Italy).

scholarly journals Mapping irrigated areas using multi-sensor remote sensing data in a Mediterranean environment

2017 ◽  
Author(s):  
Pasquale Nino ◽  
Silvia Vanino ◽  
Flavio Lupia ◽  
Guido D'Urso ◽  
Carlo De Michele ◽  
...  
Keyword(s):  
Spatial Scales ◽  
Remote Sensing Data ◽  
Arid Environment ◽  
Earth Observation ◽  
Irrigation District ◽  
Landsat 8 ◽  
Vegetation Development ◽  
Vegetation Growth ◽  
Growing Seasons ◽  
Semi Arid

Water managers need map of irrigated areas (defined as the identification of their location and their areal extent) to plan a rational use of water under limited availability and to prevent the unauthorized withdrawals. Many authors have shown that the Earth Observation techniques are an effective tool for mapping irrigated areas worldwide at different spatial scales (global/regional/and local). This study presents a methodology for mapping irrigated areas in semi-arid environment based on Earth Observation techniques and by fully exploiting datasets freely available processed by open source software and tools. Data acquired with the Landsat 8 Operational Land Imager (OLI) and the new Sentinel 2A MultiSpectral Instrument (MSI) sensors were integrated to obtain cloud free dense time series allowing to monitor the vegetation development throughout the growing seasons. Irrigated areas were identified by analysing the growing patterns under water deficit conditions from NDVI values under the assumption that, in arid and semi-arid environment (like the Mediterranean Region), high trend of vegetation growth are compatible only with irrigation. The method was applied inside the Cixerri Consortium Irrigation District located in South of Sardinia (Italy).

scholarly journals Technical Note: The Simple Diagnostic Photosynthesis and Respiration Model (SDPRM)

2013 ◽  
Vol 10 (10) ◽  
pp. 6485-6508 ◽  
Author(s):  
B. Badawy ◽  
C. Rödenbeck ◽  
M. Reichstein ◽  
N. Carvalhais ◽  
M. Heimann
Keyword(s):  
Interannual Variability ◽  
Ecosystem Respiration ◽  
Spatial Scales ◽  
Gross Primary Production ◽  
Technical Note ◽  
Atmospheric Co2 ◽  
Limiting Factor ◽  
Respiration Model ◽  
Photosynthesis And Respiration ◽  
Process Based Models

Abstract. We present a Simple Diagnostic Photosynthesis and Respiration Model (SDPRM) that has been developed based on pre-existing formulations. The photosynthesis model is based on the light use efficiency logic for calculating the gross primary production (GPP), while the ecosystem respiration (Reco) is a modified version of an Arrhenius-type equation. SDPRM is driven by satellite-derived fAPAR (fraction of Absorbed Photosynthetically Active Radiation) and climate data from the National Center for Environmental Prediction/National Center for Atmospheric Research Reanalysis (NCEP/NCAR). The model estimates 3-hourly values of GPP for seven major biomes and daily Reco. The motivation is to provide a priori fields of surface CO2 fluxes with fine temporal and spatial scales for atmospheric CO2 inversions. The estimated fluxes from SDPRM showed that the model is capable of producing flux estimates consistent with the ones inferred from atmospheric CO2 inversion or simulated from process-based models. In this Technical Note, different analyses were carried out to test the sensitivity of the estimated fluxes of GPP and CO2 to their driving forces. The spatial patterns of the climatic controls (temperature, precipitation, water) on the interannual variability of GPP are consistent with previous studies, even though SDPRM has a very simple structure and few adjustable parameters and hence it is much easier to modify in an inversion than more sophisticated process-based models. In SDPRM, temperature is a limiting factor for the interannual variability of Reco over cold boreal forest, while precipitation is the main limiting factor of Reco over the tropics and the southern hemisphere, consistent with previous regional studies.

scholarly journals Mapping irrigated areas using multi-sensor remote sensing data in a Mediterranean environment

2016 ◽  
Author(s):  
Pasquale Nino ◽  
Silvia Vanino ◽  
Flavio Lupia ◽  
Giuseppe Pulighe ◽  
Carlo De Michele ◽  
...  
Keyword(s):  
Spatial Scales ◽  
Remote Sensing Data ◽  
Arid Environment ◽  
Earth Observation ◽  
Irrigation District ◽  
Landsat 8 ◽  
Vegetation Development ◽  
Vegetation Growth ◽  
Growing Seasons ◽  
Semi Arid

Water managers need map of irrigated areas (defined as the identification of their location and their areal extent) to plan a rational use of water under limited availability and to prevent the unauthorized withdrawals. Many authors have shown that the Earth Observation techniques are an effective tool for mapping irrigated areas worldwide at different spatial scales (global/regional/and local). This study presents a methodology for mapping irrigated areas in semi-arid environment based on Earth Observation techniques and by fully exploiting datasets freely available processed by open source software and tools. Data acquired with the Landsat 8 Operational Land Imager (OLI) and the new Sentinel 2A MultiSpectral Instrument (MSI) sensors were integrated to obtain cloud free dense time series allowing to monitor the vegetation development throughout the growing seasons. Irrigated areas were identified by analysing the growing patterns under water deficit conditions from NDVI values under the assumption that, in arid and semi-arid environment (like the Mediterranean Region), high trend of vegetation growth are compatible only with irrigation. The method was applied in the Cixerri Consortium Irrigation District located in South of Sardinia (Italy).

scholarly journals Mapping irrigated areas using multi-sensor remote sensing data in a Mediterranean environment

2016 ◽  
Author(s):  
Pasquale Nino ◽  
Silvia Vanino ◽  
Flavio Lupia ◽  
Guido D'Urso ◽  
Carlo De Michele ◽  
...  
Keyword(s):  
Spatial Scales ◽  
Remote Sensing Data ◽  
Arid Environment ◽  
Earth Observation ◽  
Irrigation District ◽  
Landsat 8 ◽  
Vegetation Development ◽  
Vegetation Growth ◽  
Growing Seasons ◽  
Semi Arid

Water managers need map of irrigated areas (defined as the identification of their location and their areal extent) to plan a rational use of water under limited availability and to prevent the unauthorized withdrawals. Many authors have shown that the Earth Observation techniques are an effective tool for mapping irrigated areas worldwide at different spatial scales (global/regional/and local). This study presents a methodology for mapping irrigated areas in semi-arid environment based on Earth Observation techniques and by fully exploiting datasets freely available processed by open source software and tools. Data acquired with the Landsat 8 Operational Land Imager (OLI) and the new Sentinel 2A MultiSpectral Instrument (MSI) sensors were integrated to obtain cloud free dense time series allowing to monitor the vegetation development throughout the growing seasons. Irrigated areas were identified by analysing the growing patterns under water deficit conditions from NDVI values under the assumption that, in arid and semi-arid environment (like the Mediterranean Region), high trend of vegetation growth are compatible only with irrigation. The method was applied in the Cixerri Consortium Irrigation District located in South of Sardinia (Italy).

scholarly journals Technical Note: The Simple Diagnostic Photosynthesis and Respiration Model (SDPRM)

2012 ◽  
Vol 9 (10) ◽  
pp. 15127-15174 ◽  
Author(s):  
B. Badawy ◽  
C. Rödenbeck ◽  
M. Reichstein ◽  
N. Carvalhais ◽  
M. Heimann
Keyword(s):  
Interannual Variability ◽  
Ecosystem Respiration ◽  
Gross Primary Production ◽  
Technical Note ◽  
Atmospheric Co2 ◽  
Limiting Factor ◽  
Model Parameters ◽  
Respiration Model ◽  
Photosynthesis And Respiration ◽  
Process Based Models

Abstract. We present a Simple Diagnostic Photosynthesis and Respiration Model (SDPRM) that has been developed based on pre-existing formulations. The photosynthesis model is based on the light use efficiency logic, suggested by Monteith1977, for calculating the Gross Primary Production (GPP) while the ecosystem respiration (Reco) model is based on the formulations introduced by Lloyd1994 and modified by Reichstein2003. SDPRM is driven by satellite-derived fAPAR (fraction of Absorbed Photosynthetically Active Radiation) and climate data from NCEP/NCAR. The model estimates 3-hourly values of GPP for seven major biomes and daily Reco. The motivation is to provide a-priori fields of surface CO2 fluxes with fine temporal and spatial scales, and their derivatives with respect to adjustable model parameters, for atmospheric CO2 inversions. The estimated fluxes from SDPRM showed that the model is capable of producing flux estimates consistent with the ones inferred from atmospheric CO2 inversion or simulated from process-based models. In this Technical Note, different analyses were carried out to test the sensitivity of the estimated fluxes of GPP and Reco to their driving forces. The spatial patterns of the climatic controls (temperature, precipitation, water) on the interannual variability of GPP are consistent with previous studies even though SDPRM has a very simple structure and few adjustable parameters, and hence it is much easier to modify than more sophisticated process-based models used in these previous studies. According to SDPRM, the results show that temperature is a limiting factor for the interannual variability of Reco over the cold boreal forest, while precipitation is the main limiting factor of Reco over the tropics and the southern hemisphere, consistent with previous regional studies.

scholarly journals Multispectral Remote Sensing of Wetlands in Semi-Arid and Arid Areas: A Review on Applications, Challenges and Possible Future Research Directions

2020 ◽  
Vol 12 (24) ◽  
pp. 4190
Author(s):  
Siyamthanda Gxokwe ◽  
Timothy Dube ◽  
Dominic Mazvimavi
Keyword(s):  
Remote Sensing ◽  
Arid Regions ◽  
Remote Sensing Data ◽  
Sub Saharan Africa ◽  
Future Research ◽  
Multispectral Imagery ◽  
Aquatic Life ◽  
Sensing Data ◽  
Spatial Coverage ◽  
Semi Arid

Wetlands are ranked as very diverse ecosystems, covering about 4–6% of the global land surface. They occupy the transition zones between aquatic and terrestrial environments, and share characteristics of both zones. Wetlands play critical roles in the hydrological cycle, sustaining livelihoods and aquatic life, and biodiversity. Poor management of wetlands results in the loss of critical ecosystems goods and services. Globally, wetlands are degrading at a fast rate due to global environmental change and anthropogenic activities. This requires holistic monitoring, assessment, and management of wetlands to prevent further degradation and losses. Remote-sensing data offer an opportunity to assess changes in the status of wetlands including their spatial coverage. So far, a number of studies have been conducted using remotely sensed data to assess and monitor wetland status in semi-arid and arid regions. A literature search shows a significant increase in the number of papers published during the 2000–2020 period, with most of these studies being in semi-arid regions in Australia and China, and few in the sub-Saharan Africa. This paper reviews progress made in the use of remote sensing in detecting and monitoring of the semi-arid and arid wetlands, and focuses particularly on new insights in detection and monitoring of wetlands using freely available multispectral sensors. The paper firstly describes important characteristics of wetlands in semi-arid and arid regions that require monitoring in order to improve their management. Secondly, the use of freely available multispectral imagery for compiling wetland inventories is reviewed. Thirdly, the challenges of using freely available multispectral imagery in mapping and monitoring wetlands dynamics like inundation, vegetation cover and extent, are examined. Lastly, algorithms for image classification as well as challenges associated with their uses and possible future research are summarised. However, there are concerns regarding whether the spatial and temporal resolutions of some of the remote-sensing data enable accurate monitoring of wetlands of varying sizes. Furthermore, it was noted that there were challenges associated with the both spatial and spectral resolutions of data used when mapping and monitoring wetlands. However, advancements in remote-sensing and data analytics provides new opportunities for further research on wetland monitoring and assessment across various scales.

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