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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Biogeosciences Remote Sensing".

Deadline for manuscript submissions: closed (30 June 2019) | Viewed by 54593

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Special Issue Editors

Dr. Alemu Gonsamo

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Guest Editor

School of Geography & Earth Sciences, McMaster University, 1280 Main Street West, Hamilton, ON L8S 4K1, Canada
Interests: earth observation; global change ecology; phenology; remote sensing of vegetation; terrestrial carbon cycle

Dr. Holly Croft

E-Mail Website
Guest Editor

Department of Geography and Planning, University of Toronto, 100 St. George Street, Toronto, ON M5S 3G3, Canada
Interests: remote sensing of vegetation; terrestrial carbon cycle; photosynthetic trait mapping; phenology; ecophysiology

Dr. Mirco Migliavacca

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Guest Editor

Department of Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Hans Knoll Strasse 10, Jena, Germany
Interests: carbon cycle; phenology; sun-induced fluorescence; carbon-water interactions

Dr. Gregory Duveiller

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Guest Editor

Directorate D – Sustainable Resources - Bio-Economy Unit, Joint Research Centre, European Commission, Via Enrico Fermi 2749, TP261, 26A/010B, I-21027 Ispra (VA), Italy
Interests: carbon cycle; terrestrial ecosystem response to climate; sun-induced fluorescence; land use dynamics in Earth system; phenology

Special Issue Information

Dear Colleagues,

The terrestrial carbon cycle is controlled not only by photosynthesis, but also by respiration, carbon allocation, disturbance and rates of carbon turnover. However, these processes remain difficult to measure and challenging to model. As terrestrial ecosystem carbon cycle models become increasingly sophisticated, the level of uncertainty has also increased, as more mechanisms have been incorporated into the models. Therefore, spatially explicit quantification of terrestrial carbon budget remains uncertain. In this issue, we welcome contributions that make use of legacy or modern remote sensing observations to improve the characterisation of terrestrial carbon cycle processes. We particularly welcome novel remote sensing techniques and applications, such as chlorophyll fluorescence, CO₂ flux observations, and photosynthetic trait mapping and their integration into mechanistic models to better understand carbon cycle processes. Model-data integration and observational studies at leaf, plant, field, regional and global scales are also welcome. Potential topics for research and review articles that make use of remote sensing observations include, but are not limited to:

Photosynthesis
Respiration
Net ecosystem productivity
Photosynthetic trait mapping
Satellite observation of terrestrial CO₂ flux
Terrestrial ecosystem carbon cycle
Terrestrial ecosystem and climate change
Dynamic global vegetation models
Leaf-to-canopy photosynthesis scaling
Model-data integration for carbon cycle modelling
Terrestrial remote sensing in Earth system models
Application of chlorophyll fluorescence for photosynthesis mapping

Dr. Alemu Gonsamo
Dr. Holly Croft
Dr. Mirco Migliavacca
Dr. Gregory Duveiller
Guest Editors

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Keywords

Carbon cycle
Photosynthesis
Respiration
Net ecosystem productivity
Leaf-to-canopy photosynthesis scaling
Model-data integration for carbon cycle modelling
Terrestrial CO₂ flux observation

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Published Papers (7 papers)

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Research

12 pages, 2441 KiB

Open AccessArticle

Does Earlier and Increased Spring Plant Growth Lead to Reduced Summer Soil Moisture and Plant Growth on Landscapes Typical of Tundra-Taiga Interface?

by Alemu Gonsamo, Michael T. Ter-Mikaelian, Jing M. Chen and Jiaxin Chen

Remote Sens. 2019, 11(17), 1989; https://doi.org/10.3390/rs11171989 - 23 Aug 2019

Cited by 19 | Viewed by 5245

Abstract

Over the past four decades, satellite observations have shown intensified global greening. At the same time, widespread browning and reversal of or stalled greening have been reported at high latitudes. One of the main reasons for this browning/lack of greening is thought to be warming-induced water stress, i.e., soil moisture depletion caused by earlier spring growth and increased summer evapotranspiration. To investigate these phenomena, we use MODIS collection 6, Global Inventory Modeling and Mapping Studies third-generation (GIMMS) normalized difference vegetation index (NDVI3g), and Global Land Evaporation Amsterdam Model (GLEAM) satellite-based root-zone soil moisture data. The study area was the Far North of Ontario (FNO), 453,788 km² of heterogeneous landscape typical of the tundra-taiga interface, consisting of unmanaged boreal forests growing on mineral and peat soils, wetlands, and the most southerly area of tundra. The results indicate that the increased plant growth in spring leads to decreased summer growth. Lower summer soil moisture is related to increased spring plant growth in areas with lower soil moisture content. We also found that earlier start of growing season leads to decreased summer and peak season maximum plant growth. In conclusion, increased spring plant growth and earlier start of growing season deplete summer soil moisture and decrease the overall summer plant growth even in temperature-limited high latitude ecosystems. Our findings contribute to evolving understanding of changes in vegetation dynamics in relation to climate in northern high latitude terrestrial ecosystems. Full article

(This article belongs to the Special Issue Terrestrial Carbon Cycle)

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Graphical abstract
Full article ">Figure 1
A 30 m land cover map and images of landscape characteristics typical of ecosystems of the Far North of Ontario. Land cover data courtesy of Ontario Ministry of Natural Resources and Forestry, Version 1.4 (year 2014), Far North Land Cover; landscape scenes courtesy of Google Maps (<a href="http://www.google.ca/maps" target="_blank">www.google.ca/maps</a>). Full article ">Figure 2
Comparison between summer-time data-model estimate of root-zone soil moisture (GLEAM) and satellite observed Climate Change Initiative (CCI) soil moisture, both in m3/m3. The scatter points include all 0.25° × 0.25° Latitude-Longitude grid cells which have June–August datasets between 1982 and 2015. Full article ">Figure 3
Density plot of linear slopes between the spring and summer greenness from MODIS normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) for 2000–2017 per land cover classes. The dashed vertical lines indicate the mean value of slope while the solid vertical lines indicate the slope value of zero, the latter added for visual clarity. Mean of linear slope is significantly different from zero in all cases (p < 0.05, two-tailed one-sample t-test). Full article ">Figure 4
Linear slopes of summer greenness with spring greenness (a) and start of growing season (SOS) (b) along soil moisture (m3/m3) classes. Linear slopes of summer greenness with spring greenness (c) and SOS) (d) along tree cover classes. VI is vegetation index (i.e., NDVI or EVI). Error bars represent one standard deviation of spatial slopes in each soil moisture or tree cover class. The linear slopes are computed at native spatial resolutions of Global Inventory Modeling and Mapping Studies third-generation (GIMMS) and MODIS data. Mean of linear slope is significantly different from zero in all cases (p < 0.05, two-tailed one-sample t-test). Full article ">Figure 5
Linear slopes of start of season (SOS) with maximum productivity obtained from GIMMS NDVI3g and MODIS NDVI plotted along the long-term mean summer soil moisture (m3/m3) (a) and tree cover (b) classes. VI is vegetation index (i.e., NDVI or EVI). Error bars represent one standard deviation of spatial slopes in each soil moisture or tree cover class. The linear slopes are computed at native spatial resolutions of GIMMS and MODIS data. Maximum productivity values are obtained from peak of season day (i.e., POSvalue) using the curve-fitting algorithm and directly from maximum annual value of the raw data (i.e., max). Mean of linear slope is significantly different from zero in all cases (p < 0.05, two-tailed one-sample t-test) except indicated as NS (not significant) otherwise. Full article ">Figure 6
Spatial distributions of the summer soil moisture correlation against the spring GIMMS (a), summer GIMMS (b), spring MODIS (c) and summer MODIS (d) NDVI. All coloured pixels for (a–d) are significantly correlated at 90% confidence level (one-tailed). The relationships of spatial trends were computed after projecting all datasets into 0.25° × 0.25° Latitude-Longitude grids. Long-term (1980−2016) mean summer soil moisture is shown in (e). Full article ">

29 pages, 9733 KiB

Open AccessArticle

Quantifying the Impacts of Land-Use and Climate on Carbon Fluxes Using Satellite Data across Texas, U.S.

by Ram L. Ray, Ademola Ibironke, Raghava Kommalapati and Ali Fares

Remote Sens. 2019, 11(14), 1733; https://doi.org/10.3390/rs11141733 - 23 Jul 2019

Cited by 8 | Viewed by 6206

Abstract

Climate change and variability, soil types and soil characteristics, animal and microbial communities, and photosynthetic plants are the major components of the ecosystem that affect carbon sequestration potential of any location. This study used NASA’s Soil Moisture Active Passive (SMAP) Level 4 carbon products, gross primary productivity (GPP), and net ecosystem exchange (NEE) to quantify their spatial and temporal variabilities for selected terrestrial ecosystems across Texas during the 2015–2018 study period. These SMAP carbon products are available at 9 km spatial resolution on a daily basis. The ten selected SMAP grids are located in seven climate zones and dominated by five major land uses (developed, crop, forest, pasture, and shrub). Results showed CO₂ emissions and uptake were affected by land-use and climatic conditions across Texas. It was also observed that climatic conditions had more impact on CO₂ emissions and uptake than land-use in this state. On average, South Central Plains and East Central Texas Plains ecoregions of East Texas and Western Gulf Coastal Plain ecoregion of Upper Coast climate zones showed higher GPP flux and potential carbon emissions and uptake than other climate zones across the state, whereas shrubland on the Trans Pecos climate zone showed lower GPP flux and carbon emissions/uptake. Comparison of GPP and NEE distribution maps between 2015 and 2018 confirmed substantial changes in carbon emissions and uptake across Texas. These results suggest that SMAP carbon products can be used to study the terrestrial carbon cycle at regional to global scales. Overall, this study helps to understand the impacts of climate, land-use, and ecosystem dynamics on the terrestrial carbon cycle. Full article

(This article belongs to the Special Issue Terrestrial Carbon Cycle)

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27 pages, 4841 KiB

Open AccessArticle

Effects of the Temporal Aggregation and Meteorological Conditions on the Parameter Robustness of OCO-2 SIF-Based and LUE-Based GPP Models for Croplands

by Xiaofeng Lin, Baozhang Chen, Huifang Zhang, Fei Wang, Jing Chen, Lifeng Guo and Yawen Kong

Remote Sens. 2019, 11(11), 1328; https://doi.org/10.3390/rs11111328 - 3 Jun 2019

Cited by 7 | Viewed by 3699

Abstract

Global retrieval of solar-induced chlorophyll fluorescence (SIF) using remote sensing by means of satellites has been developed rapidly in recent years. Exploring how SIF could improve the characterization of photosynthesis and its role in the land surface carbon cycle has gradually become a very important and active area. However, compared with other gross primary production (GPP) models, the robustness of the parameterization of the SIF model under different circumstances has rarely been investigated. In this study, we examined and compared the effects of temporal aggregation and meteorological conditions on the stability of model parameters for the SIF model (

ε / S I F_{yield}

), the one-leaf light-use efficiency (SL-LUE) model (

ε_{\max}

), and the two-leaf LUE (TL-LUE) model (

ε_{msu}

and

ε_{msh}

). The three models were parameterized based on a maize–wheat rotation eddy-covariance flux tower data in Yucheng, Shandong Province, China by using the Metropolis–Hasting algorithm. The results showed that the values of the

ε / S I F_{yield}

and

ε_{\max}

were similarly robust and considerably more stable than

ε_{msu}

and

ε_{msh}

for all temporal aggregation levels. Under different meteorological conditions, all the parameters showed a certain degree of fluctuation and were most affected at the mid-day scale, followed by the monthly scale and finally at the daily scale. Nonetheless, the averaged coefficient of variation (

C V

) of

ε / S I F_{yield}

was relatively small (15.0%) and was obviously lower than

ε_{\max}

(

C V

= 27.0%),

ε_{msu}

(

C V

= 43.2%), and

ε_{msh}

(

C V

= 53.1%). Furthermore, the SIF model’s performance for estimating GPP was better than that of the SL-LUE model and was comparable to that of the TL-LUE model. This study indicates that, compared with the LUE-based models, the SIF-based model without climate-dependence is a good predictor of GPP and its parameter is more likely to converge for different temporal aggregation levels and under varying environmental restrictions in croplands. We suggest that more flux tower data should be used for further validation of parameter convergence in other vegetation types. Full article

(This article belongs to the Special Issue Terrestrial Carbon Cycle)

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25 pages, 5529 KiB

Open AccessArticle

Evaluating the Effectiveness of Using Vegetation Indices Based on Red-Edge Reflectance from Sentinel-2 to Estimate Gross Primary Productivity

by Shangrong Lin, Jing Li, Qinhuo Liu, Longhui Li, Jing Zhao and Wentao Yu

Remote Sens. 2019, 11(11), 1303; https://doi.org/10.3390/rs11111303 - 31 May 2019

Cited by 66 | Viewed by 8633

Abstract

Gross primary productivity (GPP) is the most important component of terrestrial carbon flux. Red-edge (680–780 nm) reflectance is sensitive to leaf chlorophyll content, which is directly correlated with photosynthesis as the pigment pool, and it has the potential to improve GPP estimation. The European Space Agency (ESA) Sentinel-2A and B satellites provide red-edge bands at 20-m spatial resolution on a five-day revisit period, which can be used for global estimation of GPP. Previous studies focused mostly on improving cropland GPP estimation using red-edge bands. In this study, we firstly evaluated the relationship between eight vegetation indices (VIs) retrieved from Sentinel-2 imagery in association with incident photosynthetic active radiation (PAR_in) and carbon flux tower GPP (GPP_EC) across three forest and two grassland sites in Australia. We derived a time series of five red-edge VIs and three non-red-edge VIs over the CO₂ flux tower footprints at 16-day time intervals and compared both temporal and spatial variations. The results showed that the relationship between the red-edge index (CIr,

\frac{ρ 783}{ρ 705} - 1

) multiplied by PAR_in and GPP_EC had the highest correlation (R² = 0.77, root-mean-square error (RMSE) = 0.81 gC∙m⁻²∙day⁻¹) at the two grassland sites. The CIr also showed consistency (rRMSE defined as RMSE/mean GPP, lower than 0.25) across forest and grassland sites. The high spatial resolution of the Sentinel-2 data provided more detailed information to adequately characterize the GPP variance at spatially heterogeneous areas. The high revisit period of Sentinel-2 exhibited temporal variance in GPP at the grassland sites; however, at forest sites, the flux-tower-based GPP variance could not be fully tracked by the limited satellite images. These results suggest that the high-spatial-resolution red-edge index from Sentinel-2 can improve large-scale spatio-temporal GPP assessments. Full article

(This article belongs to the Special Issue Terrestrial Carbon Cycle)

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22 pages, 4601 KiB

Open AccessArticle

Improved Modeling of Gross Primary Productivity of Alpine Grasslands on the Tibetan Plateau Using the Biome-BGC Model

by Yongfa You, Siyuan Wang, Yuanxu Ma, Xiaoyue Wang and Weihua Liu

Remote Sens. 2019, 11(11), 1287; https://doi.org/10.3390/rs11111287 - 30 May 2019

Cited by 35 | Viewed by 5684

Abstract

The ability of process-based biogeochemical models in estimating the gross primary productivity (GPP) of alpine vegetation is largely hampered by the poor representation of phenology and insufficient calibration of model parameters. The development of remote sensing technology and the eddy covariance (EC) technique has made it possible to overcome this dilemma. In this study, we have incorporated remotely sensed phenology into the Biome-BGC model and calibrated its parameters to improve the modeling of GPP of alpine grasslands on the Tibetan Plateau (TP). Specifically, we first used the remotely sensed phenology to modify the original meteorological-based phenology module in the Biome-BGC to better prescribe the phenological states within the model. Then, based on the GPP derived from EC measurements, we combined the global sensitivity analysis method and the simulated annealing optimization algorithm to effectively calibrate the ecophysiological parameters of the Biome-BGC model. Finally, we simulated the GPP of alpine grasslands on the TP from 1982 to 2015 based on the Biome-BGC model after a phenology module modification and parameter calibration. The results indicate that the improved Biome-BGC model effectively overcomes the limitations of the original Biome-BGC model and is able to reproduce the seasonal dynamics and magnitude of GPP in alpine grasslands. Meanwhile, the simulated results also reveal that the GPP of alpine grasslands on the TP has increased significantly from 1982 to 2015 and shows a large spatial heterogeneity, with a mean of 289.8 gC/m²/yr or 305.8 TgC/yr. Our study demonstrates that the incorporation of remotely sensed phenology into the Biome-BGC model and the use of EC measurements to calibrate model parameters can effectively overcome the limitations of its application in alpine grassland ecosystems, which is important for detecting trends in vegetation productivity. This approach could also be upscaled to regional and global scales. Full article

(This article belongs to the Special Issue Terrestrial Carbon Cycle)

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16 pages, 3900 KiB

Open AccessArticle

Underestimates of Grassland Gross Primary Production in MODIS Standard Products

by Xiaoyan Zhu, Yanyan Pei, Zhaopei Zheng, Jinwei Dong, Yao Zhang, Junbang Wang, Lajiao Chen, Russell B. Doughty, Geli Zhang and Xiangming Xiao

Remote Sens. 2018, 10(11), 1771; https://doi.org/10.3390/rs10111771 - 8 Nov 2018

Cited by 41 | Viewed by 5174

Abstract

As the biggest carbon flux of terrestrial ecosystems from photosynthesis, gross primary productivity (GPP) is an important indicator in understanding the carbon cycle and biogeochemical process of terrestrial ecosystems. Despite advances in remote sensing-based GPP modeling, spatial and temporal variations of GPP are still uncertain especially under extreme climate conditions such as droughts. As the only official products of global spatially explicit GPP, MOD17A2H (GPP_MOD) has been widely used to assess the variations of carbon uptake of terrestrial ecosystems. However, systematic assessment of its performance has rarely been conducted especially for the grassland ecosystems where inter-annual variability is high. Based on a collection of GPP datasets (GPP_EC) from a global network of eddy covariance towers (FluxNet), we compared GPP_MOD and GPP_EC at all FluxNet grassland sites with more than five years of observations. We evaluated the performance and robustness of GPP_MOD in different grassland biomes (tropical, temperate, and alpine) by using a bootstrapping method for calculating 95% confident intervals (CI) for the linear regression slope, coefficients of determination (R²), and root mean square errors (RMSE). We found that GPP_MOD generally underestimated GPP by about 34% across all biomes despite a significant relationship (R² = 0.66 (CI, 0.63–0.69), RMSE = 2.46 (2.33–2.58) g Cm⁻² day⁻¹) for the three grassland biomes. GPP_MOD had varied performances with R² values of 0.72 (0.68–0.75) (temperate), 0.64 (0.59–0.68) (alpine), and 0.40 (0.27–0.52) (tropical). Thus, GPP_MOD performed better in low GPP situations (e.g., temperate grassland type), which further indicated that GPP_MOD underestimated GPP. The underestimation of GPP could be partly attributed to the biased maximum light use efficiency (ε_max) values of different grassland biomes. The uncertainty of the fraction of absorbed photosynthetically active radiation (FPAR) and the water scalar based on the vapor pressure deficit (VPD) could have other reasons for the underestimation. Therefore, more accurate estimates of GPP for different grassland biomes should consider improvements in ε_max, FPAR, and the VPD scalar. Our results suggest that the community should be cautious when using MODIS GPP products to examine spatial and temporal variations of carbon fluxes. Full article

(This article belongs to the Special Issue Terrestrial Carbon Cycle)

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Graphical abstract
Full article ">Figure 1
Distribution of 15 FLUXNET sites selected with more than five years of observations. Full article ">Figure 2
Seasonal variations of GPPMOD and observed GPPEC at all study sites. Full article ">Figure 3
The relationship between GPPMOD and GPPEC for all sites. The short-dashed line is a 1:1 line. The unit of RMSE was g C m−2 day−1. Full article ">Figure 4
The relationship between GPPMOD and GPPEC for all sites. The short-dashed line is a 1:1 line. The unit of RMSE was g C m−2 day−1. Full article ">Figure 5
The relationships between GPPEC and GPPMOD for the temperate, tropical, and alpine grassland biomes. The short-dashed line is a 1:1 line. The unit of RMSE was g C m−2 day−1. Full article ">Figure 6
The relationship between GPPMOD and GPPEC for all sites with the εmax (<math display="inline"><semantics> <mrow> <msub> <mi>ε</mi> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </msub> </mrow> </semantics></math>-BPLUT) used in the MOD17 GPP algorithm was replaced by the estimated εmax (<math display="inline"><semantics> <mrow> <msub> <mi>ε</mi> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </msub> </mrow> </semantics></math>-EST). The unit of RMSE was g C m−2 day−1. Full article ">Figure 7
The relationships between Wscalar of MOD17A2H GPP products and GPPEC, respectively. The solid line is a linear regression. The short-dashed line is a 1:1 line. Full article ">

38 pages, 9570 KiB

Open AccessArticle

Estimation of Terrestrial Global Gross Primary Production (GPP) with Satellite Data-Driven Models and Eddy Covariance Flux Data

by Joanna Joiner, Yasuko Yoshida, Yao Zhang, Gregory Duveiller, Martin Jung, Alexei Lyapustin, Yujie Wang and Compton J. Tucker

Remote Sens. 2018, 10(9), 1346; https://doi.org/10.3390/rs10091346 - 23 Aug 2018

Cited by 145 | Viewed by 18393

Abstract

We estimate global terrestrial gross primary production (GPP) based on models that use satellite data within a simplified light-use efficiency framework that does not rely upon other meteorological inputs. Satellite-based geometry-adjusted reflectances are from the MODerate-resolution Imaging Spectroradiometer (MODIS) and provide information about vegetation structure and chlorophyll content at both high temporal (daily to monthly) and spatial (∼1 km) resolution. We use satellite-derived solar-induced fluorescence (SIF) to identify regions of high productivity crops and also evaluate the use of downscaled SIF to estimate GPP. We calibrate a set of our satellite-based models with GPP estimates from a subset of distributed eddy covariance flux towers (FLUXNET 2015). The results of the trained models are evaluated using an independent subset of FLUXNET 2015 GPP data. We show that variations in light-use efficiency (LUE) with incident PAR are important and can be easily incorporated into the models. Unlike many LUE-based models, our satellite-based GPP estimates do not use an explicit parameterization of LUE that reduces its value from the potential maximum under limiting conditions such as temperature and water stress. Even without the parameterized downward regulation, our simplified models are shown to perform as well as or better than state-of-the-art satellite data-driven products that incorporate such parameterizations. A significant fraction of both spatial and temporal variability in GPP across plant functional types can be accounted for using our satellite-based models. Our results provide an annual GPP value of ∼140 Pg C year

^{- 1}

for 2007 that is within the range of a compilation of observation-based, model, and hybrid results, but is higher than some previous satellite observation-based estimates. Full article

(This article belongs to the Special Issue Terrestrial Carbon Cycle)

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