Evapotranspiration (ET) from the land surface is an important component of the terrestrial hydrological cycle. Since the advent of Earth observation by satellites, various models have been developed ...to use thermal and shortwave remote sensing data for ET estimation. In this review, we provide a brief account of the key milestones in the history of remote sensing ET model development in two categories: temperature-based and conductance-based models. Temperature-based ET models utilize land surface temperature (LST) observed through thermal remote sensing to calculate the sensible heat flux from which ET is estimated as a residual of the surface energy balance or to estimate the evaporative fraction from which ET is derived from the available energy. Models of various complexities have been developed to estimate ET from surfaces of different vegetation fractions. One-source models combine soil and vegetation into a composite surface for ET estimation, while two-source models estimate ET of soil and vegetation components separately. Image contexture-based triangular and trapezoid models are simple and effective temperature-based ET models based on spatial and/or temporal variation patterns of LST. Several effective temporal scaling schemes are available for extending instantaneous temperature-based ET estimation to daily or longer time periods. Conductance-based ET models usually use the Penman-Monteith (P-M) equation to estimate ET with shortwave remote sensing data. A key put to these models is canopy conductance to water vapor, which depends on canopy structure and leaf stomatal conductance. Shortwave remote sensing data are used to determine canopy structural parameters, and stomatal conductance can be estimated in different ways. Based on the principle of the coupling between carbon and water cycles, stomatal conductance can be reliably derived from the plant photosynthesis rate. Three types of photosynthesis models are available for deriving stomatal or canopy conductance: (1) big-leaf models for the total canopy conductance, (2) two-big-leaf models for canopy conductances for sunlit and shaded leaf groups, and (3) two-leaf models for stomatal conductances for the average sunlit and shaded leaves separately. Correspondingly, there are also big-leaf, two-big-leaf and two-leaf ET models based on these conductances. The main difference among them is the level of aggregation of conductances before the P-M equation is used for ET estimation, with big-leaf models having the highest aggregation. Since the relationship between ET and conductance is nonlinear, this aggregation causes negative bias errors, with the big-leaf models having the largest bias. It is apparent from the existing literature that two-leaf conductance-based ET models have the least bias in comparison with flux measurements. Based on this review, we make the following recommendations for future work: (1) improving key remote sensing products needed for ET mapping purposes, including soil moisture, foliage clumping index, and leaf carboxylation rate, (2) combining temperature-based and conductance-based models for regional ET estimation, (3) refining methodologies for tight coupling between carbon and water cycles, (4) fully utilizing vegetation structural and biochemical parameters that can now be reliably retrieved from shortwave remote sensing, and (5) to improve regional and global ET monitoring capacity.
•Separating ET models into temperature-based and conductance-based models•Comprehensive accounts for the historical developments of models of these two types•In-depth analysis of bias errors by big-leaf, two-big-leaf and two-leaf ET conductance-based ET models•Future directions for ET research are identified.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Satellite observations show that leaf area index (LAI) has increased globally since 1981, but the impact of this vegetation structural change on the global terrestrial carbon cycle has not been ...systematically evaluated. Through process-based diagnostic ecosystem modeling, we find that the increase in LAI alone was responsible for 12.4% of the accumulated terrestrial carbon sink (95 ± 5 Pg C) from 1981 to 2016, whereas other drivers of CO
fertilization, nitrogen deposition, and climate change (temperature, radiation, and precipitation) contributed to 47.0%, 1.1%, and -28.6% of the sink, respectively. The legacy effects of past changes in these drivers prior to 1981 are responsible for the remaining 65.5% of the accumulated sink from 1981 to 2016. These results refine the attribution of the land sink to the various drivers and would help constrain prognostic models that often have large uncertainties in simulating changes in vegetation and their impacts on the global carbon cycle.
Improving the accuracy of estimates of forest carbon exchange is a central priority for understanding ecosystem response to increased atmospheric CO2 levels and improving carbon cycle modelling. ...However, the spatially continuous parameterization of photosynthetic capacity (Vcmax) at global scales and appropriate temporal intervals within terrestrial biosphere models (TBMs) remains unresolved. This research investigates the use of biochemical parameters for modelling leaf photosynthetic capacity within a deciduous forest. Particular attention is given to the impacts of seasonality on both leaf biophysical variables and physiological processes, and their interdependent relationships. Four deciduous tree species were sampled across three growing seasons (2013–2015), approximately every 10 days for leaf chlorophyll content (ChlLeaf) and canopy structure. Leaf nitrogen (NArea) was also measured during 2014. Leaf photosynthesis was measured during 2014–2015 using a Li‐6400 gas‐exchange system, with A‐Ci curves to model Vcmax. Results showed that seasonality and variations between species resulted in weak relationships between Vcmax normalized to 25°C (Vcmax25) and NArea (R2 = 0.62, P < 0.001), whereas ChlLeaf demonstrated a much stronger correlation with Vcmax25 (R2 = 0.78, P < 0.001). The relationship between ChlLeaf and NArea was also weak (R2 = 0.47, P < 0.001), possibly due to the dynamic partitioning of nitrogen, between and within photosynthetic and nonphotosynthetic fractions. The spatial and temporal variability of Vcmax25 was mapped using Landsat TM/ETM satellite data across the forest site, using physical models to derive ChlLeaf. TBMs largely treat photosynthetic parameters as either fixed constants or varying according to leaf nitrogen content. This research challenges assumptions that simple NArea–Vcmax25 relationships can reliably be used to constrain photosynthetic capacity in TBMs, even within the same plant functional type. It is suggested that ChlLeaf provides a more accurate, direct proxy for Vcmax25 and is also more easily retrievable from satellite data. These results have important implications for carbon modelling within deciduous ecosystems.
Improving modelled estimates of forest carbon exchange requires accurate parameterization of photosynthetic capacity (Vcmax) within terrestrial biosphere models (TBMs). This research investigates leaf biochemical variables for modelling photosynthetic parameters within a deciduous forest. Seasonality and interspecies variability resulted in weak correlations between Vcmax and leaf nitrogen; challenging assumptions that simple nitrogen–Vcmax25 relationships can reliably constrain photosynthetic capacity in TBMs. Conversely, leaf chlorophyll (ChlLeaf) accounted for 76% of Vcmax25 variability, suggesting that ChlLeaf provides a more accurate and direct proxy for Vcmax.
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Despite much progress has been made in recent years, the estimation of terrestrial gross primary productivity (GPP) remains highly uncertain. Recent studies have demonstrated the usefulness of ...sun-induced chlorophyll fluorescence (SIF) from plants for estimating GPP. In this study, the relationships of GPP to SIF and Photochemical Reflectance Index (PRI) under different environmental conditions are established using carbon flux data at 61 sites distributed globally and coincident SIF data from OCO-2 and PRI data from MODIS. We analyzed OCO-2 SIF data at both 757 nm and 771 nm against instantaneous and daily GPP values derived from the tower flux data. Our results for these four cases are similar, and those with SIF at 757 nm and daily GPP show that: (1) a significant relationship is found between GPP and observed SIF (SIFobs) for all vegetation types (R2 = 0.53, RMSE = 4.28 μmol m−2 s−1p < .001); (2) NIRv, a product of the normalized difference vegetation index (NDVI) and near-infrared reflectance, can be used as an approximation of the canopy-escaping probability of emitted SIF to convert observed SIF at one angle to the hemispherical SIF emission that is better correlated to GPP. The relationship between GPP and SIFobs/NIRv was considerably improved from that between GPP and SIFobs (R2 = 0.67, RMSE = 3.39 μmol m−2 s−1, p < .001); (3) The remaining scatter in the relationship between GPP and SIFobs/NIRv is significantly negatively correlated to PRI (R2 = 0.28–0.34, p < .001), and PRI can be used to improve the relationship between GPP and SIFobs/NIRv, (R2 = 0.73, RMSE = 3.04 μmol m−2 s−1, p < .001); and (4) After the use of PRI in the regression, the variability of the slope of GPP against SIF among different plant functional types is greatly reduced from 33% to 19%. As PRI is an indicator of the non-photochemical quenching (NPQ), a pathway that is in parallel of photochemical quenching and chlorophyll fluorescence, these improvements suggest that the information of NPQ from PRI can be used in addition to SIF for better GPP estimation.
•GPP from 61 tower flux sites is well correlated with coincident SIF from OCO-2.•This correlation is significantly improved when SIF is corrected with NIRv.•The correlation is further improved using PRI as an indicator of NPQ.•The slope of SIF against GPP varies much less among cover types when PRI is used.
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In this paper, we present an approach for generating a consistent long‐term global leaf area index (LAI) product (1981–2011) by quantitative fusion of Moderate Resolution Imaging Spectroradiometer ...(MODIS) and historical Advanced Very High Resolution Radiometer (AVHRR) data. First, a MODIS LAI series was generated from MODIS data based on the GLOBCARBON LAI algorithm. Then, the relationships between AVHRR observations and MODIS LAI were established pixel by pixel using two data series during overlapped period (2000–2006). Then the AVHRR LAI back to 1981 was estimated from historical AVHRR observations based on these pixel‐level relationships. The long‐term LAI series was made up by combination of AVHRR LAI (1981–2000) and MODIS LAI (2000–2011). The LAI derived from AVHRR was intercompared with that from MODIS during the overlapped period. The results show that the LAIs from these two different sensors are good consistency, with LAI differences are within ±0.6 over 99.0% vegetated pixels. The long‐term LAI was also compared with field measurements, which has an error of 0.81 LAI on average. Compared with the LAI retrieved directly from the GLOBCARBON algorithm, the LAI derived by our method has a lower temporal noise, which means uncertainties from the low quality of AVHRR measurements can be reduced with the aid of high‐quality MODIS data. This product is hosted on the GlobalMapping Web site (http://www.globalmapping.org/globalLAI) for free download, which will provide a long‐term LAI over 30 years for modeling the carbon and water cycles.
Key Points
Generate a long‐term series (1981‐2011) of global LAI product
A method to produce a temporally consistent LAI product from MODIS and AVHRR
The LAI quality from the AVHRR can be improved with the aid of MODIS
Climate control on global vegetation productivity patterns has intensified in response to recent global warming. Yet, the contributions of the leading internal climatic variations to global ...vegetation productivity are poorly understood. Here, we use 30 years of global satellite observations to study climatic variations controls on continental and global vegetation productivity patterns. El Niño‐Southern Oscillation (ENSO) phases (La Niña, neutral, and El Niño years) appear to be a weaker control on global‐scale vegetation productivity than previously thought, although continental‐scale responses are substantial. There is also clear evidence that other non‐ENSO climatic variations have a strong control on spatial patterns of vegetation productivity mainly through their influence on temperature. Among the eight leading internal climatic variations, the East Atlantic/West Russia Pattern extensively controls the ensuing year vegetation productivity of the most productive tropical and temperate forest ecosystems of the Earth's vegetated surface through directionally consistent influence on vegetation greenness. The Community Climate System Model (CCSM4) simulations do not capture the observed patterns of vegetation productivity responses to internal climatic variations. Our analyses show the ubiquitous control of climatic variations on vegetation productivity and can further guide CCSM and other Earth system models developments to represent vegetation response patterns to unforced variability. Several winter time internal climatic variation indices show strong potentials on predicting growing season vegetation productivity two to six seasons ahead which enables national governments and farmers forecast crop yield to ensure supplies of affordable food, famine early warning, and plan management options to minimize yield losses ahead of time.
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Solar‐induced chlorophyll fluorescence (SIF) from the TROPOspheric Monitoring Instrument (TROPOMI), which has substantially improved spatial and temporal resolutions, will improve the global ...estimations of gross primary production (GPP) than previous satellite SIF data. However, the canopy‐leaving SIF observed by sensors (SIFobs) represents only a portion of the total canopy SIF emission (SIFtotal). This portion is sensitive to the canopy structure and observation direction, resulting in uncertainties in GPP estimations using SIFobs. Here we used the spectral invariant theory to derive global soil‐resistant SIFtotal (SIFtotal‐SR) from TROPOMI SIFobs and evaluated the SIFtotal‐SR performance in estimating GPP. We found that the clear differences in SIFobs between needleleaf forest and crops diminished for SIFtotal‐SR. SIFtotal‐SR increased the coefficient of determination (R2) by 0.09 and 0.11 against the flux tower instantaneous and daily GPP, respectively. This derived SIFtotal‐SR can be used to develop more robust GPP models and better constrain carbon cycle models.
Plain Language Summary
Photosynthesis is an essential process by which plants convert carbon dioxide (CO2) and water into sugars and provides the primary source of energy for life on Earth. Variations in photosynthesis also affect the CO2 concentration in the atmosphere, the water‐heat balance, and the climate. It is therefore important to estimate the photosynthesis rate, which is generally quantified as the gross primary production (GPP) at the large spatial and temporal scales. Solar‐induced chlorophyll fluorescence (SIF) retrieved from the recently launched TROPOspheric Monitoring Instrument, which has substantially improved spatial and temporal resolutions, has greater potential than previous satellite SIF data to be used as a proxy of photosynthesis. However, SIF observed by sensors (SIFobs) represents only a portion of the total canopy SIF emission from all leaves within a canopy (SIFtotal). This portion depends on the canopy structure and observation direction, hindering the application of SIFobs for GPP estimation. In this study, a correction method is proposed to convert directional SIFobs retrievals into SIFtotal by considering the soil effect in the conversion. The derived soil‐resistant SIFtotal could help to better estimate the terrestrial GPP compared with using SIFobs.
Key Points
Total canopy solar‐induced chlorophyll fluorescence (SIF) emission (SIFtotal) is derived from TROPOMI SIF with reflectance data
SIFtotal better correlates to gross primary production (GPP) than observed SIF (SIFobs)
The soil background effect on SIFtotal is minimized by estimating the probability of observing a sunlit background under vegetation
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Climate change is lengthening the growing season of the Northern Hemisphere extratropical terrestrial ecosystems, but little is known regarding the timing and dynamics of the peak season of plant ...activity. Here, we use 34‐year satellite normalized difference vegetation index (NDVI) observations and atmospheric CO2 concentration and δ13C isotope measurements at Point Barrow (Alaska, USA, 71°N) to study the dynamics of the peak of season (POS) of plant activity. Averaged across extratropical (>23°N) non‐evergreen‐dominated pixels, NDVI data show that the POS has advanced by 1.2 ± 0.6 days per decade in response to the spring‐ward shifts of the start (1.0 ± 0.8 days per decade) and end (1.5 ± 1.0 days per decade) of peak activity, and the earlier onset of the start of growing season (1.4 ± 0.8 days per decade), while POS maximum NDVI value increased by 7.8 ± 1.8% for 1982–2015. Similarly, the peak day of carbon uptake, based on calculations from atmospheric CO2 concentration and δ13C data, is advancing by 2.5 ± 2.6 and 4.3 ± 2.9 days per decade, respectively. POS maximum NDVI value shows strong negative relationships (p < .01) with the earlier onset of the start of growing season and POS days. Given that the maximum solar irradiance and day length occur before the average POS day, the earlier occurrence of peak plant activity results in increased plant productivity. Both the advancing POS day and increasing POS vegetation greenness are consistent with the shifting peak productivity towards spring and the increasing annual maximum values of gross and net ecosystem productivity simulated by coupled Earth system models. Our results further indicate that the decline in autumn NDVI is contributing the most to the overall browning of the northern high latitudes (>50°N) since 2011. The spring‐ward shift of peak season plant activity is expected to disrupt the synchrony of biotic interaction and exert strong biophysical feedbacks on climate by modifying the surface albedo and energy budget.
Climate change is lengthening the growing season of high latitude plant ecosystems, but little is known about the impact on summertime peak plant growth activity. Using satellite and atmospheric CO2 concentration data records, we find that the timing of peak plant growth is shifting towards spring. The spring‐ward shift of peak plant activity is contributing towards the increased CO2 uptake by high latitude plants. The spring‐ward shift of peak plant activity is expected to disrupt the synchrony of plant–pollinator interactions and exert strong feedback on climate by modifying the radiation reflected and absorbed by Earth surface.
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Drought and heat caused major disturbance in nature by interfering with plant phenology, and can also alter the vulnerability and resilience of terrestrial ecosystems. Existing research on the ...Mongolian Plateau has primarily focused on studying the response of the start (SOS) and end (EOS) of the growing season to drought and heat variations. However, there is still a lack of comprehensive understanding regarding the coupled effects of drought and heat on phenology across different land cover types. In this study, we retrieved SOS and EOS based on 34-year (1982–2015) normalized difference vegetation index (NDVI) dataset from Global Inventory Modeling and Mapping Studies (GIMMS). Results showed that grasslands and the Gobi-Desert show rapid advancement in SOS, and forests presented the slowest advancement in SOS, but SOS in croplands were delayed. EOS across four land cover types advanced, with the Gobi-Desert showed the highest rate of advancement and forests the lowest. Using the Palmer Drought Severity Index (PDSI) and soil temperature as the indicators of drought and thermal conditions, the responses of SOS and EOS to these two climate variables were evaluated. The advanced SOS driven by lower drought severity was detected in forests, grasslands, croplands and the Gobi-Desert. The dominant response of EOS to drought severity was positive in croplands, grasslands and forests, except for the Gobi-Desert, where drought severity had negative effects on EOS. Compared with the daily average soil temperature (STmean), the daily maximum soil temperature (STmax, daytime), and the daily minimum soil temperature (STmin, nighttime), the daily diurnal soil temperature range (DSTR, where DSTR = STmax − STmin) between night and day were the most suitable indicators for assessing the response of SOS and EOS to soil temperature. Strong negative correlation between SOS and the preseason DSTR was pronounced in all land cover types on the Mongolian Plateau. However, EOS was negatively correlated with the preseason DSTR only in the Gobi-Desert. Last but not least, normalized sensitivity assessments reveal that the negative impacts of DSTR on SOS and EOS were the main controlling factors on the Mongolian Plateau phenology, followed by the couple negative effects of drought severity and DSTR.
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•DSTR (STmax − STmin) best gauges phenology to soil temperature.•Higher PDSI/DSTR inversely affected phenology sensitivity patterns.•Normalized sensitivity assessments reveal that the negative effects of DSTR outweigh other factors in driving the SOS and EOS on the Mongolian Plateau phenology.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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