The ongoing deforestation process in Amazonia has led to intensified forest fires in the region, particularly in Brazil, after more than a decade of effective forest conservation policy. This study aims to investigate the recovery of two mature sub‐montane ombrophile Amazonian forests affected by fire in terms of energy, water and carbon fluxes utilizing remote sensing (MODIS) and climate reanalysis data (GLDAS). These two forest plots, mainly composed of Manilkara spp. (Maçaranduba), Protium spp. (Breu) (∼30 m), Bertholletia excelsa (Castanheira) and Dinizia excelsa Ducke (Angelim‐Pedra) (∼50 m), occupy areas of 100.5 and 122.1 km2 and were subject to fire on the same day, on September 12, 2010. The fire significantly increased land surface temperature (0.8°C) and air temperature (1.2°C) in the forests over a 3 years interval. However, the forests showed an ability to recover their original states in terms of coupling between the carbon and water cycles comparing the 3‐year periods before and after the fires. Results from a wavelet analysis showed an intensification in annual and seasonal fluctuations, and in some cases (e.g., daily net radiation and evapotrasnspiration) sub‐annual fluctuation. We interpreted these changes to be consistent with overall intensification of the coupling of energy balance components and drivers imposed by climate and solar cycle seasonality, as well as faster time scale changes, consistent with a shift toward greater forest openness and consequent reduction in the interception of incoming solar radiation by the canopy.

In terrestrial ecosystems, leaves are aggregated into different spatial structures and their spatial distribution is non-random. Clumping index (CI) is a key canopy structural parameter, characterizing the extent to which leaf deviates from the random distribution. To assess leaf clumping effects on global terrestrial ET, we used a global leaf area index (LAI) map and the latest version of global CI product derived from MODIS BRDF data as well as the Boreal Ecosystem Productivity Simulator (BEPS) to estimate global terrestrial ET. The results show that global terrestrial ET in 2015 was 511.9 ± 70.1 mm yr−1 for Case I, where the true LAI and CI are used. Compared to this baseline case, (1) global terrestrial ET is overestimated by 4.7% for Case II where true LAI is used ignoring clumping; (2) global terrestrial ET is underestimated by 13.0% for Case III where effective LAI is used ignoring clumping. Among all plant functional types (PFTs), evergreen needleleaf forests were most affected by foliage clumping for ET estimation in Case II, because they are most clumped with the lowest CI. Deciduous broadleaf forests are affected by leaf clumping most in Case III because they have both high LAI and low CI compared to other PFTs. The leaf clumping effects on ET estimation in both Case II and Case III is robust to the errors in major input parameters. Thus, it is necessary to consider clumping effects in the simulation of global terrestrial ET, which has considerable implications for global water cycle research.

• We present four methods to calculate LAI on a daily basis from PAR. • Each method shows high linear correlation to MODIS and LAI-2000 datasets. • All methods provide a precise indication of start and end of the growing season. • PAR based LAI has broad potential to reveal phenological response to global change. Leaf area index (LAI) is a critical biophysical indicator that describes foliage abundance in ecosystems. An accurate and continuous estimation of LAI is therefore desirable to quantify ecosystem status and function (e.g. carbon and water exchange between the land surface and the atmosphere). However, deriving accurate LAI measurements at regular temporal intervals remains challenging, requiring either destructive sampling or manual collection of canopy gap fraction measurements at discrete time intervals. In this study, we present four methods to obtain continuous LAI data, simply derived from above and below canopy measurements of photosynthetically active radiation (PAR) at the Borden Forest Research Station from 1999 to 2018. We compared LAI derived using the four PAR-based methods to independent measurements of LAI from optical methods and the MODIS satellite LAI product. LAI derived from all four PAR-based methods captured the seasonal changes in observed and remotely sensed LAI and showed a close linear correspondence with one another (R 2 of 0.55 to 0.76 compared to MODIS LAI, and R 2 of 0.78 to 0.84 compared to LAI-2000 measurements). A PAR-based method using Miller's Integral theorem showed the strongest linear relationship with LAI-2000 measurements (R 2 =0.84, p<0.001, SE=0.40). In many years MODIS LAI indicated an earlier start of season and earlier end of season than the daily PAR-based LAI datasets showing systematic biases in the MODIS assessment of growing season. The four PAR-based LAI methods outlined in this study provide an LAI dataset of unprecedented temporal resolution. These methods will allow precise determination of phenological events, improve leaf to canopy scaling in process-based models, and provide valuable insight into dynamic vegetation responses to global climate change.

The successful Soil Moisture Active Passive (SMAP) mission provides operational soil moisture products of high quality; yet its impacts on global carbon and water cycle estimation are yet to be further investigated. Here we assimilated the SMAP enhanced Level-2 soil moisture product at 9 km resolution into a land surface scheme in order to study the soil moisture control on the functioning of terrestrial ecosystems. We found that SMAP significantly improves soil moisture simulations, especially in the spring. Extensive wetting signals were revealed over croplands in arid and semi-arid regions and could not be explained using reanalysis meteorological data, indicating an additional water input, for example, irrigation. Stronger impacts on gross primary production and evapotranspiration simulations are found in wetting adjustments than in drying adjustments after data assimilation. This study suggests that the performance of the land surface scheme benefits greatly from assimilating the SMAP soil moisture product.

We evaluate the potential of using a process-based ecosystem model (BEPS) for crop biomass mapping at 20 m resolution over the research site in Manitoba, western Canada driven by spatially explicit leaf area index (LAI) retrieved from Sentinel-2 spectral reflectance throughout the entire growing season. We find that overall, the BEPS-simulated crop gross primary production (GPP), net primary production (NPP), and LAI time-series can explain 82%, 83%, and 85%, respectively, of the variation in the above-ground biomass (AGB) for six selected annual crops, while an application of individual crop LAI explains only 50% of the variation in AGB. The linear relationships between the AGB and these three indicators (GPP, NPP and LAI time-series) are rather high for the six crops, while the slopes of the regression models vary for individual crop type, indicating the need for calibration of key photosynthetic parameters and carbon allocation coefficients. This study demonstrates that accumulated GPP and NPP derived from an ecosystem model, driven by Sentinel-2 LAI data and abiotic data, can be effectively used for crop AGB mapping; the temporal information from LAI is also effective in AGB mapping for some crop types.

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.

Remote sensing is a key method for advancing our understanding of global photosynthesis and is thus critical to understanding terrestrial carbon uptake and climate change. Increasingly sophisticated spectral indices including solar-induced florescence (SIF) and the photochemical reflectance index (PRI) are considered good proxies of canopy structure, biochemistry, and physiology. However, the relative influences of illumination intensity and angle on these measures are difficult to unravel, particularly at the scale of whole forest canopies. We exploit the solar dimming during the 2017 North American solar eclipse as well as a clear day before and cloudy day after the day of the eclipse. This novel approach allows us to assess changes in spectral vegetation indices due to illumination intensity independent of changes in illumination angle. Physiologically relevant spectral indices were most affected by dimming, with illumination level explaining 97% of variability in SIF and 99% of variability in PRI during the eclipse. The spectral change in reflectance through the eclipse period revealed changes in PRI were driven by reflectance differences at the 570 nm reference band rather than at the 531 nm signal band associated with xanthophyll pigment interconversions. This study refines our interpretation of vegetation properties from space with implications for our interpretation of signals related to terrestrial photosynthesis derived from sensors spanning a range of illumination conditions and angles.

[Extract] The Brazilian Amazon—the largest tropical rainforest in the world—has reached its highest level of deforestation since 2008 (Display footnote number:1). In 2019, 10,897 km2 of land were deforested, a 50.7% jump over the previous year (Display footnote number:1). A combination of threats, including tens of thousands of forest fires (Display footnote number:2), expanding road networks (Display footnote number:3, 4), weakened environmental laws (Display footnote number:5, 6), and a failure to enforce environmental laws and regulations (Display footnote number:6), is responsible. Given the staunchly pro-development policies of Brazil’s current government, a coalition of key actors in the financial sector is needed to help protect the embattled Amazon rainforest.

The conversion from primary forest to agriculture drives widespread changes that have the potential to modify the hydroclimatology of the Xingu River Basin. Moreover, climate impacts over eastern Amazonia have been strongly related to pasture and soybean expansion. This study carries out a remote-sensing, spatial-temporal approach to analyze inter- and intra-annual patterns in evapotranspiration (ET) and precipitation (PPT) over pasture and soybean areas in the Xingu River Basin during a 13-year period. We used ET estimates from the Moderate Resolution Imaging Spectroradiometer (MODIS) and PPT estimates from the Tropical Rainfall Measurement Mission (TRMM) satellite. Our results showed that the annual average ET in the pasture was ~20% lower than the annual average in soybean areas. We show that PPT is notably higher in the northern part of the Xingu River Basin than the drier southern part. ET, on the other hand, appears to be strongly linked to land-use and land-cover (LULC) patterns in the Xingu River Basin. Lower annual ET averages occur in southern areas where dominant LULC is savanna, pasture, and soybean, while more intense ET is observed over primary forests (northern portion of the basin). The primary finding of our study is related to the fact that the seasonality patterns of ET can be strongly linked to LULC in the Xingu River Basin. Further studies should focus on the relationship between ET, gross primary productivity, and water-use efficiency in order to better understand the coupling between water and carbon cycling over this expanding Amazonian agricultural frontier.

Deforestation in the Brazilian Amazon is related to the use of fire to remove natural vegetation and install crop cultures or pastures. In this study, we evaluated the relation between deforestation, land-use and land-cover (LULC) drivers and fire emissions in the Apyterewa Indigenous Land, Eastern Brazilian Amazon. In addition to the official Brazilian deforestation data, we used a geographic object-based image analysis (GEOBIA) approach to perform the LULC mapping in the Apyterewa Indigenous Land, and the Brazilian biomass burning emission model with fire radiative power (3BEM_FRP) to estimate emitted particulate matter with a diameter less than 2.5 µm (PM2.5), a primary human health risk. The GEOBIA approach showed a remarkable advancement of deforestation, agreeing with the official deforestation data, and, consequently, the conversion of primary forests to agriculture within the Apyterewa Indigenous Land in the past three years (200 km2), which is clearly associated with an increase in the PM2.5 emissions from fire. Between 2004 and 2016 the annual average emission of PM2.5 was estimated to be 3594 ton year−1, while the most recent interval of 2017–2019 had an average of 6258 ton year−1. This represented an increase of 58% in the annual average of PM2.5 associated with fires for the study period, contributing to respiratory health risks and the air quality crisis in Brazil in late 2019. These results expose an ongoing critical situation of intensifying forest degradation and potential forest collapse, including those due to a savannization forest-climate feedback, within “protected areas” in the Brazilian Amazon. To reverse this scenario, the implementation of sustainable agricultural practices and development of conservation policies to promote forest regrowth in degraded preserves are essential.

Author(s): Luo, X; Chen, JM; Liu, J; Black, TA; Croft, H; Staebler, R; He, L; Arain, MA; Chen, B; Mo, G; Gonsamo, A; McCaughey, H | Abstract: Evapotranspiration (ET) is commonly estimated using the Penman-Monteith equation, which assumes that the plant canopy is a big leaf (BL) and the water flux from vegetation is regulated by canopy stomatal conductance (Gs). However, BL has been found to be unsuitable for terrestrial biosphere models built on the carbon-water coupling principle because it fails to capture daily variations of gross primary productivity (GPP). A two-big-leaf scheme (TBL) and a two-leaf scheme (TL) that stratify a canopy into sunlit and shaded leaves have been developed to address this issue. However, there is a lack of comparison of these upscaling schemes for ET estimation, especially on the difference between TBL and TL. We find that TL shows strong performance (r2n=n0.71, root-mean-square errorn=n0.05nmm/h) in estimating ET at nine eddy covariance towers in Canada. BL simulates lower annual ET and GPP than TL and TBL. The biases of estimated ET and GPP increase with leaf area index (LAI) in BL and TBL, and the biases of TL show no trends with LAI. BL miscalculates the portions of light-saturated and light-unsaturated leaves in the canopy, incurring negative biases in its flux estimation. TBL and TL showed improved yet different GPP and ET estimations. This difference is attributed to the lower Gs and intercellular CO2 concentration simulated in TBL compared to their counterparts in TL. We suggest to use TL for ET modeling to avoid the uncertainty propagated from the artificial upscaling of leaf-level processes to the canopy scale in BL and TBL.