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The Gross Primary Productivity (GPP) is an important component in regional and global carbon budgets. Southeastern China has experienced vegetation greening and climate change. Yet, it remains unclear how these changes have impacted GPP in this region. As one of six special national parks in China with complex topography, high biodiversity, and little destructive human activities to the ecosystems, the Wuyi Mountain region is selected to study these impacts. In this study, we use a hydroecological model (BEPS-TerrainLab V2.0) to simulate the spatial and temporal variations of GPP in the Wuyi Mountain region over 2001–2018. We quantitatively separate the effects of vegetation greening and climate change on the trend and interannual variation in GPP through sensitivity experiments. The results show a significant increasing trend in Leaf Area Index (LAI) in the region over 2001–2018 (0.06 m2 m−2 yr−1, p

Light Detection and Ranging (LiDAR), with its ability to capture vegetation vertical profile, could be a unique technique for deriving Leaf Area Index (LAI). A global LAI product at 25-m spatial resolution was derived from the Global Ecosystem Dynamics Investigation (GEDI) LiDAR data since 2019, but it was often significantly underestimated in dense forests. Here we explored the potential for improving the estimation of the underestimated GEDI LAI in dense forests by using the Digital Elevation Model (DEM) as auxiliary data to separate ground and canopy returns in the received waveform. Dense forests were defined as forests with high vegetation greenness (annual maximum NDVI ≥ 0.8). The newly estimated GEDI footprint LAI was first validated with the ground-measured LAI at two sites in Fujian, China, and the results showed that the underestimation was significantly reduced compared to the original GEDI LAI product (r increased from −0.55 to 0.81, RMSE decreased from 3.94 to 1.43, Bias decreased from 3.17 to 0.48). To evaluate whether the improvement was applicable to other areas and forest types, the newly estimated GEDI footprint LAI for the entire Fujian and Contiguous US (CONUS) was then compared to the consistent LAI among three widely used global LAI products. The comparison results demonstrated that the use of DEM as auxiliary data could largely reduce the underestimation of GEDI footprint LAI (In Fujian, RMSE decreased from 4.75 to 2.52, and Bias decreased from 4.61 to 0.58; in CONUS, RMSE decreased from 5.24 to 1.96, and Bias decreased from 5.1 to 0.73). Overall, this study demonstrates the effectiveness of correcting the large underestimation of GEDI footprint LAI in dense forests by utilizing DEM, which has an important influence on the results, as auxiliary data.

Abstract Cotton maps (10 m) of Xinjiang (XJ_COTTON10), which is the largest cotton production region of China, were produced from 2018 to 2021 through supervised classification. A two-step mapping strategy, i.e., cropland mapping followed by cotton extraction, was employed to improve the accuracy and efficiency of cotton mapping for a large region of about 1.66 million km2 with high heterogeneity. Additionally, the time-series satellite data related to spectral, textural, structural, and phenological features were combined and used in a supervised random forest classifier. The cotton/non-cotton classification model achieved overall accuracies of about 95% and 90% on the test samples of the same and adjacent years, respectively. The proposed two-step cotton mapping strategy proved promising and effective in producing multi-year and consistent cotton maps. XJ_COTTON10 agreed well with the statistical areas of cotton at the county level (R2 = 0.84–0.94). This is the first cotton mapping for the entire Xinjiang at 10-meter resolution, which can provide a basis for high-precision cotton monitoring and policymaking in China.

Individual inversions of Leaf Area Index (LAI) and Leaf Chlorophyll Content (LCC) have problems due to the mutual interference between these two vegetation parameters on remote sensing signals. We therefore explore synergetic inversion of these two parameters to improve their inversion accuracy. We selected subtropical forest plantations, where canopy reflectance data were collected using a DJI Phantom 4 Multispectral Unmanned Aerial Vehicle (UAV) every month during 2021–2022. Monthly in-situ observations of LAI and Clumping Index (CI) were also made in 23 broadleaf tree plots of dimension 12 m × 12 m. Vegetation Indices (VI) were calculated with the mean reflectance of all pixels at 0.06 m resolution within each sampling plot, and only those VIs with highest sensitivities to LAI or LCC were selected and correlated to LAI and LCC. An empirical model in the form of VI = f(LAI, LCC) was constructed for synergetic inversion of LAI and LCC. For the purpose of comparison, two models VI = f(LAI) and VI = f(LCC) were also constructed and used for the inversions of LAI and LCC, separately. The synergetic inversion model yields R2 = 0.60 and RMSE = 2.80 cm2/cm2 for LAI and R2 = 0.45 and RMSE = 32.71 μg/cm2 for LCC, whereas the separate inversion models result in R2 = 0.59 and RMSE = 2.82 cm2/cm2 for LAI and R2 = 0.35 and RMSE = 35.86 μg/cm2 for LCC. Moreover, we found that the inclusion of VIs containing a red edge band in the synergetic inversion can effectively improve the inversion accuracy. The proposed synergetic inversion method based on multiple VIs would be an effective way to separate the mutual interference between LAI and LCC and improve the accuracy of LCC inversion from remote sensing data.

Forests are chiefly responsible for the terrestrial carbon sink that greatly reduces the buildup of CO2 concentrations in the atmosphere and alleviates climate change. Current predictions of terrestrial carbon sinks in the future have so far ignored the variation of forest carbon uptake with forest age. Here, we predict the role of China’s current forest age in future carbon sink capacity by generating a high-resolution (30 m) forest age map in 2019 over China’s landmass using satellite and forest inventory data and deriving forest growth curves using measurements of forest biomass and age in 3,121 plots. As China’s forests currently have large proportions of young and middle-age stands, we project that China’s forests will maintain high growth rates for about 15 years. However, as the forests grow older, their net primary productivity will decline by 5.0% ± 1.4% in 2050, 8.4% ± 1.6% in 2060, and 16.6% ± 2.8% in 2100, indicating weakened carbon sinks in the near future. The weakening of forest carbon sinks can be potentially mitigated by optimizing forest age structure through selective logging and implementing new or improved afforestation. This finding is important not only for the global carbon cycle and climate projections but also for developing forest management strategies to enhance land sinks by alleviating the age effect.

Abstract Satellite XCO2 retrievals could improve the estimates of surface carbon fluxes, but it remains unknown on what scales these estimates are robust. Here, we use the time‐dependent Bayesian synthesis top‐down method and prior net ecosystem exchanges (NEEs) from 12 terrestrial biosphere models (TBMs) to infer the monthly carbon fluxes of 51 land regions with constraints by GOSAT XCO2 retrievals. We find that the uncertainty (standard deviation of 12 TBMs) reduction rates (uncertainty reduction rate (URR)) decrease significantly at decreasing spatial scales. On the continental‐scale, the mean URR is about 57%, and the annual and seasonal cycle estimates of NEE are rather robust. The evaluation shows that the posterior CO2 concentrations are significantly improved at the continental scale. Our study suggests that the GOSAT XCO2 can only promise a robust continental‐scale NEE estimate, and improving the XCO2 accuracy is an effective way to achieve robust estimates on smaller scales under current spatial coverage.

China, the Unite States (US), the European Union (EU), India, and Russia are the world's top 5 fossil fuel and cement CO2 (FFC) emitting countries or regions (CRs). It is very important to understand their status of carbon neutrality, and to monitor their future changes of net carbon fluxes (NCFs). In this study, we implemented a well-established global carbon assimilation system (GCAS, Version 2) to infer global surface carbon fluxes from May 2009 to December 2019 using both GOSAT and OCO-2 XCO2 retrievals. The reductions of flux uncertainty and XCO2 bias, and the evaluation of posterior flux show that GCAS has comparable and good performance in the 5 CRs. The results suggest that Russia has achieved carbon neutrality, but the other 4 are still far from being carbon neutral, especially China. The mean annual NCFs in China, the US, the EU, India, and Russia are 2.33 ± 0.29, 0.82 ± 0.20, 0.42 ± 0.16, 0.50 ± 0.12, and -0.33 ± 0.23 PgC yr−1, respectively. From 2010 to 2019, the NCFs showed an increasing trend in the US and India, a slight downward trend after 2013 in China, and were stable in the EU. The changes of land sinks in China and the US might be the main reason for their trends. India's trend was mainly due to the increase of FFC emission. The relative contributions of NCFs to the global land net carbon emission of China and the EU have decreased, while those of the US and India have increased, implying the US and India must take more active measures to control carbon emissions or increase their sinks. This study indicates that satellite XCO2 could be successfully used to monitor the changes of regional NCFs, which is of great significance for major countries to achieve greenhouse gas control goals.

The influence of tree distribution and slope on canopy gap fraction (GF) and bidirectional reflectance factor (BRF) is shown here to be non-negligible. Trees are often assumed to be randomly distributed in natural forests due to random distribution of natural resources, but this assumption is not valid for forest plantations. A geometric optical model for forest plantations (GOFP) is a geometric optical model for forest plantations on horizontal surfaces based on the theory of exclusion distance among crowns. Sloping terrains change the exclusion distance among crowns, and inevitably affect the canopy GF and BRF. In this article, GOFP with a hypergeometric model (distances among trees are considered) on horizontal surfaces is modified as GOFP-T to simulate BRF for forest plantations on sloping terrains under two scenarios: (horizontal distances among crowns remain unchanged with slope) and (sloping distances among crowns remain unchanged with slope). Two three-dimensional (3-D) radiative transfer models (DART and LESS) and field measurements are used to evaluate and validate GOFP-T simulations. The results show that 1) the canopy GF, four component area ratios, and canopy BRF simulated by GOFP-T show high consistency with results from the two 3-D model: root-mean-square errors in GF, sunlit foliage, and sunlit ground are less than 0.02, 0.06, and 0.03, respectively; 2) forest coverage and canopy reflectance in GOFP-T are compared well with point cloud results from an airboard LiDAR system and Landsat 8 OLI surface reflectance products, respectively, indicating that GOFP-T has ability in simulating canopy reflectance for forest plantations on sloping terrains. GOFP-T with the hypergeometric model in this article is the first simple model to simulate canopy GF and BRF for forest plantations on sloping terrains.

Over the past 2 to 3 decades, Chinese forests are estimated to act as a large carbon sink, yet the magnitude and spatial patterns of this sink differ considerably among studies. Using 3 microwave (L- and X-band vegetation optical depth [VOD]) and 3 optical (normalized difference vegetation index, leaf area index, and tree cover) remote-sensing vegetation products, this study compared the estimated live woody aboveground biomass carbon (AGC) dynamics over China between 2013 and 2019. Our results showed that tree cover has the highest spatial consistency with 3 published AGC maps (mean correlation value R = 0.84), followed by L-VOD (R = 0.83), which outperform the other VODs. An AGC estimation model was proposed to combine all indices to estimate the annual AGC dynamics in China during 2013 to 2019. The performance of the AGC estimation model was good (root mean square error = 0.05 Pg C and R2 = 0.90 with a mean relative uncertainty of 9.8% at pixel scale [0.25°]). Results of the AGC estimation model showed that carbon uptake by the forests in China was about +0.17 Pg C year−1 from 2013 to 2019. At the regional level, provinces in southwest China including Guizhou (+22.35 Tg C year−1), Sichuan (+14.49 Tg C year−1), and Hunan (+11.42 Tg C year−1) provinces had the highest carbon sink rates during 2013 to 2019. Most of the carbon-sink regions have been afforested recently, implying that afforestation and ecological engineering projects have been effective means for carbon sequestration in these regions.

Land use decisions directly affect the terrestrial carbon balance by changing the quantity and type of vegetation present. However, such consequences are difficult for decisionmakers to account for in environmental impact assessments. Solar induced chlorophyll fluorescence (SIF) measured from satellite is strongly related to gross primary productivity of terrestrial vegetation and presents a new source of information from which to assess environmental impacts of land use decisions. We assess the usefulness of SIF measurements in accounting for changes in vegetation primary productivity due to land use change. We use a weighted double logistic regression based on the land cover mixture within SIF measurements from the TROPOspheric Monitoring Instrument (TROPOMI) to separate the phenology of the SIF signal by land cover for the region of Ontario, Canada. We use SIF integrated annually to determine, in relative units, the consequences of land use change on photosynthetic carbon uptake for the study region. Finally, we map GPP at a spatial resolution of 30 m across the province of Ontario using previously reported relationships between SIF and GPP. We find that SIF tracks expected biogeographical patterns of productivity: urban areas exhibit an earlier start of the growing season, lower SIF magnitude and later end of season than natural land cover classes, whereas croplands exhibit a later start of season. Patterns of phenology and SIF magnitude show land cover transitions from Broadleaf Deciduous Forests to croplands or urban environments to have the highest impacts on carbon uptake. Satellite-based SIF measurements are useful in exploring the geographic variation of vegetation productivity and can support inclusion of carbon accounting in environmental assessment at regional scale.

Forest biomass dynamics are important indicators of forest productivity and carbon sinks, which are useful for evaluating forest ecological benefits and management options. Rapid and accurate methods for monitoring forest biomass would serve this purpose well. This study aimed at measuring aboveground biomass (AGB) and stand growth from tree crown parameters derived using unmanned aerial vehicle–light detection and ranging (UAV–LiDAR). We focused on 17-year-old Chinese fir plantations in a subtropical area in China and monitored them using UAV–LiDAR from February 2019 to February 2020. Two effective crown height (ECH) detection methods based on drone discrete point clouds were evaluated using ground survey data. Based on the evaluation results, the voxel method based on point cloud segmentation (root-mean-squared error (RMSE) = 0.62 m, relative RMSE (rRMSE) = 4.26%) was better than the tree crown boundary pixel sum method based on canopy height segmentation (RMSE = 1.26 m, rRMSE = 8.63%). The effective crown area (ECA) of an individual tree extracted using ECH was strongly correlated with the annual biomass growth (coefficient of determination (R2) = 0.47). The estimation of annual growth of individual tree crowns based on annual tree height increase (ΔH) derived from LiDAR was statistically significant (R2 = 0.33, p < 0.01). After adding the crown projection area or ECA, the model accuracy R2 increased to 0.57 or 0.63, respectively. As the scale increased to the plot level, the direct model with ECA (RMSE = 1.59 Mg∙ha−1∙a−1, rRMSE = 15.02%) had a better performance than the indirect model using tree height and crown diameter (RMSE = 1.81 Mg∙ha−1∙a−1, rRMSE = 17.10%). The mean annual growth rate of AGB per middle-aged Chinese fir tree was determined to be 8.45 kg∙a−1 using ECA and ΔH, and the plot-level growth rate was 11.47 Mg∙ha−1∙a−1. We conclude that the rapid and accurate monitoring of the annual growth of Chinese fir can be achieved based on multitemporal UAV–LiDAR and effective crown information.

The locally adjusted cubic-spline capping (LACC) algorithm is well recognized for its effectiveness in the global time series reconstruction of vegetation biophysical and biochemical parameters. However, in its application, we often encounter issues, such as identifying positively biased outliers for vegetation biochemical parameters and reducing the influence of long consecutive gaps. In this study, we improved the LACC algorithm to address the above two issues by (1) incorporating a procedure to remove outliers and (2) integrating the spatial information of neighboring pixels for large data gap filling. To evaluate the performance of the new version of LACC (namely LACC2.0), leaf chlorophyll content (LCC) was taken as an example. A reference LCC curve was generated for each pixel of the global map as the true value for global evaluation, and a time series of LCC with real gaps in the original data for each pixel was created by adding Gaussian noises into observations for testing the effectiveness of time series reconstruction algorithms. Results showed that the percentage of pixels with an RMSE smaller than 5 μg/cm2 was improved from 81.2% in LACC to 96.4% in LACC2.0, demonstrating that LACC2.0 had the potential to provide a better reconstruction of global daily satellite-derived vegetation biochemical parameters. This finding highlights the significance of outlier removal and spatial-temporal fusion to enhance the accuracy and reliability of time series reconstruction.

The fraction of leaf nitrogen allocated to RuBisCO indicates differing nitrogen use strategies of plants and varies considerably. Here the authors show that this variation is largely driven by leaf thickness and phosphorus content with light intensity, atmospheric dryness and soil pH also having considerable influence.

Abstract Background Forests in the Far North of Ontario (FNO), Canada, are likely the least studied in North America, and quantifying their current and future carbon (C) stocks is the first step in assessing their potential role in climate change mitigation. Although the FNO forests are unmanaged, the latter task is made more important by growing interest in developing the region’s natural resources, primarily for timber harvesting. In this study, we used a combination of field and remotely sensed observations with a land surface model to estimate forest C stocks in the FNO forests and to project their future dynamics. The specific objective was to simulate historical C stocks for 1901–2014 and future C stocks for 2015–2100 for five shared socioeconomic pathway (SSP) scenarios selected as high priority scenarios for the 6th Assessment Report on Climate Change. Results Carbon stocks in live vegetation in the FNO forests remained relatively stable between 1901 and 2014 while soil organic carbon (SOC) stocks steadily declined, losing about 16% of their initial value. At the end of the historical simulation (in 2014), the stocks were estimated at 19.8, 46.4, and 66.2 tCha−1 in live vegetation, SOC, and total ecosystem pools, respectively. Projections for 2015–2100 indicated effectively no substantial change in SOC stocks, while live vegetation C stocks increased, accelerating their growth in the second half of the twenty-first century. These results were consistent among all simulated SSP scenarios. Consequently, increase in total forest ecosystem C stocks by 2100 ranged from 16.7 to 20.7% of their value in 2015. Simulations with and without wildfires showed the strong effect of fire on forest C stock dynamics during 2015–2100: inclusion of wildfires reduced the live vegetation increase by half while increasing the SOC pool due to higher turnover of vegetation C to SOC. Conclusions Forest ecosystem C stock estimates at the end of historical simulation period were at the lower end but within the range of values reported in the literature for northern boreal forests. These estimates may be treated as conservatively low since the area included in the estimates is poorly studied and some of the forests may be on peat deposits rather than mineral soils. Future C stocks were projected to increase in all simulated SSP scenarios, especially in the second half of the twenty-first century. Thus, during the projected period forest ecosystems of the FNO are likely to act as a C sink. In light of growing interest in developing natural resources in the FNO, collecting more data on the status and dynamics of its forests is needed to verify the above-presented estimates and design management activities that would maintain their projected C sink status.

Forest disturbance detection is of great significance for understanding forest dynamics. The Landsat-based detection of the Trends in Disturbance and Recovery (LandTrendr) algorithm is widely used for forest disturbance mapping. However, there are still two limitations in LandTrendr: first, it only used for summer-composited observations, which may delay the detection of forest disturbances that occurred in autumn and winter by one year, and second, it detected all disturbance types simultaneously using a single spectral index, which may reduce the mapping accuracy for certain forest disturbance types. Here, we modified LandTrendr (mLandTrendr) for forest disturbance mapping in China by using multi-season observations and multispectral indices. Validations using the randomly selected 1957 reference forest disturbance samples across China showed that the overall accuracy (F1 score) of forest disturbance detection in China was improved by 21% with these two modifications. The mLandTrendr can quickly and accurately detect forest disturbance and can be extended to national and global forest disturbance mapping for various forest types.

TanSat is China’s first greenhouse gases observing satellite. In recent years, substantial progresses have been achieved on retrieving column-averaged CO2 dry air mole fraction (XCO2). However, relatively few attempts have been made to estimate terrestrial net ecosystem exchange (NEE) using TanSat XCO2 retrievals. In this study, based on the GEOS-Chem 4D-Var data assimilation system, we infer the global NEE from April 2017 to March 2018 using TanSat XCO2. The inversion estimates global NEE at −3.46 PgC yr-1, evidently higher than prior estimate and giving rise to an improved estimate of global atmospheric CO2 growth rate. Regionally, our inversion greatly increases the carbon uptakes in northern mid-to-high latitudes and significantly enhances the carbon releases in tropical and southern lands, especially in Africa and India peninsula. The increase of posterior sinks in northern lands is mainly attributed to the decreased carbon release during the nongrowing season, and the decrease of carbon uptakes in tropical and southern lands basically occurs throughout the year. Evaluations against independent CO2 observations and comparison with previous estimates indicate that although the land sinks in the northern middle latitudes and southern temperate regions are improved to a certain extent, they are obviously overestimated in northern high latitudes and underestimated in tropical lands (mainly northern Africa), respectively. These results suggest that TanSat XCO2 retrievals may have systematic negative biases in northern high latitudes and large positive biases over northern Africa, and further efforts are required to remove bias in these regions for better estimates of global and regional NEE.

Global development has been heavily reliant on the overexploitation of natural resources since the Industrial Revolution. With the extensive use of fossil fuels, deforestation, and other forms of land-use change, anthropogenic activities have contributed to the ever-increasing concentrations of greenhouse gases (GHGs) in the atmosphere, causing global climate change. In response to the worsening global climate change, achieving carbon neutrality by 2050 is the most pressing task on the planet. To this end, it is of utmost importance and a significant challenge to reform the current production systems to reduce GHG emissions and promote the capture of CO2 from the atmosphere. Herein, we review innovative technologies that offer solutions achieving carbon (C) neutrality and sustainable development, including those for renewable energy production, food system transformation, waste valorization, C sink conservation, and C-negative manufacturing. The wealth of knowledge disseminated in this review could inspire the global community and drive the further development of innovative technologies to mitigate climate change and sustainably support human activities.

There lacks systematic analysis on the importance of vegetation structural change in the global terrestrial carbon cycle. Here the authors conducted a multi-model comparison analysis and find that the increase in leaf area index has been responsible for 12.4% of the accumulated terrestrial carbon sink from 1981 to 2016.

Abstract Indian Ocean Dipole (IOD), a major climate variability in the tropics which drives the abiotic stress associated with heavy rainfalls and severe droughts, is not much understood in terms of its role in the carbon cycle, while El Niño‐Southern Oscillation (ENSO)‐related terrestrial carbon cycle variation has been intensively studied. Here, we investigate IOD's impact on land photosynthesis over the Indian ocean rim countries during austral spring using satellite‐based gross primary productivity (GPP) and Earth System Model simulations produced in the Coupled Model Intercomparison Project Phase 6 (CMIP6). IOD independently affects GPP with significant positive partial correlation coefficients (pcor) over most of Africa and India, and negative pcor over southern China, Indo‐China peninsula, maritime continent, and Australia, mostly driven by precipitation variations; this obviously differs from the widespread significant negative pcor pattern induced by ENSO. The recent extremely positive IOD in 2019 caused the canonical IOD‐affected GPP patterns, however, with its extreme impacts. Furthermore, though large inter‐model spreads exist, the CMIP6 multimodel median can basically capture the main characteristics of IOD‐affected precipitation and GPP patterns. Importantly, IOD is predicted to occur more frequently in future warming scenarios. Model future projections suggest that it will exert larger impacts on GPP variations over central and eastern Africa, Sumatra, western and southeastern Australia with stronger pcor and enhanced explained variance, but less impacts over southern Africa, east India, Indo‐China peninsula, and northeastern Australia. Therefore, besides ENSO, understanding the IOD impacts can provide us new insights into regional and global carbon cycle interannual variability.

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.

This paper proposes a systematic image mosaicking methodology to produce hyperspectral image for environment monitoring using an emerging UAV-based push-broom hyperspectral imager. The suitability of alternative methods in each step is assessed by experiments of an urban scape, a river course and a forest study area. First, the hyperspectral image strips were acquired by sequentially stitching the UAV images acquired by push-broom scanning along each flight line. Next, direct geo-referencing was applied to each image strip to get initial geo-rectified result. Then, with ground control points, the curved surface spline function was used to transform the initial geo-rectified image strips to improve their geometrical accuracy. To further remove the displacement between pairs of image strips, an improved phase correlation (IPC) and a SIFT and RANSAC-based method (SR) were used in image registration. Finally, the weighted average and the best stitching image fusion method were used to remove the spectral differences between image strips and get the seamless mosaic. Experiment results showed that as the GCPs‘ number increases, the mosaicked image‘s geometrical accuracy increases. In image registration, there exists obvious edge information that can be accurately extracted from the urban scape and river course area; comparative results can be achieved by the IPC method with less time cost. However, for the ground objects with complex texture like forest, the edges extracted from the image is prone to be inaccurate and result in the failure of the IPC method, and only the SR method can get a good result. In image fusion, the best stitching fusion method can get seamless results for all three study areas. Whereas, the weighted average fusion method was only useful in eliminating the stitching line for the river course and forest areas but failed for the urban scape area due to the spectral heterogeneity of different ground objects. For different environment monitoring applications, the proposed methodology provides a practical solution to seamlessly mosaic UAV-based push-broom hyperspectral images with high geometrical accuracy and spectral fidelity.

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 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.

Light use efficiency (LUE) models are widely used to estimate gross primary productivity (GPP), a dominant component of the terrestrial carbon cycle. Their outputs are very sensitive to LUE. Proper determination of this parameter is a prerequisite for LUE models to simulate GPP at regional and global scales. This study was devoted to investigating the ability of the photochemical reflectance index (PRI) to track LUE variations for a sub-tropical planted coniferous forest in southern China using tower-based PRI and GPP measurements over the period from day 101 to 275 in 2013. Both half-hourly PRI and LUE exhibited detectable diurnal and seasonal variations, and decreased with increases of vapor pressure deficit (VPD), air temperature (Ta), and photosynthetically active radiation (PAR). Generally, PRI is able to capture diurnal and seasonal changes in LUE. However, correlations of PRI with LUE varied dramatically throughout the growing season. The correlation was the strongest (R2 = 0.6427, p < 0.001) in July and the poorest in May. Over the entire growing season, PRI relates better to LUE under clear or partially cloudy skies (clearness index, CI > 0.3) with moderate to high VPD (>20 hPa) and high temperatures (>31 C). Overall, we found that PRI is most sensitive to variations in LUE under stressed conditions, and the sensitivity decreases as the growing conditions become favorable when atmosphere water vapor, temperature and soil moisture are near the optimum conditions.

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.

Leaf area index (LAI) is a key surface parameter that describes the structure of vegetation and plays an important role in Earth system process modeling. In this paper, a new set of LAI products (MERSI GLOBCARBON LAI) has been developed based on the GLOBCARBON LAI algorithm and one year of FY-3A/MERSI land surface reflectance data. MERSI GLOBCARBON LAI has been inter-compared and validated over mainland China against MODIS land surface reflectance (LSR) derived LAI (using the same algorithm) and field LAI measurements. MERSI GLOBCARBON LAI and MODIS GLOBCARBON LAI show continuous and smooth LAI distributions at the start and end of the growing season. For most areas in China, the two LAI products agree well. The temporal variation in MERSI GLOBCARBON LAI and MODIS GLOBCARBON LAI consistently follows the growing season. The largest LAI difference occurs during July, when MERSI shows a much higher frequency of retrievals than does MODIS. Through validation of LAI retrievals with field measurements, our study demonstrates that LAI derived from MERSI and MODIS land surface reflectance products have comparable accuracy. MODIS top-of-atmosphere simple ratio (MODIS TOA SR) is related to MERSI TOA SR with linear correlation coefficients greater than 0.6. After atmospheric correction, the correlation coefficient increases from 0.69 to 0.75 over cropland and from 0.82 to 0.93 over grassland. However, atmospheric correction can still give rise to substantial differences in the reflectance data between the two sensors. Furthermore, different land cover types and different terrain relief have contrasting influences on the atmospheric correction, and these influences reduce the agreement between the two LAI products. This study shows the great potential of FY-3A/MERSI data for global LAI retrieval.

Accurate measurement of leaf chlorophyll concentration (LChl) in the field using a portable chlorophyll meter (PCM) is crucial to support methodology development for mapping the spatiotemporal variability of crop nitrogen status using remote sensing. Several PCMs have been developed to measure LChl instantaneously and non-destructively in the field, however, their readings are relative quantities that need to be converted into actual LChl values using conversion functions. The aim of this study was to investigate the relationship between actual LChl and PCM readings obtained by three PCMs: SPAD-502, CCM-200, and Dualex-4. Field experiments were conducted in 2016 on four crops: corn (Zea mays L.), soybean (Glycine max L. Merr.), spring wheat (Triticum aestivum L.), and canola (Brassica napus L.), at the Central Experimental Farm of Agriculture and Agri-Food Canada in Ottawa, Ontario, Canada. To evaluate the impact of other factors (leaf internal structure, leaf pigments other than chlorophyll, and the heterogeneity of LChl distribution) on the conversion function, a global sensitivity analysis was conducted using the PROSPECT-D model to simulate PCM readings under different conditions. Results showed that Dualex-4 had a better performance for actual LChl measurement than SPAD-502 and CCM-200, using a general conversion function for all four crops tested. For SPAD-502 and CCM-200, the error in the readings increases with increasing LChl. The sensitivity analysis reveals that deviations from the calibration functions are more induced by non-uniform LChl distribution than leaf architectures. The readings of Dualex-4 can have a better ability to restrict these influences than those of the other two PCMs.

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.

Leaf chlorophyll content plays a vital role in plant photosynthesis. The PROSPECT model has been widely used for retrieving leaf chlorophyll content from remote sensing data over various plant species. However, despite wide variations in leaf surface reflectance across different plant species and environmental conditions, leaf surface reflectance is assumed to be the same for different leaves in the PROSPECT model. This work extends the PROSPECT model by taking into account the variation of leaf surface reflection. In the modified model named PROSPECT-Rsurf, an additional surface layer with a variable refractive index is bounded on the N elementary layers. Leaf surface reflectance (Rs) is characterized by the difference between the refractive indices of leaf surface and interior layers. The specific absorption coefficients of the leaf total chlorophyll and carotenoids were recalibrated using a cross-calibration method and the refractive indices of leaf surface and interior layers were obtained during model inversion. Chlorophyll content (Cab) retrieval and spectral reconstruction in the visible spectral region (VIS, 400−750 nm) were greatly improved using PROSPECT-Rsurf, especially for leaves covered by heavy wax or hard cuticles that lead to high surface reflectance. The root mean square error (RMSE) of chlorophyll estimates decreased from 11.1 µg/cm2 to 8.9 µg/cm2 and the Pearson’s correlation coefficient (r) increased from 0.81 to 0.88 (p < 0.01) for broadleaf samples in validation, compared to PROSPECT-5. For needle leaves, r increased from 0.71 to 0.89 (p < 0.01), but systematic overestimation of Cab was found due to the edge effects of needles. After incorporating the edge effects in PROSPECT-Rsurf, the overestimation of Cab was alleviated and its estimation was improved for needle leaves. This study explores the influence of leaf surface reflectance on Cab estimation at the leaf level. By coupling PROSPECT-Rsurf with canopy models, the influence of leaf surface reflectance on canopy reflectance and therefore canopy chlorophyll content retrieval can be investigated across different spatial and temporal scales.

Information on crop seeding date is required in many applications such as crop management and yield forecasting. This study presents a novel method to estimate crop seeding date at the field level from time-series 250-m Moderate Resolution Imaging Spectroradiometer (MODIS) data and growing degree days (GDD; base 5 ºC; ºC-days). The start of growing season (SOS) was first derived from time-series EVI2 (two-band Enhanced Vegetation Index) calculated from a MODIS 8-day composite surface reflectance product (MOD09Q1; Collection 6). Based on GDD calculated from the Daymet gridded estimates of daily weather parameters, a simple model was developed to establish a linkage between the observed seeding date and the SOS. Calibration and validation of the model was conducted on three major crops, spring wheat, canola and oats in the Province of Manitoba, Canada. The estimated SOS had a strong linear correlation with the observed seeding date; with a deviation of a few days depending on the year. The seeding date of the three crops can be calculated from the SOS by adjusting the number of days needed to accumulate GDD (AGDD) for emergence. The overall root-mean-square-difference (RMSD) of the estimated seeding date was less than 10 days. Validation showed that the accuracy of the estimated seeding date was crop-type independent. The developed method is useful for estimating the historical crop seeding date from remote sensing data in Canada to support studies of the interactions among seeding date, crop management and crop yield under climate change. It is anticipated that this method can be adapted to other crops in other locations using the same or different satellite data.

In this study, we evaluated the effectiveness of photochemical reflectance index (PRI) and non-photochemical quenching (NPQ) for assessing water stress in maize for the purpose of developing remote sensing techniques for monitoring water deficits in crops. Leaf-level chlorophyll fluorescence and canopy-level PRI were measured concurrently over a maize field with five different irrigation treatments, ranging from 20% to 90% of the field capacity (FC). Significant correlations were found between leaf-level NPQ (NPQleaf) and the ratio of chlorophyll to carotenoid content (Chl/Car) (R2 = 0.71, p < 0.01) and between NPQleaf and the actual photochemical efficiency of photosystem II (ΔF/Fm′) (R2 = 0.81, p < 0.005). At the early growing stage, both canopy-level PRI and NPQleaf are good indicators of water stress (R2 = 0.65 and p < 0.05; R2 = 0.63 and p < 0.05, respectively). For assessment of extreme water stress on plant growth, a relationship is also established between the quantum yield of photochemistry in PSII (ΦP) and the quantum yield of fluorescence (ΦF) as determined from photochemical quenching (PQ) and non-photochemical quenching (NPQleaf) of excitation energy at different water stress levels. These results would be helpful in monitoring soil water stress on crops at large scales using remote sensing techniques.

Nitrogen is an essential nutrient in many terrestrial ecosystems because it affects vegetation’s primary production. Due to the variety of nitrogen-containing substances and the differences in their composition across species, statistical approaches are now dominant in remote sensing retrieval of leaf nitrogen content. Many studies remove spectral regions characterized by strong water absorptions before retrieving nitrogen content, because water is believed to mask the absorption features of nitrogen. The objectives of this study are to discuss the necessity of this practice and to explore how water absorption affects leaf nitrogen estimation. Spectral measurements and chemical analyses for Maize, Sawtooth Oak, and Sweetgum leaves were carried out in 2014. The leaf optical properties model PROSPECT5 was used to eliminate the influences of water on the measured reflectance spectra. The inversion accuracy of PROPECT5 for chlorophyll, carotenoid, water, and dry matter of Maize was also discussed. Measured, simulated, and water-removed spectra were used to: (1) find the optimal nitrogen-related spectral index; and (2) regress with the area-based leaf nitrogen concentration (LNC) using the partial least square regression technique (PLSR). Two types of spectral indices were selected in this study: Normalized Difference Spectral Index (NDSI) and Ratio Spectral Index (RSI). Additionally, first-order derivative forms of measured, simulated, and water-removed spectra were devised to search for the optimal spectral indices. Finally, species-specific optimal indices and cross-species optimal indices, as well as their root mean square errors (RMSE) and coefficients of determination (R2), were obtained. The Ending Top Percentile (ETP), an indicator of the performance of cross-species optimal indices, was also calculated. PLSR was combined with leave-one-out cross validation (LOOCV) for each species. The predicted root mean square errors (RMSEP) and predicted R2 were finally calculated. The results showed that chlorophyll, carotenoid, and water contents could be estimated with R2 of 0.75, 0.59, and 0.69, respectively, which were acceptable for fresh leaves. The dry matter was retrieved with a relatively lower accuracy because of the fixed absorption coefficients adopted by PROSPECT5. The performances of species-specific optimal indices using water-free spectra were comparable to or worse than the corresponding indices derived with measured or simulated spectra. Compared with measured spectra, ETP did not change much after the effects of water were removed, and the R2 between cross-species optimal spectral indices and area-based LNC for Sawtooth Oak and Sweetgum decreased while it remained almost the same for Maize, suggesting that the water-removed cross-species optimal indices were inferior to the corresponding optimal indices found without water removal. ETP was larger than 30% for all spectra, demonstrating the non-existence of common optimal NDSI or RSI for the three species. After water removal, the accuracy of PLSR for Sawtooth Oak and Sweetgum decreased and increased negligibly for Maize. The results suggest that water absorption has limited effects on reducing the accuracy of leaf nitrogen estimation. On the contrary, the accuracy may decrease due to the loss of spectral information caused by the removal of water-sensitive spectral regions.

Chlorophyll is the main light-harvesting pigment in leaves, facilitating photosynthesis and indicating the supply of nitrogen for photosynthetic enzymes. In this study, we explore the feasibility of integrating leaf chlorophyll content (Chlleaf) into a Terrestrial Biosphere Model (TBM), as a proxy for the leaf maximum carboxylation rate at 25°C (Vmax25), for the purpose of improving carbon and water flux estimation. Measurements of Chlleaf and Vmax25 were made in a deciduous forest stand at the Borden Forest Research Station in southern Ontario, Canada, where carbon and water fluxes were measured by the eddy covariance method. The use of Chlleaf-based Vmax25 in the TBM significantly reduces the bias of estimated gross primary productivity (GPP) and evapotranspiration (ET) and improves the temporal correlations between the simulated and the measured fluxes, relative to the commonly employed cases of using specified constant Vmax25, leaf area index (LAI)-based Vmax25 or specific leaf area (SLA)-based Vmax25. The biggest improvements are found in spring and fall, when the mean absolute errors (MAEs) between modelled and measured GPP are reduced from between 2.2–3.2 to 1.8gCm−2d−1 in spring and from between 2.1–2.8 to 1.8gCm−2 d−1 in fall. The MAEs in ET estimates are reduced from 0.7–0.8mmd−1 to 0.6mmd−1 in spring, but no significant improvement is noted in autumn. A two-leaf upscaling scheme is used to account for the uneven distribution of incoming solar radiation inside canopies and the associated physiological differences between leaves. We found that modelled Vmax25 in sunlit leaves is 34% larger than in the shaded leaves of the same Chlleaf, which echoes previous physiological studies on light acclimation of plants. This study represents the first case of the incorporation of chlorophyll as a proxy for Vmax25 in a two-leaf TBM at a forest stand and demonstrates the efficacy of using chlorophyll to constrain Vmax25 and reduce the uncertainties in GPP and ET simulations.