Leaf Area Index Data Research Articles

Polarization of Light Reflected by Grass: Modeling Using Visible-Sunlit Areas

The Bidirectional polarization distribution function (BPDF) of land surfaces is important for studies of land surfaces and aerosol. With the availability of a huge number of polarization measurements, several semi-empirical BPDF models have been proposed. However, these models do not pay much attention to canopy structure, which is fundamental for generation of polarization. In this article, we propose a new BPDF model using canopy structure information, which is parameterized by visible-sunlit areas. It is evaluated over grassland using POLDER BPDF and MODIS leaf area index data sets. Experiments suggest that compared to Nadal–Bréon and Litvinov models, the new BPDF model reduces root-mean-square error by 7% and 10%, respectively. The new BPDF model also provides better performance when it is fitted using observations clustered by sun zenith angle. The new BPDF model thus provides an effective tool for the study of land surface polarization.

Spatiotemporal dynamics in assimilated-LAI phenology and its impact on subtropical bamboo forest productivity

Phenology has a significant effect on forest growth and directly affects the forest ecosystem carbon cycle. Bamboo forests possess strong carbon sequestration capacities. However, it is not clear whether variations in phenology increase or decrease carbon uptake and storage in subtropical bamboo forests. We first extracted the length of the growing season (LOS) by coupling a data assimilation algorithm and MODIS leaf area index (LAI) data, and then the LOS was used to drive the integrated terrestrial ecosystem carbon-budget (InTEC) model to simulate gross primary productivity (GPP) and net ecosystem productivity (NEP) in Zhejiang Province from 2001 to 2017. Our results showed that the LOS estimation using the assimilated LAI time series was more reliable than that of the MODIS LAI and enhanced vegetation index (EVI). The annual average LOS increased on average by 0.76 day yr−1 from 2001 to 2017. The GPP and NEP simulations based on the LAI assimilation-based phenology indicated that bamboo forest ecosystems possess strong carbon sequestration capacities and act as carbon sinks, with mean annual GPP and NEP values of 434.74 ± 257.93 g C m−2 yr−1 and 141.42 ± 82.54 g C m−2 yr−1, respectively, during 2001–2017. An increase of one day in the regional annual LOS increases the annual average GPP and NEP by 1.34 g C m−2 yr−1 and 0.75 g C m−2 yr−1, respectively. Moreover, the interannual variation of NEP was significantly correlated with precipitation and temperature, whereas GPP was not. Our results demonstrated that phenology extraction based on LAI data assimilation should play an important role in the simulation of bamboo forest productivity with ecological process models. The variation in phenology induced by climate change can strengthen the bamboo forest carbon sink, which is of great significance for subtropical forests coping with climate change in the future.

Nondestructive estimation of potato yield using relative variables derived from multi-period LAI and hyperspectral data based on weighted growth stage

BackgroundThe accurate estimation of potato yield at regional scales is crucial for food security, precision agriculture, and agricultural sustainable development.MethodsIn this study, we developed a new method using multi-period relative vegetation indices (rVIs) and relative leaf area index (rLAI) data to improve the accuracy of potato yield estimation based on the weighted growth stage. Two experiments of field and greenhouse (water and nitrogen fertilizer experiments) in 2018 were performed to obtain the spectra and LAI data of the whole growth stage of potato. Then the weighted growth stage was determined by three weighting methods (improved analytic hierarchy process method, IAHP; entropy weight method, EW; and optimal combination weighting method, OCW) and the Slogistic model. A comparison of the estimation performance of rVI-based and rLAI-based models with a single and weighted stage was completed.ResultsThe results showed that among the six test rVIs, the relative red edge chlorophyll index (rCIred edge) was the optimal index of the single-stage estimation models with the correlation with potato yield. The most suitable single stage for potato yield estimation was the tuber expansion stage. For weighted growth stage models, the OCW-LAI model was determined as the best one to accurately predict the potato yield with an adjusted R2 value of 0.8333, and the estimation error about 8%.ConclusionThis study emphasizes the importance of inconsistent contributions of multi-period or different types of data to the results when they are used together, and the weights need to be considered.

A spatial-temporal continuous dataset of the transpiration to evapotranspiration ratio in China from 1981\u20132015

The ratio of plant transpiration to total terrestrial evapotranspiration (T/ET) captures the role of vegetation in surface-atmosphere interactions. However, several studies have documented a large variability in T/ET. In this paper, we present a new T/ET dataset (also including transpiration, evapotranspiration data) for China from 1981 to 2015 with spatial and temporal resolutions of 0.05° and 8 days, respectively. The T/ET dataset is based on a model-data fusion method that integrates the Priestley-Taylor Jet Propulsion Laboratory (PT-JPL) model with multivariate observational datasets (transpiration and evapotranspiration). The dataset is driven by satellite-based leaf area index (LAI) data from GLASS and GLOBMAP, and climate data from the Chinese Ecosystem Research Network (CERN). Observational annual T/ET were used to validate the model, with R2 and RMSE values were 0.73 and 0.07 (12.41%), respectively. The dataset provides significant insight into T/ET and its changes over the Chinese terrestrial ecosystem and will be beneficial for understanding the hydrological cycle and energy budgets between the land and the atmosphere.

Generating Time-Series LAI Estimates of Maize Using Combined Methods Based on Multispectral UAV Observations and WOFOST Model.

Green leaf area index (LAI) is an important variable related to crop growth. Accurate and timely information on LAI is essential for developing suitable field management strategies to mitigate risk and boost yield. Several remote sensing (RS) based methods have been recently developed to estimate LAI at the regional scale. However, the performance of these methods tends to be affected by the quality of RS data, especially when time-series LAI are required. For crop LAI estimation, supplementary growth information from crop model is helpful to address this issue. In this study, we focus on the regional-scale LAI estimations of spring maize for the entire growth season. Using time-series multispectral RS data acquired by an unmanned aerial vehicle (UAV) and the World Food Studies (WOFOST) crop model, three methods were applied at different crop growth stages: empirical method using vegetation index (VI), data assimilation method and hybrid method. The VI-based method and assimilation method were used to generate time-series LAI estimations for the whole crop growth season. Then, a hybrid method specially for the late-stage LAI retrieval was developed by integrating WOFOST model and data assimilation. Using field-collected LAI data in Hongxing Farm in 2014, the performances of these three methods were evaluated. At the early stage, the VI-based method (R2 = 0.63, RMSE = 0.16, n = 36) achieved higher accuracy than the assimilation method (R2 = 0.54, RMSE = 0.52, n = 36), whereas at the mid stage, the assimilation method (R2 = 0.63, RMSE = 0.46, n = 28) showed higher accuracy than the VI-based method (R2 = 0.41, RMSE = 0.51, n = 28). At the late stage, the hybrid method yielded the highest accuracy (R2 = 0.63, RMSE = 0.46, n = 29), compared with the VI-based method (R2 = 0.19, RMSE = 0.43, n = 28) and the assimilation method (R2 = 0.20, RMSE = 0.44, n = 29). Based on the results above, we considered a combination of the three methods, i.e., the VI-based method for the early stage, the assimilation method for the mid stage, and the hybrid method for the late stage, as an ideal strategy for spring-maize LAI estimation for the entire growth season of 2014 in Hongxing Farm, and the accuracy of the combined method over the whole growth season is higher than that of any single method.

Large‐scale ecological field data for satellite validation in deciduous forests and grasslands

AbstractIn situ accurate data sets of leaf area index (LAI), above‐ground biomass (AGB), and fraction of absorbed photosynthetically active radiation (fAPAR) are indispensable to validate and improve ecological products obtained from satellites. In situ data for satellite validation must be created not from a single‐point data but from areal data (such as multiple‐points data) representing a satellite footprint. Using multiple‐points data, the error of in situ data can be calculated statistically. The quantification of the error in the in situ data enables us to evaluate the discrepancy between the satellites' products and the in situ data as the error in the in situ data and the estimation error in the products separately. Besides, the accuracy of the in situ data is required to be much higher than the accuracy of the satellite products which was officially set. To obtain such in situ data, we have established observation sites for typical land cover types in East Asia, from temperate to cool ecosystems: deciduous needle‐leaved forest (DNF), evergreen needle‐leaved forest (ENF), deciduous broad‐leaved forest (DBF), and grassland (GL). We conducted the observations in 500 m × 500 m areas, which is the footprint scale of the Global Change Observation Mission‐Climate satellite. In this paper, the data of LAI, AGB, and fAPAR observed at DNF, DBF, and GL (i.e., except at ENF) are reported. These data are useful even for the validation of other satellite products, especially with higher spatial resolution. Also, the long‐term tree census data from 2005 to 2018 at DNF are reported.The complete data set for this abstract published in the Data Paper section of the journal is available in electronic format in MetaCat in JaLTER at http://db.cger.nies.go.jp/JaLTER/metacat/metacat/ERDP-2020-16.1/jalter-en.

Assessment of Cornfield LAI Retrieved from Multi-Source Satellite Data Using Continuous Field LAI Measurements Based on a Wireless Sensor Network

Accurate and continuous monitoring of leaf area index (LAI), a widely-used vegetation structural parameter, is crucial to characterize crop growth conditions and forecast crop yield. Meanwhile, advancements in collecting field LAI measurements have provided strong support for validating remote-sensing-derived LAI. This paper evaluates the performance of LAI retrieval from multi-source, remotely sensed data through comparisons with continuous field LAI measurements. Firstly, field LAI was measured continuously over periods of time in 2018 and 2019 using LAINet, a continuous LAI measurement system deployed using wireless sensor network (WSN) technology, over an agricultural region located at the Heihe watershed at northwestern China. Then, cloud-free images from optical satellite sensors, including Landsat 7 the Enhanced Thematic Mapper Plus (ETM+), Landsat 8 the Operational Land Imager (OLI), and Sentinel-2A/B Multispectral Instrument (MSI), were collected to derive LAI through inversion of the PROSAIL radiation transfer model using a look-up-table (LUT) approach. Finally, field LAI data were used to validate the multi-temporal LAI retrieved from remote-sensing data acquired by different satellite sensors. The results indicate that good accuracy was obtained using different inversion strategies for each sensor, while Green Chlorophyll Index (CIgreen) and a combination of three red-edge bands perform better for Landsat 7/8 and Sentinel-2 LAI inversion, respectively. Furthermore, the estimated LAI has good consistency with in situ measurements at vegetative stage (coefficient of determination R2 = 0.74, and root mean square error RMSE = 0.53 m2 m−2). At the reproductive stage, a significant underestimation was found (R2 = 0.41, and 0.89 m2 m−2 in terms of RMSE). This study suggests that time-series LAI can be retrieved from multi-source satellite data through model inversion, and the LAINet instrument could be used as a low-cost tool to provide continuous field LAI measurements to support LAI retrieval.

Using climate-driven leaf phenology and growth to improve predictions of gross primary productivity in North American forests.

Forest ecosystems are an important sink for terrestrial carbon sequestration. Hence, accurate modeling of the intra- and interannual variability of forest photosynthetic productivity remains a key objective in global biology. Applying climate-driven leaf phenology and growth in models may improve predictions of the forest gross primary productivity (GPP). We used a dynamic non-structural carbohydrates (NSC) model (FORCCHN2) that couples leaf development and phenology to investigate the relationships among photosynthesis and environmental factors. FORCCHN2 simulates spring and autumn phenological events from heat and chilling, respectively. Leaf area index data from satellites along with climate data estimated localized phenological parameters. NSC limitation, immediate temperature, accumulated heat, and growth potential comprised a daily leaf-growth model. Functionally, leaf growth was decoupled from photosynthesis. Leaf biomass determined overall photosynthetic production. We compared this model with outputs of the other six terrestrial biospheric models and with observations from the North American Carbon Program Site Interim Synthesis in 18 forest sites. This model improved the predicted performance of yearly GPP with a 57%-210% increase in correlation (median) and up to a 102% reduction in biases (median), compared to three prognostic models and three prescribed models. At the North America continental scale, the model predicted the average annual GPP of 7.38Pg C/year from forest ecosystems during 1985-2016. The results showed an increasing trend of GPP in North America (1.0Pg C/decade). The inclusion of climate-driven phenology and growth has a significant potential for improving dynamic vegetation models, and promotes a further understanding of the complex relationship between environment and photosynthesis.

Accelerated rate of vegetation green-up related to warming at northern high latitudes.

Mid- to high-latitude vegetation are experiencing changes in their seasonal cycles as a result of climate change. Although the rates of seasonal growth from winter dormancy to summer maturity have accelerated because of changes in environmental conditions, less attention has been paid to the rate of vegetation green-up (RVG) and its dynamics, which could advance vegetation maturity. We analyzed the long-term changes in RVG and the drivers at high northern latitudes for 35years (1982-2016) using satellite-retrieved leaf area index data based on partial correlation analyses and multivariable linear regression. The rates tended to increase significantly with time, particularly at high latitudes above 60°N in North America (1.8% mon-1 decade-1 , p<.01) and Eurasia (1.0% mon-1 decade-1 , p<.01). The increasing trend in North America was mostly because of increased heat accumulation in spring (1.2% mon-1 decade-1 ), that is, more rapid green-up owing to warming, with an increased carbon dioxide concentration (0.6 mon-1 decade-1 ). The trend in Eurasia, however, was induced by warming, increased carbon dioxide concentration, and stronger radiation, 1.0%, 0.7%, and 0.5% mon-1 decade-1 , respectively, but was partly counteracted by earlier pregreen-up dates of -1.2% mon-1 decade-1 , that is, earlier initiation of growth which counteracted green-up rate acceleration. The results suggested that warming was the predominant factor influencing the accelerated RVG at high latitudes; however, Eurasian vegetation exhibited different green-up dynamics, mitigating the influence of warming with the earlier pregreen-up. Our findings imply that high-latitude warming will drive vegetation seasonality toward rapid green-up and early maturity, leading to the reinforcement of climate-vegetation interactions; however, the consequences will be more distinct in North America owing to the absence of alleviation by earlier pregreen-up.

Comparison of EPIC-Simulated and MODIS-Derived Leaf Area Index (LAI) across Multiple Spatial Scales.

Modeled leaf area index (LAI) in conjunction with satellite-derived LAI data streams may be used to support various regional and local scale air quality models for retrospective and future meteorological assessments. The Environmental Policy Integrated Climate (EPIC) model holds promise for providing LAI within a dynamic range for input into climate and air quality models, improving on current LAI distribution assumptions typical within atmospheric modeling. To assess the potential use of EPIC LAI, we first evaluated the Moderate Resolution Imaging Spectroradiometer (MODIS) LAI product collections 5 and 6 (i.e., Mc5, Mc6) with in situ LAI estimates upscaled at four 1.0 km resolution research sites distributed over the Albemarle-Pamlico Basin in North Carolina and Virginia, USA. We then compared the EPIC modeled 12.0 km resolution LAI to aggregated MODIS LAI (Mc5, Mc6) over a 3 × 3 grid (or 36 km × 36 km) centered over the same four research sites. Upscaled in situ LAI comparison with MODIS LAI showed improvement with the newer collection where the Mc5 overestimate of +2.22 LAI was reduced to +0.97 LAI with the Mc6. On three of the four sites, the EPIC/MODIS LAI comparison at 12.0 km resolution grid showed similar weighted mean LAI differences (LAI 1.29-1.34), with both Mc5 and Mc6 exceeding EPIC LAI across most dates. For all four research sites, both MODIS collections showed a positive bias when compared to EPIC LAI, with Mc6 (LAI = 0.40) aligning closer to EPIC than the Mc5 (LAI = 0.61) counterpart. Despite modest differences between both MODIS collections and EPIC LAI, the overestimation trend suggests the potential for EPIC to be used for future meteorological alternative management applications on a regional or national scale.

Modeling crop growth and land surface energy fluxes in wheat–maize double cropping system in the North China Plain

The land surface characteristic influences climatic environments by controlling the land surface energy and water fluxes. Although large progresses have been made in coupling detailed crop models into land surface models, there is much room left for improved estimates of surface fluxes in double cropping system, such as the winter wheat (Triticum aestivum L.)–summer maize (Zea mays L.) system under intensive influences of climate change and human activities in the North China Plain (NCP). Here, we modeled the growth and surface fluxes in winter wheat–summer maize double cropping system in the NCP through modified SiBcrop model. The parameters related with the crop growth in winter wheat and maize sub-models were emphasized to better simulate the seasonal dynamics of phenology phase, leaf area index (LAI), and latent (LH) and sensible (SH) heat fluxes. Observational data for phenology, LAI, and energy fluxes were compiled from Yucheng and Guantao stations, to calibrate and validate the SiBcrop model. The results showed that the adjusted SiBcrop captured better the seasonal dynamics of phenology, LAI, LH, and SH, relative to the original code. The largest biases in model results were in SH when horizontal advection prevails and in LH when there is summer maize senescence. Sensitivity analysis illustrated that growth duration, emergence date, LH, SH, and Bowen ratio were quite sensitive to sowing date of wheat, and growth duration and emergence and harvest dates to sowing of maize. The calibrated SiBcrop also simulated the LH and SH well at Guantao station. The adjusted SiBcrop is capable of assessing the responses of surface biophysical processes to the changes of human management and climate change in the double cropping system in the NCP.

ANALYSIS OF RESPONSE AND RECOVERY OF VEGETATION TO FOREST FIRE

Abstract. To analyse the response and recovery characteristics of forest to forest fire, this paper selected the forest fire in the Greater Khingan Mountains (GKM) in China in 1987 and the forest fire in the Yellowstone National Park (YSP) in the United States in 1988. We first used Landsat-5 TM images before and after the fire to extract the burned area and calculate burn severity based on the Differential Normalized Burn Ratio (dNBR). Next, we analysed the response of forest vegetation to forest fire with different burn severity using the anomaly value of Leaf area index (LAI) derived from Global Land Surface Satellite (GLASS) products. And the recovery of forest vegetation after forest fire were revealed using time – series LAI data and MODIS Land cover data. The results showed that the LAI decreased rapidly after the forest fire, and the greater the burn severity, the higher the decreasing amplitude of LAI. The maximum decreasing amplitude of LAI in the burned areas with high burn severity were 1.3–3.8 times higher than that in low burn severity areas. The recovery time of LAI is affected by burn severity and manual interference. The recovery time of LAI in burned areas in the GKM is about 5–10 years, which in the burned areas with high burn severity is 2 times than that with low burn severity. The recovery time of LAI in the burned areas with low burn severity in the YSP is at least 20 years, while that with high burn severity will take longer time to recovery. And the manual interference accelerated the recovery of LAI in the GKM. Our research on the response and recovery of vegetation is helpful for formulating and implementing adaptation and mitigation strategies in response to forest fire.

Observed Vegetation Greening and Its Relationships with Cropland Changes and Climate in China

Chinese croplands have changed considerably over the past decades, but their impacts on the environment remain underexplored. Meanwhile, understanding the contributions of human activities to vegetation greenness has been attracting more attention but still needs to be improved. To address both issues, this study explored vegetation greening and its relationships with Chinese cropland changes and climate. Greenness trends were first identified from the normalized difference vegetation index and leaf area index from 1982–2015 using three trend detection algorithms. Boosted regression trees were then performed to explore underlying relationships between vegetation greening and cropland and climate predictors. The results showed the widespread greening in Chinese croplands but large discrepancies in greenness trends characterized by different metrics. Annual greenness trends in most Chinese croplands were more likely nonlinearly associated with climate compared with cropland changes, while cropland percentage only predominantly contributed to vegetation greening in the Sichuan Basin and its surrounding regions with leaf area index data and, in the Northeast China Plain, with vegetation index data. Results highlight both the differences in vegetation greenness using different indicators and further impacts on the nonlinear relationships with cropland and climate, which have been largely ignored in previous studies.

A METHOD FOR DEVELOPING IRRIGATION DECISION SUPPORT SYSTEMS de novo: EXAMPLE OF SESAME (Sesamum indicum L.) A KNOWN DROUGHT TOLERANT SPECIES

Irrigation decision support systems (DSS) are tools that can help achieve higher system level water use efficiency by more accurately targeting water application to crop need. They also have a role to play in preventing over-irrigation of drought tolerant crops that can be sensitive to flooded conditions. However, there are challenges in developing DSS for newly introduced crops in regions where they have not typically been produced. One such crop is sesame, a recently introduced drought tolerant crop in the southeastern United States where up to 50% of the agronomic crop production is irrigated. A first irrigation DSS, called SesameFARM1, was developed in 2013 by estimating a water demand curve using the Food and Agricultural Organization (FAO) crop coefficient (Kc) values, seasonal measures of the leaf area index (LAI) for the crop measured in 2012, and an estimated maximum rooting depth. On a daily time-step, SesameFARM1 estimated crop evapotranspiration using the product of the Kc and the ETo obtained from a local weather station, and an estimation of the maximum plant available water based on the available water capacity for the soil type and the maximum crop rooting depth. To improve SesameFARM1, LAI data from 6 years of field trials along with root data from a previous study were used to develop a breakpoint regression model for LAI and total functional root length. In SesameFARM2, the resulting curve for LAI was used to estimated continuous Kc values; and total measured functional root length replaced the estimation of maximum rooting depth in a new variable called root water access. SesameFARM2 performance was compared to SesameFARM1 using 6 years of weather data from Citra FL. SesameFARM2 consistently recommended less water be applied, and recommendations of water application during the senescence phase of the crop were reduced. Because of sesame’s inherent drought tolerance, a more conservative irrigation recommendation is likely more appropriate. Therefore, SesameFARM2 is likely a better model than SesameFARM1 and may help growers unfamiliar with sesame achieve irrigation higher irrigation crop water productivity.

Reconstruction of time series leaf area index for improving wheat yield estimates at field scales by fusion of Sentinel-2, -3 and MODIS imagery

Continuous time series crop growth monitoring during the main crop growth and development period at field scales is very important for crop management and yield estimation. For more than a decade, the time series leaf area index (LAI) products obtained from high temporal resolution satellites have been widely used in global crop growth monitoring. However, the spatial resolutions (250–1000 m) of these satellite sensors are too coarse for areas with complex and diverse land-use types, especially in China, which causes great uncertainties in crop growth monitoring and yield estimation results. In addition, due to the influence of clouds, optical remote sensing satellites cannot obtain continuous time series data at a given time step over the main crop growth and development period. In this paper, a method based on spatiotemporal data fusion and singular vector decomposition (SVD) is proposed to reconstruct field-scale time series LAI imagery over the main growth and development period of winter wheat. In this method, the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) is used to fuse the reflectance imagery of Sentinel-2 and Sentinel-3, and a linear regression model between the LAI data retrieved from the fused reflectance data and the singular vectors derived from the 4-day interval Moderate Resolution Imaging Spectroradiometer (MODIS) LAI data is established to reconstruct the continuous time series field-scale LAI imagery at a given time step. The accuracy of the reconstructed LAI and its capability for winter wheat yield estimation were tested on the Guanzhong Plain of China. The results indicate that (1) the ESTARFM model can fuse the reflectance bands from visible to shortwave infrared of Sentinel-2 and Sentinel-3 on the Guanzhong Plain accurately within a 20-day interval of the winter wheat growth and development period; (2) the 4-day interval field-scale LAI imagery over the main winter wheat growth and development period can be accurately reconstructed based on the linear regression models between the fused LAI data and the singular vectors derived from the 4-day interval MODIS LAI data; and (3) the yield map estimated from the reconstructed field-scale LAI shows more yield distribution details than MODIS yield estimation results. This study shows the feasibility of reconstructing continuous time series field-scale LAI data over the main winter wheat growth and development period on the Guanzhong Plain by combining the spatiotemporal data fusion model with SVD and the potential for estimating the winter wheat yield at field scales.

Evaluation of Evapotranspiration Models Using Different LAI and Meteorological Forcing Data from 1982 to 2017

We evaluated the performance of three global evapotranspiration (ET) models at local, regional, and global scales using the multiple sets of leaf area index (LAI) and meteorological data from 1982 to 2017 and investigated the uncertainty in ET simulations from the model structure and forcing data. The three ET models were the Simple Terrestrial Hydrosphere model (SiTH) developed by our team, the Priestley–Taylor Jet Propulsion Laboratory model (PT-JPL), and the MODerate Resolution Imaging Spectroradiometer (MODIS) ET algorithm (MOD16). Comparing the observed with simulated monthly ET by the three models over 43 Fluxnet sites, we found that SiTH overestimated ET for forests with mean slope from 1.25 to 1.67, but it performed better than the other two models over short vegetation. MOD16 and PT-JPL models simulated well for forests but poorly in dryland biomes (slope = 0.25~0.55; R2 = 0.02~0.46). At the catchment scale, all models performed well, except for some tropical and high latitudinal catchments, with NSE values lower than 0 and RMSE and MAE values far beyond their mean values. At the global scale, SiTH highly overestimated ET in tropics, while PT-JPL slightly underestimated ET between 30°N and 60°N and MOD16 underestimated ET between 15°S and 30°S. Generally, the PT-JPL provided the better performance than SiTH and MOD16 models. This study also revealed that the estimated ET by SiTH and especially PT-JPL model were influenced by the uncertainty in meteorological data, and the estimated ET was performed better using MERRA-2 datasets for PT-JPL and using ERA5 datasets for SiTH. While the estimated ET by MOD16 were relatively sensitive to LAI data. In addition, our results suggested that the GLOBMAP and GIMMS datasets were more suitable for long-term ET simulations than the GLASS dataset.

Examining land surface phenology in the tropical moist forest eco-zone of South America

Using leaf area index (LAI) data from 1981 to 2014 in the tropical moist forest eco-zone of South America, we extracted start (SOS) and end (EOS) dates of the active growing season in forest and savanna at each pixel. Then, we detected spatiotemporal characteristics of SOS and EOS in the two vegetation types. Moreover, we analyzed relationships between interannual variations of SOS/EOS and climatic factors, and simulated SOS/EOS time series based on preceding mean air temperature and accumulated rainfall. Results show that mean SOS and EOS ranged from 260 to 330day of year (DOY) and from 150 to 260DOY across the study region, respectively. From 1981 to 2014, SOS advancement is more extensive than SOS delay, while EOS advancement and delay are similarly extensive. For most pixels of forest and savanna in tropical moist forest eco-zone, preceding rainfall correlates predominantly negatively with SOS but positively with EOS, while the relationship between preceding temperature and phenophases is location-specific. In addition, preceding rainfall is more extensive than preceding temperature in simulating SOS, while both preceding rainfall and temperature play an important role for simulating EOS. This study highlights the reliability of using LAI data for long-term phenological analysis in the tropical moist forest eco-zone.

Future greening of the Earth may not be as large as previously predicted

Changes in global vegetation growth and its drivers during recent decades have been well studied with satellite data, ecosystem models and field experiments. However, a systematic understanding of how global vegetation will respond to projected changes in climate and atmospheric composition is still lacking. Here, we analyze changes in projected global leaf area index (LAI) from 16 Coupled Model Intercomparison Project phase 5 (CMIP5) and 17 phase 6 (CMIP6) Earth System Models (ESMs) during the 21st century under future scenarios RCP2.6, RCP4.5 and RCP8.5 and future scenarios SSP1–2.6, SSP2–4.5, SSP3–7.0 and SSP5–8.5, respectively. In addition to the widely-used multi-model ensemble mean (MME) method, we employed the Reliability Ensemble Averaging (REA) strategy to integrate the modeled results. The REA integration weights were determined by combining the performance of the ESMs in simulating present-day LAI changes, which was based on a comparison with three long-term remote sensing LAI data sets, with their convergence of future predictions. The results suggest that global LAI will increase under all seven future scenarios with both integration methods. The magnitude of LAI growth is expected to increase with the forcing levels of the scenarios. The ESMs integrated with the REA weights predicted significantly smaller magnitudes and lower uncertainties in global LAI growth by the end of the 21st century than those integrated with the MME method. Both REA and MME results suggest that the growth in atmospheric CO2 concentration is the main positive driver of the projected increment of global LAI, which is partly offset by the negative effects of global warming. Our improved comprehensive prediction based on CMIP5 and CMIP6 ESMs using the REA integration strategy provides a more robust estimation of global vegetation change during the 21st century, and is expected to help better understand the state of the planet in the coming decades.

Generating a Spatio-Temporal Complete 30 m Leaf Area Index from Field and Remote Sensing Data

The leaf area index (LAI) is an important parameter for vegetation monitoring and land surface ecosystem research. Although a variety of LAI products have been generated, the moderate to coarse spatial resolution and low temporal resolution of these products are insufficient for regional-scale analysis. In this study, a modified ensemble Kalman filter model (MEnKF) was proposed to generate spatio-temporal complete 30 m LAI data. High-quality, filtered historical Moderate-resolution Imaging Spectroradiometer (MODIS) LAI data were used to obtain the LAI background, and an LAI temporal dynamic model was constructed based on it. An improved back-propagation (BP) neural network based on a simulated annealing algorithm (SA-BP) was constructed with paired Landsat surface reflectance data and field LAI data to generate a 30 m LAI. The MEnKF was used to estimate the spatio-temporal complete LAI beginning from the LAI peak value position where Landsat observations were available. The spatio-temporal 30 m LAI was estimated in farmland (Pshenichne), grassland (Zhangbei), and woodland (Genhe) sites. The results indicate that the MEnKF-estimated LAI is consistent with the field measurements for all sites (the coefficient of determination ( R 2 ) = 0.70; root mean squared error (RMSE) = 0.40) and is better than that of the conventional sequence data assimilation algorithm ( R 2 = 0.40; RMSE = 0.78). The regional LAI captures the vegetation growth pattern and is consistent with the Landsat LAI, with an R 2 larger than 0.65 and an RMSE less than 0.51. The proposed MEnKF algorithm, which effectively avoids error accumulation in the data assimilation scheme, is an efficient method for spatio-temporal complete 30 m LAI estimation.

The influence of assimilating leaf area index in a land surface model on global water fluxes and storages

Abstract. Vegetation plays a fundamental role not only in the energy and carbon cycles but also in the global water balance by controlling surface evapotranspiration (ET). Thus, accurately estimating vegetation-related variables has the potential to improve our understanding and estimation of the dynamic interactions between the water, energy, and carbon cycles. This study aims to assess the extent to which a land surface model (LSM) can be optimized through the assimilation of leaf area index (LAI) observations at the global scale. Two observing system simulation experiments (OSSEs) are performed to evaluate the efficiency of assimilating LAI into an LSM through an ensemble Kalman filter (EnKF) to estimate LAI, ET, canopy-interception evaporation (CIE), canopy water storage (CWS), surface soil moisture (SSM), and terrestrial water storage (TWS). Results show that the LAI data assimilation framework not only effectively reduces errors in LAI model simulations but also improves all the modeled water flux and storage variables considered in this study (ET, CIE, CWS, SSM, and TWS), even when the forcing precipitation is strongly positively biased (extremely wet conditions). However, it tends to worsen some of the modeled water-related variables (SSM and TWS) when the forcing precipitation is affected by a dry bias. This is attributed to the fact that the amount of water in the LSM is conservative, and the LAI assimilation introduces more vegetation, which requires more water than what is available within the soil.