Flux Tower Measurements Research Articles

Data-constrained modeling of terrestrial gross primary production over the Tibetan Plateau for 2003–2019

The gross primary productivity (GPP) of terrestrial ecosystem is the largest carbon flux between atmosphere and land surface. However, accurately simulating ecosystem GPP remains a great challenge for most land surface models (LSMs) due to the biased leaf area index (LAI) simulated by the models. In this study, we use remotely sensed LAI dataset for the period 2003–2019 to drive the Farquhar model and simulate the spatial-temporal changes of GPP for the alpine ecosystem over Tibetan Plateau. The annual GPP over the Tibetan Plateau is estimated to be 540.8 ± 27.3 Tg C yr−1, which is consistent with the benchmark datasets derived from flux tower measurements and remote sensing estimations. The GPP for the past two decades has been increasing at a rate of 2.6 Tg C yr−2. The canopy greening featured by increasing LAI contributed to 27.2 % of the increasing trend in GPP, while the direct impacts from warming and wetting climate contributed to 65.2 % of GPP changes. The contribution from the direct impact of elevated atmosphere CO2 concentration is only 6.7 %. In this study we show that constraining Farquhar model with remotely sensed LAI datasets could provide reliable simulation of GPP. Given the great challenge in modeling LAI and the considerable overestimation of LAI in current LSMs, our results highlight the importance of improving LAI modeling in LSMs and the necessity of constraining LAI when tuning the parameters for photosynthesis module.

Exploring the Potential of Long Short‐Term Memory Networks for Predicting Net CO2 Exchange Across Various Ecosystems With Multi‐Source Data

AbstractUpscaling flux tower measurements based on machine learning (ML) algorithms is an essential approach for large‐scale net ecosystem CO2 exchange (NEE) estimation, but existing ML upscaling methods face some challenges, particularly in capturing NEE interannual variations (IAVs) that may relate to lagged effects. With the capacity to characterize temporal memory effects, the Long Short‐Term Memory (LSTM) networks are expected to help solve this problem. Here we explored the potential of LSTM for predicting NEE across various ecosystems using flux tower data over 82 sites in North America. The LSTM model with differentiated plant function types (PFTs) demonstrates the capability to explain 79.19% (R2 = 0.79) of the monthly variations in NEE within the testing set, with RMSE and Mean Absolute Error values of 0.89 and 0.57 g C m−2 d−1 respectively (r = 0.89, p < 0.001). Moreover, the LSTM model performed robustly in predicting cross‐site variability, with 67.19% of the sites that can be predicted by both LSTM models with and without distinguished PFTs showing improved predictive ability. Most importantly, the IAV of predicted NEE highly correlated with that in flux observations (r = 0.81, p < 0.001), clearly outperforming that by the random forest model (r = −0.21, p = 0.011). Among all nine PFTs, solar‐induced chlorophyll fluorescence, downward shortwave radiation, and leaf area index are the most important variables for explaining NEE variations, collectively accounting for approximately 54.01% in total. This study highlights the great potential of LSTM for improving carbon flux upscaling with multi‐source remote sensing data.

Temporal Error Correlations in a Terrestrial Carbon Cycle Model Derived by Comparison to Carbon Dioxide Eddy Covariance Flux Tower Measurements

AbstractAtmospheric CO2 flux inversions require as input an estimate of spatial and temporal correlations of errors in their estimate of the prior mean. Some previous studies have used the differences in CO2 daily average flux estimates produced by terrestrial carbon cycle models and eddy covariance measurements to constrain the flux error correlations. Since inversions are starting to resolve the daily cycle, we set out to examine the correlations at sub‐daily time scales, as well as the correlations across years. To this end, we examine the autocorrelations in the difference between net ecosystem‐atmosphere exchange measurements from 75 AmeriFlux towers and temporally downscaled high‐spatial‐resolution flux estimates from the Carnegie‐Ames‐Stanford Approach (CASA) terrestrial carbon cycle model. We find that the daily cycle is prominent in these hourly autocorrelations and that these autocorrelations persist across years. We propose a family of functions to model these temporal correlations in atmospheric inversions, and use cross validation to determine which of the correlation functions best fits autocorrelation data from towers not in the training set. Correlation functions with a component that attempts to model the daily cycle in the differences match correlations from other towers better than those without. Those models that reproduce the same correlation structures at 1‐year intervals while modulating the amplitudes of the correlations between those intervals improve the fit still further.

Assessment of an evapotranspiration algorithm accounting for land cover types and photosynthetic perspectives using remote sensing images

ABSTRACT In this study, Eco-hydrometeorological Remote Sensing-based Penman-Monteith algorithm (Eh-RSPM) was developed by implementing the gross primary productivity into the revised Remote Sensing based Penman-Monteith algorithm (RS-PM). Evaluation of Eh-RSPM was conducted through comparison with in-situ measurements as well as model-based products (e.g. MODerate resolution Imaging Spectroradiometer (MODIS) 16 global ET products (MOD16 ET) and Surface Energy Balance System (SEBS)) during two years (2004 and 2012) in Northeast Asia. Comparison of ET from Eh-RSPM algorithm with five flux tower measurement agreed well with the flux tower datasets at the entire validation sites. Especially, Eh-RSPM showed advantages in improving the accuracy of ET at stations with relatively short canopy height (e.g. QHB and KBU site) as well as the forest site (e.g. SMK). Focusing on the forest site, Eh-RSPM exhibited slightly better statistical performance compared to MOD16. Specifically, the temporal mean bias and RMSD showed a slight improvement, decreasing from −15.40 W m−2 to −12.58 W m−2 and from 28.41 W m−2 to 25.26 W m−2, respectively. This is a key finding of this study, demonstrating the applicability of the improved ET algorithm to regions with significant forest cover. Similarly, spatial distribution of Eh-RSPM showed similar patterns with MOD16 and SEBS. Eh-RSPM strongly showed advantages over the land cover types with relatively shorter canopy height (e.g. grassland and alpine meadow) as well as the heterogeneous forest showed significant improvement in Eh-RSPM through considering the actual physiological behavior variation and influence of photosynthesis into ET calculation.

Empirical upscaling of OzFlux eddy covariance for high-resolution monitoring of terrestrial carbon uptake in Australia

Abstract. We develop high-resolution (1 km) estimates of gross primary productivity (GPP), ecosystem respiration (ER), and net ecosystem exchange (NEE) over the Australian continent for the period January 2003 to June 2022 by empirical upscaling of flux tower measurements. We compare our estimates with nine other products that cover the three broad categories that define current methods for estimating the terrestrial carbon cycle and assess if consiliences between datasets can point to the correct dynamics of Australia's carbon cycle. Our results indicate that regional empirical upscaling greatly improves upon the existing global empirical upscaling efforts, outperforms process-based models, and agrees much better with the dynamics of CO2 flux over Australia as estimated by two regional atmospheric inversions. Our nearly 20-year estimates of terrestrial carbon fluxes revealed that Australia is a strong net carbon sink of −0.44 PgC yr−1 (interquartile range, IQR = 0.42 PgC yr−1) on average, with an inter-annual variability of 0.18 PgC yr−1 and an average seasonal amplitude of 0.85 PgC yr−1. Annual mean carbon uptake estimated from other methods ranged considerably, while carbon flux anomalies showed much better agreement between methods. NEE anomalies were predominately driven by cumulative rainfall deficits and surpluses, resulting in larger anomalous responses from GPP than ER. In contrast, we show that the long-term average seasonal cycle is dictated more by the variability in ER than GPP, resulting in peak carbon uptake typically occurring during the cooler, drier austral autumn and winter months. This new estimate of Australia's terrestrial carbon cycle provides a benchmark for assessment against land surface model simulations and a means for monitoring of Australia's terrestrial carbon cycle at an unprecedented high resolution. We call this new estimate of Australia's terrestrial carbon cycle “AusEFlux” (Australian Empirical Fluxes).

Application of a two source energy balance model coupled with satellite based soil moisture and thermal infrared data

Satellite retrievals of thermal infrared data from the Earth’s surface when combined with the surface energy balance equation are widely used to monitor land surface evapotranspiration (ET). Unfortunately thermal infrared based models using this approach have a systematic positive bias with ET during soil water stress conditions. The Two-source Energy Balance model using information on Soil Moisture as another key constraint (TSEB-SM) improves model performance and utility. Here, high resolution satellite soil moisture data from SMAP (Soil Moisture Active Passive) were used to provide soil water boundary conditions for the TSEB-SM model. This SMAP driven TSEB-SM approach was applied in the Heihe river basin in China across a wide range of soil moisture conditions using MODIS thermal infrared land surface temperature (LST) data. The ET estimates from TSEB-SM were validated using flux tower measurements over the main land cover types in the study area and compared with the results from the original TSEB model. The comparison showed that the TSEB-SM approach reduced the RMSE error in latent heat flux estimates by 5 % to 20 % at satellite overpass time, and TSEB-SM outperformed the original TSEB model in daily ET estimates (RMSE values of 1.0 mm/d versus 1.5 mm/d, respectively) across all validation sites. The magnitude of the TSEB-SM improvements were larger early and late in the growing season and in arid areas, consistent with soil moisture improving estimates of ET particularly under times of higher water stress. The use of SMAP satellite soil moisture data with the TSEB-SM model increased the reliability of ET estimation and provides an improved capacity for continuos monitoring of drought effects on the global water cycle.

Modeling Urban Heat Islands and Thermal Comfort During a Heat Wave Event in East China With CLM5 Incorporating Local Climate Zones

AbstractThe urban expansion‐induced heat can exacerbate heat stress for urban dwellers, especially during heat waves. With a focus on the intra‐urban variability of urban heat islands (UHIs) and thermal comfort, the urban parameterization within the Community Land Model version 5 (CLM5) was modified incorporating the local climate zones (LCZs) framework, named CLM5‐LCZs, to simulate the urban climate during a heat wave (HW) event in the summer of 2013. The evaluation of model performance demonstrated that it did a reasonable job of simulating surface energy balance and thermal regimes in cities against observational fluxes from a flux tower measurement site and temperatures from automatic meteorological stations in Nanjing, China. Then we investigated the characteristics and causes of UHIs associated with local background climate, intra‐urban inhomogeneity and HW intensity in East China. The results exhibited that daytime and nighttime canopy urban heat island intensity (CUHII) were highest in the Compact Low Rise (LCZ3) and the Compact High Rise (LCZ1) areas, respectively, while surface urban heat island intensity (SUHII) peaked in the Large Low Rise (LCZ8) and the Compact High Rise (LCZ1) areas during daytime and nighttime, respectively. Urban dwellers were easier exposed to serious heat environment in LCZ3 and LCZ1 areas over the north subtropical climate zone. Contrasts of CUHII and SUHII among different urban classes could exceed 1.7°C and 5.4°C. The intra‐urban heterogeneity may alter the dominant factors controlling SUHII, which were also modulated by local climate and HW intensity. Unlike other controlling factors, the impact of local climate on the contribution from the urban‐rural contrast of convection efficiency was larger than urban features. Overall, CLM5‐LCZs displayed potential of implementing detailed simulations for inter‐ and intra‐city UHIs at a larger scale, and enhancing the capabilities in modeling urban climate and exploring the causes and controls of UHIs.

Large Scale Crop Water Footprint Evaluation Based on Remote Sensing Methods: A Case Study of Maize

AbstractAgriculture is the world's largest consumer of water resources, and accurate measurement of crop water footprint (CWF) can provide a scientific basis for evaluating the water use characteristics of agricultural production and guiding water management. The measurement of regional CWF requires large amounts of ground data, and remote sensing provides an effective means to scientifically measure the CWF on a large scale. In this study, we developed an effective means to estimate regional maize CWF based on global evapotranspiration (MOD16 and Global Land Data Assimilation System (GLDAS)‐Noah) products in China. And we also calculated the CWF based on the FAO Penman‐Monteith equation (FAO‐PM) on the site scale in China. We assessed the accuracy of these methods by comparing them against eight eddy‐covariance based flux tower measurements. Links and differences behind the results of the three water footprint calculations were analyzed in terms of the basic principles and characteristics of the calculations. The results showed that the CWF had a similar distribution based on the MOD16 and GLDAS‐Noah, reflecting the influence of regional soil moisture on the CWF. Computational rationale analysis of the three quantitative methods showed that the ETc‐based method from the FAO‐PM model was similar to the MOD16‐ and GLDAS‐Noah‐based CWF in the wetter areas, two remote sensing‐based methods considered the water constraints during soil evaporation and water dissipation during dry and wet canopy conditions, and were closer to reality than the ETc‐based methods in measuring the CWF in arid and semi‐arid regions.

Applicability evaluation of soil moisture constraint algorithms in remote sensing evapotranspiration models

Remotely sensed (RS) evapotranspiration (ET) models have been widely used to estimate ET over large areas. However, a common challenge for these models is the lack of reliable soil moisture (SM) constraints. Due to the lack of reliable, spatiotemporal continuous SM profile data, existing RS-ET models tend to use atmospheric or land surface variables as proxies for SM constraints. Although several proxy algorithms for SM constraints have been developed, few studies have evaluated their performance in ET simulations. To address this gap, we evaluated the applicability of five proxy algorithms for SM constraints (namely, fVPD, fLST, fDT, fZhang, and fdrying) to ET simulations in China using the Penman-Monteith-Leuning (PML) model. These algorithms were evaluated at 14 ChinaFlux sites and 286 basins using flux tower measurements and water balance-based ET estimates, respectively. The results show that among the five algorithms, fdrying performs test at the flux sites, with a median of Kling-Gupta efficiency (KGE) of 0.75. The second-best algorithm is fZhang (KGE = 0.73), followed by fDT (KGE = 0.70), fLST (KGE = 0.68), and fVPD (KGE = 0.65). The performance ranking of the five algorithms at the basin scale is consistent with that at the flux sites. Using the flux site SM measurements as a reference, we further found that the algorithms with better performance in ET simulations also have better SM simulation capabilities. This study highlights the importance of reliable SM constraints in the RS-ET models.

Impact of surface heterogeneity on flux tower measurements

Impact of surface heterogeneity on flux tower measurements

Developing global annual land surface phenology datasets (1982–2018) from the AVHRR data using multiple phenology retrieval methods

Land surface phenology that reflects periodical changes in terrestrial vegetation plays an important role in influencing the functioning and services of ecosystems. Satellite-derived metrics of land surface phenology have been widely used to assess the patterns in land cover dynamics. There are still limited satellite-based datasets of global land surface phenology available to date due to uneven qualities of satellite data. Here, we developed a global annual land surface phenology dataset based on Advanced Very High Resolution Radiometer (AVHRR) data from 1982 to 2018. The number of global vegetation growing seasons for each individual pixel was counted for each year from 1982 to 2018. We implemented four phenology retrieval methods, including the amplitude threshold method, the second-order derivative method, the third-order derivative method, and the curvature change rate method, to retrieve four key phenological metrics for each vegetation growth cycle, including the start of the growing season (SOS), maturity, senescence, and the end of the growing season (EOS). The results show that the phenological metrics retrieved using different phenology retrieval methods have similar patterns of global spatial distribution. From 1982 to 2018, the timing of both maturity and senescence within a year advanced in the Northern Hemisphere and the timing of SOS delayed in the Southern Hemisphere. The phenological metrics extracted from in-situ data such as the USA National Phenology Network (USA-NPN), the Pan European Phenology Database (PEP725), and flux tower measurements were positively correlated with satellite-derived phenological metrics. Despite the effects of mismatching spatial scales, the retrieved phenological metrics based on 5 km AVHRR data were consistent with those based on 500 m Moderate-resolution Imaging Spectroradiometer (MODIS) data and 30 m fused satellite data at both site-scale and regional-scale. The developed dataset released for usages and applications on global land surface dynamics offers potential end users options for choosing desirable phenological metrics derived from different methods.

Emission of Terpenoid Compounds from Rice Plants

The global cultivation area of rice is equivalent to 4% of the world’s forest area and may be an important sink and source of trace gases. To produce a precise terpenoid emission inventory, it is essential to obtain reliable data of terpenoid emission from rice plants. In the present study, terpenoid emissions from various rice species were measured using flow-through chamber and tower flux measurement methods. In the flow-through chamber measurement, linalool was emitted from the above-ground parts of the three rice cultivars “Koshihikari”, “Nipponbare” and “IR72”. The emission rates gradually decreased (<0.1 µg gDW−1 h−1) within two days during the measurement periods. As the touching stimulus might have enhanced linalool emission, a non-contact measurement method, i.e., the tower flux measurement method, was applied to a “Koshihikari” paddy. Linalool was not detected, but α-pinene was detected in the atmosphere above the rice paddy. The α-pinene flux (mean ± 95% confidence interval) was 0.006 ± 0.004 nmol m−2 s−1 on a land-area basis. The flux was 1/200 of the previously reported monoterpene emission rate of the rice plants measured in a commercial chamber, but was not largely different from three other reports. We provide terpenoid flux data above a rice paddy for the first time, which is more reliable because the tower flux measurement method can avoid stimuli to rice leaves and stems. Although the obtained terpenoid emission rate is very low, the obtained values can contribute to the establishment of a precise BVOC inventory in Asia.

Land surface phenology indicators retrieved across diverse ecosystems using a modified threshold algorithm

Land surface phenology (LSP), the study of the seasonal vegetation dynamics from remote sensing imagery, provides crucial information for plant monitoring and reflects the responses of ecosystems to climate change. The Moderate Resolution Imaging Spectroradiometer (MODIS) phenology product (MCD12Q2) provides global LSP information, but it has large spatial gaps in many regions, especially in ecosystems where rainfall influences phenology more than temperature. This study aimed to improve spatial coverage of LSP retrieval in these ecosystems. To do so, we used a regionally modified threshold algorithm for LSP retrievals, which were tested over continental Australia as it includes diverse landscapes of arid, mesic, and forest environments. We generated LSP metrics annually from 2003 to 2018 using satellite Enhanced Vegetation Index (EVI) time series at 500 m resolution, including the start, peak, end, and length of growing seasons, the minimum EVI value prior to and after the peak date, the seasonal maximum EVI value, the integral EVI value during the growing season (an approximation of productivity), and seasonal amplitude (maximum EVI value minus minimum EVI). Our regionally optimised algorithm improved the spatial coverage of LSP information in Australia from only 26 % of the continent to 70 % averaged across 16 years. Our results showed that the growing season amplitude was low (EVI < 0.1) over arid/semi-arid shrublands and savannas, tropical and subtropical savannas, and temperate evergreen forests, whose LSP metrics were captured by our regional algorithm and not by the global product. Some ecosystems, such as arid/semi-arid shrublands and savannas, showed more irregular phenology with low seasonal dynamics, and the growing seasons could skip a year or occur more than once in a year depending on climate conditions. Our algorithm was more sensitive to ecosystems with low seasonal amplitudes. We found that the detectability of LSP increases as the growing season amplitude increases, regardless of vegetation cover. Evaluation of the LSP metrics using eddy covariance flux tower measurements of gross primary productivity (GPP) demonstrated the reliability and accuracy of the algorithm. These improved LSP retrievals provide a greater understanding of the vegetation phenology across diverse ecosystems, especially savanna, shrubland, and evergreen forest ecosystems that cover more than 30 % of the land globally. The LSP provides essential information for ecological and agricultural studies such as quantifying bushfire fuel accumulation and forest carbon cycling, whilst enhancing our capacity for quantifying ecological responses to climate change.

Triple collocation-based multi-source evaporation and transpiration merging

The main objective of this study is to combine three reanalysis-based evaporation (E) and transpiration (T) estimates (i.e., ERA5-Land, GLDAS, and MERRA2) to improve the accuracy of evapotranspiration (ET) over East Asia. Here, we calculate the uncertainty of E and T separately through triple collocation (TC), which then used as a weighting factor to produce merged estimates of E and T. The combination of the two was quantitatively evaluated by comparing it with ground-based measurements. Before assessing the merged ET estimates, the error standard deviation (σmean) of TC was analyzed. ERA5-Land showed the smallest σmean for E for most of the study region, although MERRA2 achieved superior performance for T compared with ERA5-Land. This behavior further resulted in the highest weighting factors of ERA5-Land (59%) and MERRA2 (49%) for E and T, respectively. An evaluation of merged ET estimates by comparison with flux tower measurements revealed the close agreement of the two datasets in crop and forest regions. For croplands, the merged ET estimates showed better statistics compared with the two worst original products. However, the merged ET showed limitation in reflecting crop rotation due to the uncertainty of ERA5-Land E, which yielded the largest weighting factor over cropland. At forest sites, merged ET estimates yielded better statistical performance than the three individual ET products as well as the ET estimated through TC without separating E and T. Detailed analysis of the ratio of T and ET revealed that the merged ET was similar in magnitude to ground-based measurements. Spatial distribution of annual and seasonal ET indicated that the overall improvement of merged ET resulted from superior estimates of T during the spring and summer. These results confirm that separate error characterization of E and T through TC can enhance the accuracy of terrestrial ET estimates.

Limited impact of vapor pressure deficit on rainfed maize evapotranspiration, CO2 flux, and canopy temperature

AbstractRecent studies have suggested that increasing growing season vapor pressure deficit (vpd) will result in greater crop water use and more frequent water stress in rainfed maize (Zea mays L.). In this 3‐year study, we used flux tower and biometeorological measurements to examine the impact of diurnal variability of vpd and other climate parameters on latent heat (LE), sensible heat (H) and CO2 fluxes, and canopy temperature in field‐grown maize at non‐stress (high) and stress (low) volumetric soil water contents (SWC). Increases in vpd impacted LE primarily through midday when both net radiation (Rn) and vpd are increasing but vpd can still be low enough (&lt;1,800 Pa) so that not all Rn is being dissipated as LE. At high SWC, increases in LE at maximum daily vpd (1500–1700 h) were limited because Rn was decreasing at that time. Increases in LE above Rn at maximum vpd depended on H flux toward the canopy, which occurred late in the day and was generally low (&lt;50 W m−2). Vpds above 2000 Pa were only recorded at low SWC where CO2 flux was reduced and canopy temperature was greater than air temperature, indicating reduced canopy conductance. However, similar LE occurred at both low and high SWC due to higher potential evapotranspiration at low SWC. Our data suggest that the impact of possible increases in growing season vpd on LE may be limited in rainfed maize.

Thermodynamic driving mechanisms for the formation of global precipitation extremes and ecohydrological effects

Global warming has altered the thermodynamic and dynamic environments of climate systems, affecting the biogeochemical processes between the geosphere and atmosphere, which has significant impacts on precipitation extremes and the terrestrial carbon budget of ecosystems. Existing studies have reported a hook structure for precipitation extreme-temperature relationships but have rarely examined the underlying physical mechanisms. Previous studies have also failed to quantify the impact of precipitation on ecosystem productivity, hindering the assessment of future extreme climatic hazards and potential ecosystem risks. To reveal the thermodynamic driving mechanisms for the formation of global precipitation extremes and ecohydrological effects, this study utilizes over ten multisource datasets (i.e., satellite, reanalysis, climate model, land surface model, machine learning reconstruction, and flux tower measurements). We first assess the response of water-heat-carbon flux to precipitation extremes and explain the underlying physical mechanisms behind the hook structures in terms of atmospheric thermodynamics and dynamics. Based on outputs from five global climate models (GCMs) under ISIMIP3b, we project future changes in the hook structures as well as their impacts on precipitation extremes. Finally, we discuss the impact of precipitation on the terrestrial carbon budget by using outputs from the CLM4.5 model. The results show that precipitation extremes are usually accompanied by strong exchanges of water and heat and demonstrate a nonlinear relationship between precipitation and ecosystem productivity. The intensity (duration) of extreme precipitation is intensifying (decreasing) over most areas of the globe, whereas three-dimensional precipitation events are becoming more concentrated. Atmospheric dynamics play a key role in shaping the hook structure. The structure is not stable; it shifts under climate change and is projected to result in a 10–10% intensification in precipitation by the end of this century. Moderate levels of precipitation contribute to carbon assimilation in ecosystems, and the response of the carbon budget to precipitation is relatively stable under climate change.

Sun-induced fluorescence as a proxy for primary productivity across vegetation types and climates

Abstract. Sun-induced chlorophyll a fluorescence (SIF) retrieved from satellites has shown potential as a remote sensing proxy for gross primary productivity (GPP). However, to fully exploit the potential of this signal, the robustness and stability of the SIF–GPP relationship across vegetation types and climates must be assessed. For this purpose, current studies have been limited by the availability of SIF datasets with sufficient spatial resolution to disentangle the signal between different vegetation cover types. To overcome this limitation, this analysis uses GOME-2 (Global Ozone Monitoring Experiment-2) SIF retrievals, downscaled to a resolution of 0.05∘ (∼5 km) to explore the relationship between SIF and FLUXCOM GPP (GPPFX), a data-driven dataset of primary productivity obtained by upscaling flux-tower measurements. The high resolution of the downscaled SIF (SIFDS) dataset allows the relationships to be broken down by vegetation cover for separate climate zones, thus enabling a confrontation between GPP and SIF at fine granularity. This analysis first investigates the spatial and temporal relationships between FLUXCOM GPP and downscaled SIF at a global scale. A reasonably strong linear relationship is generally observed between SIFDS and GPPFX in all vegetation categories, and an analysis of covariance (ANCOVA) shows that the spatial response is similar between certain plant traits, with some distinction between herbaceous and woody vegetation and notable exceptions, such as equatorial broadleaf forests. Geographical regions of non-linearity suggest where SIFDS could potentially provide information about ecosystem dynamics that are not represented in the FLUXCOM GPP dataset. With the demonstration of downscaled SIF as a proxy for GPP, the response of SIFDS to short-term fluctuations in several meteorological variables is analysed and the most significant short-term environmental driving and limiting meteorological variables determined. Vegetation groupings of similar SIF–meteorological response reinforce the vegetation categorisations suggested by the ANCOVA. This comparative exploration of two of the most recent products in carbon productivity estimation shows the value in downscaling SIF data, provides an independent probe of the FLUXCOM GPP model, enhances our understanding of the global SIF–GPP spatio-temporal relationship with a particular focus on the role of vegetation cover, and explores the similarity of the SIF and GPP responses to meteorological fluctuations. Additional analyses with alternative SIF and GPP datasets support these conclusions.

Understanding the impact of vegetation dynamics on the water cycle in the Noah-MP model

The impact of extreme climate events, especially prolonged drought, on ecosystem response, can influence the land-atmosphere interactions and modify local and regional weather and climate. To investigate the impact of vegetation dynamics on the simulation of energy, water, and carbon exchange at the land surface and streamflow, especially during drought conditions, we compared the performance of multiple versions of the Noah- multiparameterization (MP) land surface model (both Noah-MP LSM, version 3.6 and 4.0.1) with default configurations as well as various vegetation physics options, including the dynamic or input leaf area index (LAI) and the fractional vegetated area (FVEG). At the site level, simulated water and energy fluxes from each version were compared to eddy covariance (EC) flux tower measurements and remote sensing data from Moderate-Resolution Imaging Spectroradiometer (MODIS) at well-characterized natural grassland sites in Kansas from 2008 to 2018. The ability of each version to reproduce annual mean river flows was compared to gauged observations at United States Geological Survey (USGS) stations over 11 years (2008–2018). Model performance in replicating spatial patterns during extreme events was assessed by comparing simulated soil moisture (SM) percentiles over the state of Kansas to the U.S. Drought Monitor (USDM). Results from these comparisons indicate that (a) even though there were differences in the latent heat (LE) components (i.e., transpiration, canopy evaporation, and soil evaporation), the total LE is mostly insensitive to variations in LAI across all model versions. This indicates that the incoming net radiation limits the total evaporation, as the presence of adequate soil moisture allows for higher soil evaporation when LAI limits transpiration; (b) regardless of the model version, the force of the precipitation largely dictates the accuracy of evapotranspiration (ET) simulation; (c) Overestimation of LE resulted in underestimation of streamflow, particularly over the land surface type dominated by a combination of grasses and cropland in the western and central part of the state; (d) all of the tested Noah-MP 4.0.1 vegetation physics produced spatial patterns of drought that more closely matched the USDM as compared to version 3.6. These findings have important relevance for applications of large-scale ecosystem-atmosphere feedbacks in water, carbon, and energy exchange.

Seasonal Ecosystem Productivity in a Seasonally Dry Tropical Forest (Caatinga) Using Flux Tower Measurements and Remote Sensing Data

The Caatinga dry forest encompasses 11% of the total continental territory of Brazil. Nevertheless, most research on the relationship between phenology and ecosystem productivity of Brazilian tropical forests is aimed at the Amazon basin. Thus, in this study we evaluated the seasonality of ecosystem productivity (gross primary production—GPP) in a preserved Caatinga environment in northeast Brazil. Analyses were carried out using eddy covariance measurements and satellite-derived data from sensor MODIS (MODerate Resolution Imaging Spectroradiometer, MOD17 and MOD13 products). In addition to GPP, we investigated water use efficiency (WUE) and meteorological and phenological aspects through remotely sensed vegetation indices (NDVI and EVI). We verified that ecosystem productivity is limited mainly by evapotranspiration, with maximum GPP values registered in the wetter months, indicating a strong dependency on water availability. NDVI and EVI were positively associated with GPP (r = 0.69 and 0.81, respectively), suggesting a coupling between the emergence of new leaves and the phenology of local photosynthetic capacity. WUE, on the other hand, was strongly controlled by consecutive dry days and not necessarily by total precipitation amount. The vegetation indices adequately described interannual variations of the forest response to environmental factors, and GPP MODIS presented a good relationship with tower-measured GPP in dry (R2 = 0.76) and wet (R2 = 0.62) periods.

Disentangling Soil, Shade, and Tree Canopy Contributions to Mixed Satellite Vegetation Indices in a Sparse Dry Forest

Remote sensing (RS) for vegetation monitoring can involve mixed pixels with contributions from vegetation and background surfaces, causing biases in signals and their interpretations, especially in low-density forests. In a case study in the semi-arid Yatir forest in Israel, we observed a mismatch between satellite (Landsat 8 surface product) and tower-based (Skye sensor) multispectral data and contrasting seasonal cycles in near-infrared (NIR) reflectance. We tested the hypothesis that this mismatch was due to the different fractional contributions of the various surface components and their unique reflectance. Employing an unmanned aerial vehicle (UAV), we obtained high-resolution multispectral images over selected forest plots and estimated the fraction, reflectance, and seasonal cycle of the three main surface components (canopy, shade, and sunlit soil). We determined that the Landsat 8 data were dominated by soil signals (70%), while the tower-based data were dominated by canopy signals (95%). We then developed a procedure to resolve the canopy (i.e., tree foliage) normalized difference vegetation index (NDVI) from the mixed satellite data. The retrieved and corrected canopy-only data resolved the original mismatch and indicated that the spatial variations in Landsat 8 NDVI were due to differences in stand density, while the canopy-only NDVI was spatially uniform, providing confidence in the local flux tower measurements.