Fraction Of Photosynthetically Active Radiation Research Articles

Post-fire Pinus radiata invasion in a threatened biodiversity hotspot forest: A multi-scale remote sensing assessment

Biological invasions are one of the most relevant factors of biodiversity loss, especially after fire disturbances. Wildfires can accelerate invasions of fire-prone species, like Pinus radiata, and dramatically alter ecosystems. However, how to assess the main impacts of this invasion process on the composition, structure, and functionality of ecosystems, including the post-fire revegetation processes, has not been fully resolved. This study aimed to evaluate the impacts of P. radiata invasion on fire-damaged forest ecosystems using combined remote sensing and in situ data, focusing particularly on changes in biodiversity, ecosystem structure, and functionality. The recovery of forest leaf area index (LAI) and the fraction of photosynthetically active radiation (FPAR) were monitored using Sentinel-2 time series products. Then the pre- and post-fire native community composition and the relationships of invasion and biodiversity with biotic and abiotic components were characterized using structural equation modeling (SEM). The postfire P. radiata density was mapped to quantify invasion intensity in three burned native forest fragments using generalized additive modeling (GAM) regressions based on UAS multispectral data. Biophysical metrics indicate that all forest fragments impacted by high, medium-high, and medium-low severity fires achieved a partial recovery of their canopy. The SEM model showed that microtopographic features and vegetation height explain native species diversity under pre-fire conditions due to their close relationship with favorable microclimatic conditions for species establishment. Vegetation height determined the abundance of P. radiata in post-fire conditions, and it negatively impacted diversity by promoting the homogenization of vegetation cover and altering diversity patterns. The general composition and abundance metrics also showed a substantial modification associated with the heavy (significant) invasion of P. radiata species. Predictive mapping of P. radiata density showed high accuracies (R2 =0.73 and explained deviation of 80%). The maps depicted an intense concentration of the invasive tree with a mean density of 76,217 individuals per ha−1 and high invasion spots with more than 176,000 individuals per ha−1. The quantification of invasion and mapping is a fundamental input for prioritizing areas and resources for a large-scale restoration program, and is a priority to avoid the loss of these highly threatened forest ecosystems.

Shedding light on the increased carbon uptake by a boreal forest under diffuse solar radiation across multiple scales.

Solar radiation is scattered by cloud cover, aerosols and other particles in the atmosphere, all of which are affected by global changes. Furthermore, the diffuse fraction of solar radiation is increased by more frequent forest fires and likewise would be if climate interventions such as stratospheric aerosol injection were adopted. Forest ecosystem studies predict that an increase in diffuse radiation would result in higher productivity, but ecophysiological data are required to identify the processes responsible within the forest canopy. In our study, the response of a boreal forest to direct, diffuse and heterogeneous solar radiation conditions was examined during the daytime in the growing season to determine how carbon uptake is affected by radiation conditions at different scales. A 10-year data set of ecosystem, shoot and forest floor vegetation carbon and water-flux data was examined. Ecosystem-level carbon assimilation was higher under diffuse radiation conditions in comparison with direct radiation conditions at equivalent total photosynthetically active radiation (PAR). This was driven by both an increase in shoot and forest floor vegetation photosynthetic rate. Most notably, ecosystem-scale productivity was strongly related to the absolute amount of diffuse PAR, since it integrates both changes in total PAR and diffuse fraction. This finding provides a gateway to explore the processes by which absolute diffuse PAR enhances productivity, and the long-term persistence of this effect under scenarios of higher global diffuse radiation.

Light‐use efficiency for coral reef communities and benthic functional types

AbstractCoral reef metabolism is dominated by benthic photoautotrophic communities that comprise varying combinations of algae, coral, and sand. Rates of daily gross primary production (GPP) for these benthic functional types (BFTs) are remarkably consistent across biogeographical regions, supporting the idea that reefs exhibit modal metabolism. Most variability in reported rates likely arises from differences in light availability. In fact, GPP is a linear function of incident photosynthetically active radiation (PAR), the fraction of PAR absorbed (fAPAR) by photoautotrophic organisms or communities, and light‐use efficiency (ε), which parameterizes photosynthesizers' biochemical capacity for CO2 fixation: GPP = ε × fAPAR × PAR. On time scales of days to weeks, fAPAR and ε are far more stable than PAR. ε is a critical parameter, because it represents productive response integrated across all environmental conditions, other than light. If BFTs exhibit consistent GPP across wide geographic ranges, then their εs must also be consistent. The aim of this study was to estimate ε for algae, coral, and sand. Using data collected during NASA's CORAL mission in 2016–2017, ε was calculated for 32 mixed communities at Lizard Island, Australia (10); Kāne'ohe Bay, Hawai'i (8); Guam (6); and Palau (8). Nonnegative least squares was used to solve for ε of each BFT, producing values of 0.038, 0.060, and 0.016 C photon−1 for algae, coral, and sand, respectively. These values can be used in light‐driven models of reef metabolism. Further work is necessary to refine these estimates and, importantly, to explore how ε is affected by environmental conditions.

Sensor-independent LAI/FPAR CDR: reconstructing a global sensor-independent climate data record of MODIS and VIIRS LAI/FPAR from 2000 to 2022

Abstract. Leaf area index (LAI) and fraction of photosynthetically active radiation (FPAR) are critical biophysical parameters for the characterization of terrestrial ecosystems. Long-term global LAI/FPAR products, such as the moderate resolution imaging spectroradiometer (MODIS) and the Visible Infrared Imaging Radiometer Suite (VIIRS), provide the fundamental dataset for accessing vegetation dynamics and studying climate change. However, existing global LAI/FPAR products suffer from several limitations, including spatial–temporal inconsistencies and accuracy issues. Considering these limitations, this study develops a sensor-independent (SI) LAI/FPAR climate data record (CDR) based on Terra-MODIS/Aqua-MODIS/VIIRS LAI/FPAR standard products. The SI LAI/FPAR CDR covers the period from 2000 to 2022, at spatial resolutions of 500 m/5 km/0.05∘, 8 d/bimonthly temporal frequencies and available in sinusoidal and WGS1984 projections. The methodology includes (i) comprehensive analyses of sensor-specific quality assessment variables to select high-quality retrievals, (ii) application of the spatial–temporal tensor (ST-tensor) completion model to extrapolate LAI and FPAR beyond areas with high-quality retrievals, (iii) generation of SI LAI/FPAR CDR in various projections and various spatial and temporal resolutions, and (iv) evaluation of the CDR by direct comparisons with ground data and indirectly through reproducing results of LAI/FPAR trends documented in the literature. This paper provides a comprehensive analysis of each step involved in the generation of the SI LAI/FPAR CDR, as well as evaluation of the ST-tensor completion model. Comparisons of SI LAI (FPAR) CDR with ground truth data suggest an RMSE of 0.84 LAI (0.15 FPAR) units with R2 of 0.72 (0.79), which outperform the standard Terra/Aqua/VIIRS LAI (FPAR) products. The SI LAI/FPAR CDR is characterized by a low time series stability (TSS) value, suggesting a more stable and less noisy dataset than sensor-dependent counterparts. Furthermore, the mean absolute error (MAE) of the CDR is also lower, suggesting that SI LAI/FPAR CDR is comparable in accuracy to high-quality retrievals. LAI/FPAR trend analyses based on the SI LAI/FPAR CDR agree with previous studies, which indirectly provides enhanced capabilities to utilize this CDR for studying vegetation dynamics and climate change. Overall, the integration of multiple satellite data sources and the use of advanced gap filling modeling techniques improve the accuracy of the SI LAI/FPAR CDR, ensuring the reliability of long-term vegetation studies, global carbon cycle modeling, and land policy development for informed decision-making and sustainable environmental management. The SI LAI/FPAR CDR is open access and available under a Creative Commons Attribution 4.0 License at https://doi.org/10.5281/zenodo.8076540 (Pu et al., 2023a).

Light blocking film in a glasshouse impacts Capsicum annuum L. yield differentially across planting season.

High energy costs are a barrier to producing high-quality produce at protected cropping facilities. A potential solution to mitigate high energy costs is film technology, which blocks heat-producing radiation; however, the alteration of the light environment by these films may impact crop yield and quality. Previous studies have assessed the impact of ULR 80 [i.e., light-blocking film (LBF)] on crop yield and photosynthetically active radiation (PAR); however, an assessment of the spectral environment over different seasons is important to understand potential crop impacts through different developmental phases. In this study, two varieties (red and orange) of Capsicum annuum were grown across two crop cycles: one cycle with primary crop growth in the autumn (i.e., autumn experiment [AE]) and the other with primary crop growth in the summer (i.e., summer experiment [SE]). LBF reduced PAR (roof level: 26%-30%, plant canopy level: 8%-25%) and net radiation (36%-66%). LBF also reduced total diffuse PAR (AE: 8%, SE: 15%), but the diffuse fraction of PAR increased by 7% and 9% for AE and SE, respectively, potentially resulting in differential light penetration throughout the canopy across treatments. LBF reduced near-infrared radiation (700 nm-2,500 nm), including far-red (700 nm-780 nm) at mid- and lower-canopy levels. LBF significantly altered light quantity and quality, which determined the amount of time that the crop grew under light-limited (<12mol m-2 d-1) versus sufficient light conditions. In AE, crops were established and grown under light-limited conditions for 57% of the growing season, whereas in SE, crops were established and grown under sufficient light conditions for 66% of the growing season. Overall, LBF significantly reduced the yield in SE for both varieties (red: 29%; orange: 16%), but not in AE. The light changes in different seasons in response to LBF suggest that planting time is crucial for maximizing fruit yield when grown under a film that reduces light quantity. LBF may be unsuitable for year-round production of capsicum, and additional development of LBF is required for the film to be beneficial for saving energy during production and sustaining good crop yields in protected cropping.

Analysis of Vegetation Carbon Stock Using Remote Sensing In Penggaron Forest, Central Java Province

Global warming is a climate problem due to the greenhouse effect so it must be anticipated from external factors in the form of carbon stocks from forest vegetation. Forest areas have an important role in maintaining carbon reserves so that they become a pillar of controlling carbon emissions, one of which is the Penggaron Forest in Ungaran City. The Penggaron Forest needs to be studied regarding estimates of carbon stock abundance so that the research carries out estimates using remote sensing data. The method in this research uses light use efficiency (LUE). The concept of this method estimates vegetation biomass based on 3 parameters such as the Normalized Difference Vegetation Index (NDVI) value to model the fraction of photosynthetic active radiation (fPAR), maximum efficiency of using sunlight, and photosynthetic active radiation (PAR). This research resulted in an estimate of the total carbon stock content in the Penggaron Forest of 3,049,515.14 KgC with a forest area of 9,183 km2. 46,921 cm. Accuracy testing was carried out for carbon stock modeling which obtained an RMSE value of 97.289 KgC. The carbon stock modeling estimation was carried out by the Pearson correlation test which obtained a correlation result of 0.0974.

Biophysical and biochemical features’ feedback associated with a flood episode in a tropical river basin model

Global climate change scenarios such as frequent and extreme floods disturb the river basins by destructing the vegetation resulting in rehabilitation procedures being more costly. Thus, understanding the recovery and regeneration of vegetation followed by extreme flood events is critical for a successful rehabilitation process. Spatial and temporal variation of biochemical and biophysical features derived from remote sensing technology in vegetation can be incorporatedto understand the recovery and regeneration of vegetation. The present study explores the flood impact on vegetation caused by major river basins in Sri Lanka (a model tropical river basin) by comparing pre-flood and post-flood cases. The study utilized enhanced vegetation index (EVI), normalized difference vegetation index (NDVI), the fraction of photosynthetically active radiation (FPAR), and gross primary productivity (GPP) of the Moderate Resolution Imaging Spectroradiometer (MODIS) platform. A remarkable decline in EVI, LAI, FPAR, GPP, and vegetation condition index was observed in the post-flood case. Notably, coupled GPP-EVI and GPP-LAI portrayed dependency of features and showed a significant impact triggered by the flood episode by narrowing the feature in post-flood events. EVI depicted the highest regeneration (0.333) while GPP presented the lowest regeneration (0.093) after the flood event. Further, it was revealed that 1.18years have been on the regeneration. The regeneration of GPP and LAI remained low comparatively justifying the magnitude and impact of the flood event. The study revealed successful implications of vegetation indices on flood basin management of small to large tropical river basins.

A deep learning framework combining CNN and GRU for improving wheat yield estimates using time series remotely sensed multi-variables

Accurate and timely crop yield estimation is crucial for crop market planning and food security. Combining remotely sensed big data with deep learning for yield estimation has attracted extensive attention. However, it is still challenging to understand and quantify the time cumulative effects of crop growth over time for crop yield estimation. In this study, we combined the powerful feature extraction capability of the convolutional neural network (CNN) and the advantage of time series memory of the gated recurrent unit (GRU) network to develop a novel deep learning model called CNN-GRU for estimating county-level winter wheat yields in the Guanzhong Plain using three remotely sensed variables, vegetation temperature condition index (VTCI), leaf area index (LAI) and fraction of photosynthetically active radiation (FPAR). The CNN-GRU model was able to extract features related to yield from the input variables and the accuracy of the proposed model (R2 = 0.64, RMSE = 462.56 kg/ha, MRE = 8.90 %) was higher than that of the GRU model (R2 = 0.62, RMSE = 479.79 kg/ha, MRE = 9.34 %), and the CNN-GRU model’s reliability and robustness were confirmed by applying the leave-one-year-out cross-validation. Furthermore, we applied the proposed CNN-GRU model to simulate the wheat yields in the Plain pixel by pixel and examined the spatiotemporal patterns of the estimated yields. The distribution of yields presented the spatial characteristics of low yields in the east and high yields in the west, and the inter-annual variation characteristics of overall stability and steady increase. Additionally, we explored the possibility of timely prediction of winter wheat yield and the contribution of the multi-variables at different growth stages to yield estimation based on the ability of deep learning to reveal cumulative effects and non-linear relationships between influencing factors and yield. It was found that the information reflected by the multi-variables from late March to late April was important for yield estimation and the best prediction could be achieved approximately 20 days before the harvest of winter wheat. Our study demonstrated that combining CNN and GRU was an efficient and promising approach to improve yield estimation, offering great promise for global crop yield estimation.

Investigating the roles of different extracted parameters from satellite images in improving the accuracy of daily reference evapotranspiration estimation

Agricultural water management, crop modeling, and irrigation scheduling are all dependent on the accurate estimation of reference evapotranspiration (ET0). A satellite image can also compensate for the lack of reliable weather information. So, in this study, stochastic gradient descent (SGD) has been implemented for optimizing multilayer perceptron (MLP) and developing SGD-MLP to estimate daily ET0 in Tabriz (semi-arid climate) and Babolsar (humid climate) stations, Iran, using extracted data from satellite images. The estimated ET0 values were compared to the determined ET0 based on the FAO-Penman–Monteith equation. Based on satellite image data collected from 2003 to 2021, the database was constructed. During the development of the abovementioned models, data from 2003 to 2016 (70%) were used for training purposes, and residual data (30%) were used for testing purposes. Additionally, the input variables, including land surface temperature (LST) day and night, normalized difference vegetation index (NDVI), leaf area index (LAI), and a fraction of photosynthetically active radiation (FPAR) from MODIS sensor, were utilized to estimate the daily ET0. Thus, there are three studied models; first is based on the LST, second on the vegetation indices, and third on the combination of the LST and the vegetation indices. Additionally, four performance indexes, including the coefficient of determination (R2), the root-mean-square error (RMSE), Willmott’s index of agreement (WI), and Nash–Sutcliffe efficiency, were utilized in order to measure the implemented model’s accuracy. According to the obtained results, the SGD-MLP-3 with input parameters of LSTday&night, LSTmean, LAI, NDVI, and FPAR gave the most accurate results with RMSE and WI values of as 0.417 mm/day, 0.973, for Tabriz and 0.754 mm/day, 0.922 for Babolsar stations, respectively. Conclusively, LST of daytime, nighttime, and average may be suggested as the most influential parameter for ET0 estimation.

Spectral mixture analysis of AVIRIS-NG data for grouping plant functional types

In a recent scenario, the prediction of environmental changes through plant functional types can aid in simplification of ecosystem processes. The present study attempts to identify and map plant functional types (PFTs) in the AVIRIS-NG campaign site, namely Shoolpaneshwar Wildlife Sanctuary (site id 67), using AVIRIS-NG data combined with spectral mixture analysis that accounts for endmember variability. Due to the occurrence of heterogeneous vegetation in the selected AVIRIS-NG site, the measured spectral signal for every pixel of surface reflectance data will be the outcome of fractions in which various plant functional types and the soil background exist. The interest of the present research lies in these fractions; hence spectral mixture analysis was applied. Ground truthing was carried out simultaneous to AVIRIS-NG flight pass. Six plant functional traits, namely Diameter at breast height (DBH), Height, biomass, leaf chlorophyll content (CC), Fraction of Photosynthetic Active Radiation (FPAR), and Leaf Area Index (LAI), were measured for twenty-three tree species found in the study site which were further used for plant functional grouping by applying k-means clustering algorithm. Trait-based cluster analysis classified the tree species into four plant functional types. Endmember selection from AVIRIS-NG image for these plant functional types was done using a manual field observation-based approach, which was used as input in spectral mixture analysis. The final product of the analysis is a set of fractional abundance images for each plant functional type. Considerable accuracy was obtained on validating the fractional abundance image by in situ data. The study highlighted the potential of a spectral mixture analysis classifier in identifying and mapping different plant functional types using AVIRIS-NG data when performed using an appropriate number of end members.

Environmental controls on the light use efficiency of terrestrial gross primary production.

Gross primary production (GPP) by terrestrial ecosystems is a key quantity in the global carbon cycle. The instantaneous controls of leaf-level photosynthesis are well established, but there is still no consensus on the mechanisms by which canopy-level GPP depends on spatial and temporal variation in the environment. The standard model of photosynthesis provides a robust mechanistic representation for C3 species; however, additional assumptions are required to "scale up" from leaf to canopy. As a consequence, competing models make inconsistent predictions about how GPP will respond to continuing environmental change. This problem is addressed here by means of an empirical analysis of the light use efficiency (LUE) of GPP inferred from eddy covariance carbon dioxide flux measurements, in situ measurements of photosynthetically active radiation (PAR), and remotely sensed estimates of the fraction of PAR (fAPAR) absorbed by the vegetation canopy. Focusing on LUE allows potential drivers of GPP to be separated from its overriding dependence on light. GPP data from over 100 sites, collated over 20 years and located in a range of biomes and climate zones, were extracted from the FLUXNET2015 database and combined with remotely sensed fAPAR data to estimate daily LUE. Daytime air temperature, vapor pressure deficit, diffuse fraction of solar radiation, and soil moisture were shown to be salient predictors of LUE in a generalized linear mixed-effects model. The same model design was fitted to site-based LUE estimates generated by 16 terrestrial ecosystem models. The published models showed wide variation in the shape, the strength, and even the sign of the environmental effects on modeled LUE. These findings highlight important model deficiencies and suggest a need to progress beyond simple "goodness of fit" comparisons of inferred and predicted carbon fluxes toward an approach focused on the functional responses of the underlying dependencies.

Downscaling solar-induced chlorophyll fluorescence for field-scale cotton yield estimation by a two-step convolutional neural network

As the largest cotton-growing region in China, Xinjiang has contributed more than 80% of the total national cotton production in recent years. Timely and accurate estimation of cotton yield in Xinjiang is important for sustainable agricultural development and food security. However, most current studies have been devoted to the linkage of crop yield with remotely sensed reflectance and climate parameters. This has caused numerous uncertainties due to that these explanatory variables are unable to quickly reflect the actual photosynthetic dynamics of crops. Solar-induced chlorophyll fluorescence (SIF), as a direct proxy of plant photosynthesis (gross primary productivity, GPP), has recently been suggested to be a promising method for crop yield estimation, but the spatial resolution of current SIF products derived from satellites is usually very low (such as Global OCO-2 SIF (GOSIF): 0.05°). This greatly limited the ability of SIF to accurately estimate field-scale cotton yield in Xinjiang. Here, we first proposed a two-step convolutional neural network (CNN) strategy to downscale the monthly GOSIF products sequentially from 0.05°, 0.005° to 0.0005° to match the size of cotton field parcels, and then linear regression and random forest (RF) regression were respectively conducted using the monthly downscaled SIF product (CNN-SIF) to assess its feasibility to estimate field-scale yield. Results showed that the proposed stepwise approach for downscaling GOSIF worked well, indicating a high goodness of fit (R2 > 0.85) with the referenced SIF as well as strong correlations to both GPP products and fraction of photosynthetically active radiation (FPAR) (the median r > 0.90). On this basis, preferable accuracies (the optimal R2 = 0.62 and the ratio of prediction to deviation = 1.64) were also achieved for our proposed cotton yield estimation models in the Mosuowan region, Xinjiang only by the 0.0005° SIF products. With the assistance of NDVI (normalized difference vegetation index), the higher performance was given (R2 = 0.67 and RPD = 1.72). This study reveals the importance of finer-resolution SIF products for accurate crop yield estimation and offers a promising and practical approach for estimating agricultural yield, especially for fragmented farmlands.

Calibration of Co-Located Identical PAR Sensors Using Wireless Sensor Networks and Characterization of the In Situ fPAR Variability in a Tropical Dry Forest

The fraction of photosynthetic active radiation (fPAR) attempts to quantify the amount of enery that is absorbed by vegetation for use in photosynthesis. Despite the importance of fPAR, there has been little research into how fPAR may change with biome and latitude, or the extent and number of ground networks required to validate satellite products. This study provides the first attempt to quantify the variability and uncertainties related to in-situ 2-flux fPAR estimation within a tropical dry forest (TDF) via co-located sensors. Using the wireless sensor network (WSN) at the Santa Rosa National Park Environmental Monitoring Super Site (Guanacaste, Costa Rica), this study analyzes the 2-flux fPAR response to seasonal, environmental, and meteorological influences over a period of five years (2013–2017). Using statistical tests on the distribution of fPAR measurements throughout the days and seasons based on the sky condition, solar zenith angle, and wind-speed, we determine which conditions reduce variability, and their relative impact on in-situ fPAR estimation. Additionally, using a generalized linear mixed effects model, we determine the relative impact of the factors above, as well as soil moisture on the prediction of fPAR. Our findings suggest that broadleaf deciduous forests, diffuse light conditions, and low wind patterns reduce variability in fPAR, whereas higher winds and direct sunlight increase variability between co-located sensors. The co-located sensors used in this study were found to agree within uncertanties; however, this uncertainty is dominated by the sensor drift term, requiring routine recalibration of the sensor to remain within a defined criteria. We found that for the Apogee SQ-110 sensor using the manufacturer calibration, recalibration around every 4 years is needed to ensure that it remains within the 10% global climate observation system (GCOS) requirement. We finally also find that soil moisture is a significant predictor of the distribution and magnitude of fPAR, and particularly impacts the onset of senescence for TDFs.

The effect of strip orientation and width on radiation interception in maize–soybean strip intercropping systems

AbstractMaize–soybean intercropping systems are widely used in developing countries, but few studies have been performed to optimize such systems. The effects of strip width and orientation on photosynthetically active radiation (PAR) distribution characteristics were therefore determined to gain a better understanding of crop productivity in maize–soybean intercropping systems. A series of intercropping experiments incorporating different strip orientations and widths were conducted. In addition, an improved radiation transmission model was used to simulate PAR interception by the maize and soybean crops. We found that more PAR was intercepted by the soybean plants than by the maize plants when the strip orientation angle increased from 0° to 90° in the maize–soybean intercropping systems. This result was obtained in most of the main maize production areas, including those with different latitudes. The effect of strip widths within the intercropping systems also had a significant effect on PAR interception. The relative fraction of PAR intercepted ranged from 39.4% to 86% for soybean and 105.8% to 169.3% for maize. The effect of soybean strip width on leaf PAR interception was greater than that of maize strip width. These results are not only of fundamental biological importance but they also have important implications for agriculture in terms of the optimization of production in intercropping systems.

Daily estimation of gross primary production under all sky using a light use efficiency model coupled with satellite passive microwave measurements

Although satellite-based light use efficiency (LUE) model is widely used to estimate gross primary production (GPP) of terrestrial ecosystems, microwave observations have not been integrated into LUE models. This study developed a new LUE model coupled with a passive microwave vegetation index (Emissivity Difference Vegetation Index, EDVI) for daily GPP estimation. Normalized EDVI (nEDVI), an indicator of canopy-scale leaf development and biomass change, was used as a proxy of fraction of photosynthetically active radiation (FPAR). EDVI-based evaporative fraction (EDVI-EF) under all sky was used to indicate synoptic-scale water stress on LUE. 8-year in-situ measurements from seven flux tower sites (four forests, two grasslands and one croplands) of ChinaFLUX network were used to evaluate the model. We found that nEDVI-based FPAR better captured the short-term variations in daily in-situ GPP (GPPobs) than other optic-based FPAR schemes. This capability of nEDVI was more noticeable under moderate and heavy cloud cover (Frc) conditions. Validations against daily GPPobs at all sites showed that EDVI-based GPP (GPPEDVI) generated an overall small bias of −0.47 gC m−2 day−1 (−8.1%) and good Taylor score (S) of 0.86 at the daily scale. Better accuracy of GPPEDVI was found at forests sites with R2 of 0.43 to 0.73, bias of-5.29% to 3.03% and S of 0.58 to 0.78, respectively. At a tropical forest site with most frequent cloud cover, the model also well captured the variation in GPPobs from clear sky to cloudy sky (R2 of 0.93) with stable accuracies. Furthermore, the accuracy of daily GPPEDVI was found to be comparable with global satellite optic MOD17 GPP (GPPMOD17) and EC-LUE GPP (GPPECLUE) from 8-day to yearly scales across the sites. In particular, GPPEDVI performed generally smaller bias at evergreen broadleaf forests, while both of GPPMOD17 and GPPECLUE were overestimated, suggesting that there could be less saturation for microwave-based LUE model over dense vegetation. Although all three satellite LUE models severely underestimated GPP of crop, GPPEDVI generated lower bias (−29.8%) than GPPMOD17 (−66.8%) and GPPECLUE (−59.5%). Overall, this study is the first attempt toward the integration of microwave-derived variables into LUE model for daily GPP estimation. The microwave-based LUE model has a potential of mapping spatiotemporally continues daily GPP under various clouds.

A Framework to Assess the Potential Uncertainties of Three FPAR Products

AbstractThe remote sensing products of the fraction of photosynthetically active radiation (FPAR) are widely used in large‐scale terrestrial vegetation condition monitoring and ecological modeling. However, the validation and evaluation of these products are still insufficient due to limited ground measurements. A framework is proposed in this study that does not rely on ground FPAR observations to evaluate the potential uncertainties of three FPAR data sets over a large scale. Two independent methods, including a statistical method named extended triple colocation (ETC) and FPAR‐based gross primary production (GPP) comparative evaluation at the site level, are integrated. The framework was applied to three widely used FPAR products, including GLASS (Global Land Surface Satellite), GEOV2 (version 2 of the COPERNICUS 1‐km product), and MODIS (Moderate Resolution Imaging Spectroradiometer), in China from 2001–2015. The root‐mean‐square error (RMSE) and correlation coefficient (R) based on the ETC method showed that at the national spatial scale, GEOV2‐FPAR had the highest precision (RMSEFPAR = 0.0367, RFPAR = 0.9012), followed by MODIS‐FPAR (RMSEFPAR = 0.0482, RFPAR = 0.8392) and GLASS‐FPAR (RMSEFPAR = 0.0536, RFPAR = 0.736). In particular, each FPAR data set was locally optimal over some regions. Using a light use efficiency model, GPP was calculated using these three FPAR data sets and other identical variables and parameters at 22 eddy flux sites. General coherent results were found in both GPP evaluation and site‐level ETC analysis of these three FPAR data sets. Therefore, we conclude that this framework is reliable and applicable in evaluating the potential uncertainties of FPAR.

Development of a Rapid Mangrove Zonation Mapping Workflow Using Sentinel 2-Derived Indices and Biophysical Dataset

Moderate to high resolution satellite imageries are commonly used in mapping mangrove cover from local to global scales. In addition to extent information, studies such as mangrove composition, ecology, and distribution analysis require further information on mangrove zonation. Mangrove zonation refers to unique sections within a mangrove forest being dominated by a similar family, genus, or species. This can be observed both in natural and planted mangrove forests. In this study, a mapping workflow was developed to detect zonation in test mangrove forest sites in Katunggan-It Ibajay (KII) Ecopark (Aklan), Bintuan (Coron), Bogtong, and Sagrada (Busuanga) in the Philippines and Fukido Mangrove Park (Ishigaki, Japan) using Sentinel-2 imagery. The methodology was then applied to generate a nationwide mangrove zonation map of the Philippines for year 2020. Combination of biophysical products, water, and vegetation indices were used as classification inputs including leaf area index (LAI), fractional vegetation cover (FVC), fraction of photosynthetically-active radiation (FAPAR), Canopy chlorophyll content (Cab), canopy water content (Cw), Normalized Difference Vegetation Index (NDVI), modified normalized difference water index (MNDWI), modified chlorophyll absorption in reflectance index (MCARI), and red-edge inflection point (REIP). Mangrove extents were first mapped using either the Maximum Likelihood Classification (MLC) algorithm or the Mangrove Vegetation Index (MVI)-based methodology. The biophysical and vegetation indices within these areas were stacked and transformed through Principal Component Analysis (PCA). Regions of Interest (ROIs) were selected on the PCA bands as training input to the MLC. Results show that mangrove zonation maps can highlight the major mangrove zones in the study sites, commonly limited up to genera level only except for genera with only one known species thriving in the area. Four zones were detected in KII Ecopark: Avicennia zone, Nypa zone, Avicennia mixed with Nypa zone, and mixed mangroves zones. For Coron and Busuanga, the mapped mangrove zones are mixed mangroves, Rhizophora zone and sparse/damaged zones. Three zones were detected in Fukido site: Rhizophora stylosa-dominant zone, Bruguiera gymnorrhiza-dominant zone, and mixed mangrove zones. The zonation maps were validated using field plot data and orthophotos generated from Unmanned Aerial System (UAS) surveys, with accuracies ranging from 75 to 100%.

Estimating aboveground net primary productivity of reforested trees in an urban landscape using biophysical variables and remotely sensed data

Recently, urban reforestation programs have emerged as potential carbon sinks and climate mitigates in urban landscapes. Thus, spatially explicit information on net primary productivity (NPP) of reforested trees in urban environments is central to understanding the value of reforestation initiatives in the global carbon budget and climate regulation potential. To date, numerous studies have mainly focused on natural and commercial forests NPP at a regional scale based on coarse spatial resolution remotely sensed data. Generally, local scale NPP studies based on fine spatial resolution data are limited. Therefore, this study sought to estimate aboveground NPP of an urban reforested landscape using biophysical and Sentinel-2 Multispectral Imager data derived variables. Using the MOD17 model, results showed that mean NPP ranged between 6.24 Mg C ha−1 with high coefficient of determination (R2: 0.92) and low RMSE (0.82 Mg ha−1) across all reforested trees within the study area. Results also showed a considerable variation in NPP among the reforested trees, with deciduous Acacia and Dalbergia obovate species showing the highest NPP (7.62 Mg C ha−1 and 7.58 Mg C ha-1, respectively), while the evergreen Syzygium cordatum and shrub Artemisia afra had the lowest NPP (4.54 Mg C ha−1 and 5.26 Mg C ha−1). Furthermore, the multiple linear regression analysis showed that vegetation specific biophysical variables (i.e. leaf area index, Normalized Difference Vegetation Index and Fraction of Photosynthetically Active Radiation) significantly improved the estimation of reforested aboveground NPP at a fine-scale resolution. These findings demonstrate the effectiveness of biophysical and remotely sensed variables in determining NPP (as carbon sequestration surrogate) at fine-scaled reforested urban landscape. Furthermore, the utility of species biometric measurements and MOD17 model offers unprecedented opportunity for improved local scale reforestation assessment and monitoring schedules.

MODIS PRI performance to track Light Use Efficiency of a Mediterranean coniferous forest: Determinants, restrictions and the role of LUE range

The relationship between the Photochemical Reflectance Index (PRI) and Light Use Efficiency (LUE) is well established at leaf and small canopy scales, but upscaling to ecosystem level is still a challenge. Only few studies have applied satellite-derived PRI to estimate LUE, mostly using MODIS, and although the results are promising, many external factors have been identified affecting PRI performance. The present study investigates determinants and restrictions of MODIS-derived PRI potential to follow the LUE variability of a Mediterranean coniferous forest.Daily and half-hour LUE values were calculated from eddy covariance measurements, dividing GPP by either Photosynthetically Active Radiation (PAR) or the absorbed fraction of PAR (APAR). Also, various PRI datasets were created based on different sensor (Terra, Aqua, Both), reference band (1, 12, 13) and observation/illumination angles. Overall, PRI correlated better with LUE calculated using PAR instead of APAR and Aqua PRI yielded better results than Terra. Restricting acquisitions according to observation/illumination angles improves the PRI:LUE relationship (maximum R2 = 0.512), with backscatter observations yielding the best correlations. Our findings suggest that MODIS-derived PRI is more sensitive to relatively large seasonal LUE changes, but is unable to closely follow severe drought events. Among the tested reference bands, the best results were derived using band 12 (546 - 556 nm), although the optimum reference band seems to depend on viewing conditions. The PRI:LUE relationship was further improved when half-hour LUE of the satellite overpass was used instead of daily LUE. However, it was found that the PRI:LUE relationships for the different datasets were strongly affected by the range of LUE values corresponding to each PRI group, with lower LUE variability resulting to weaker PRI:LUE correlations. LUE range effect should be accounted for in future studies, when different PRI datasets are compared and might explain the contradicting findings in the existing literature.

Aboveground Biomass Mapping of Crops Supported by Improved CASA Model and Sentinel-2 Multispectral Imagery

The net primary productivity (NPP) and aboveground biomass mapping of crops based on remote sensing technology are not only conducive to understanding the growth and development of crops but can also be used to monitor timely agricultural information, thereby providing effective decision making for agricultural production management. To solve the saturation problem of the NDVI in the aboveground biomass mapping of crops, the original CASA model was improved using narrow-band red-edge information, which is sensitive to vegetation chlorophyll variation, and the fraction of photosynthetically active radiation (FPAR), NPP, and aboveground biomass of winter wheat and maize were mapped in the main growing seasons. Moreover, in this study, we deeply analyzed the seasonal change trends of crops’ biophysical parameters in terms of the NDVI, FPAR, actual light use efficiency (LUE), and their influence on aboveground biomass. Finally, to analyze the uncertainty of the aboveground biomass mapping of crops, we further discussed the inversion differences of FPAR with different vegetation indices. The results demonstrated that the inversion accuracies of the FPAR of the red-edge normalized vegetation index (NDVIred-edge) and red-edge simple ratio vegetation index (SRred-edge) were higher than those of the original CASA model. Compared with the reference data, the accuracy of aboveground biomass estimated by the improved CASA model was 0.73 and 0.70, respectively, which was 0.21 and 0.13 higher than that of the original CASA model. In addition, the analysis of the FPAR inversions of different vegetation indices showed that the inversion accuracies of the red-edge vegetation indices NDVIred-edge and SRred-edge were higher than those of the other vegetation indices, which confirmed that the vegetation indices involving red-edge information can more effectively retrieve FPAR and aboveground biomass of crops.