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1. Digital hemispherical photography (DHP) is widely used to derive forest biophysi- cal variables including leaf, plant, and green area index (LAI, PAI and GAI), the fraction of intercepted photosynthetically active radiation (FIPAR), and the frac- tion of vegetation cover (FCOVER). However, the majority of software packages for processing DHP data are based on a graphical user interface, making program- matic analysis difficult. Meanwhile, few natively support analysis of RAW image formats, while none incorporate the propagation or provision of uncertainties. 2. To address these limitations, we present HemiPy, an open-source Python module for deriving forest biophysical variables and uncertainties from DHP images in an automated manner. We assess HemiPy using simulated hemispherical images, in addition to multiannual time-series and litterfall data from several forested National Ecological Observatory Network (NEON) sites, as well as comparison against the CAN-EYE software package. [... continue]

The Copernicus Global Land Service (CGLS) provides global time series of leaf area index (LAI), fraction of absorbed photosynthetically active radiation (fAPAR) and fraction of vegetation cover (fCOVER) data at a resolution of 300 m and a frequency of 10 days. We performed a quality assessment and validation of Version 1 Collection 300 m products that were consistent with the guidelines of the Land Product Validation (LPV) subgroup of the Committee on Earth Observation System (CEOS) Working Group on Calibration and Validation (WGCV). The spatiotemporal patterns of Collection 300 m V1 LAI, fAPAR and fCOVER products are consistent with CGLS Collection 1 km V1, Collection 1 km V2 and Moderate Resolution Imagery Spectroradiometer Collection 6 (MODIS C6) products. The Collection 300 m V1 products have good precision and smooth temporal profiles, and the interannual variations are consistent with similar satellite products. The accuracy assessment using ground measurements mainly over crops shows an overall root mean square deviation of 1.01 (44.3%) for LAI, 0.12 (22.2%) for fAPAR and 0.21 (42.6%) for fCOVER, with positive mean biases of 0.36 (15.5%), 0.05 (10.3%) and 0.16 (32.2%), respectively. The products meet the CGLS user accuracy requirements in 69.1%, 62.5% and 29.7% of the cases for LAI, fAPAR and fCOVER, respectively. The CGLS will continue the production of Collection 300 m V1 LAI, fAPAR and fCOVER beyond the end of the PROBA-V mission by using Sentinel-3 OLCI as input data.

Field experiments were carried out in 2014 and 2015 to characterize the associated attributes responsible for dry matter accumulation in high-yielding soybean. We attempted to create a high-yielding environment by introducing narrow-row planting at two planting densities, using a new cultivar 'Hatsusayaka' and a current leading cultivar 'Sachiyutaka' in an upland experimental field. Dry matter accumulation was assessed in terms of light interception and radiation use efficiency (RUE). Growth analysis was performed to evaluate the crop growth rate (CGR) and the contribution of the net assimilation rate (NAR) and mean leaf area index to CGR. Maximum soybean yields of 590 and 658 g m −2 were obtained for Hatsusayaka and Sachiyutaka, respectively, in 2015 at the high planting density, with the corresponding maximum aboveground dry matter equaling 1463 and 1331 g m −2 and maximum LAI equaling 8.5 and 7.6. Although cumulative intercepted solar radiation was lower than in previous studies, early canopy closure at around the beginning of the flowering stage and very high RUE (1.54 and 1.68 g MJ −2 for Hatsusayaka and Sachiyutaka, respectively) contributed to the high dry matter accumulation. In contrast to the high yield in 2015, continuous excess soil moisture in early August 2014 may have inhibited nodule nitrogen fixation and decreased the nitrogen content, resulting in an extremely high specific leaf area and low leaf greenness, which agrees well with the low NAR during the corresponding period.

Peatlands store significant amounts of carbon, which is released as greenhouse gases when peatlands are degraded. Restoration and rewetting can help prevent these emissions, while continuous monitoring is critical for evaluating their success. Using satellite-derived observations of essential climate variables, we conducted the first large-scale assessment of how peatland restoration influences land surface temperature (LST), albedo, and vegetation across 72 sites in North America and Europe. Our findings indicated that before restoration, degraded peatlands had a commonly lower daytime LST and albedo but higher nighttime LST, leaf area index (LAI), and fraction of absorbed photosynthetically active radiation (FPAR) compared to intact sites. The largest restoration-induced absolute values of monthly changes reached +3.18 °C (daytime LST), −1.22 °C (nighttime LST), −2.54 (LAI), −0.29 (FPAR), and −0.16 (albedo). While restored peatlands tended to align more closely with intact sites a decade after restoration began, the probability of this alignment varied depending on the climate variables. Restored peatlands became more similar than different to intact sites in nighttime LST and albedo after a post-restoration decade, with high similarity projected within five decades. Peatland restoration modifies local and regional climate and should be included in future climate projections.

Observational studies in several regions and our dataset indicate changes in global solar radiation (RS); here, we analyze how atmospheric conditions modulate its spectral composition. This study investigates the effects of atmospheric conditions on the spectral composition of global solar radiation (RS) across different wavelength ranges: ultraviolet A (UVA), photosynthetically active radiation (PAR), and near-infrared radiation (NIR), using the ratios UVA/RS, PAR/RS, and NIR/RS. A high-quality spectral irradiance dataset (300–1025 nm) covering eight years of observations from a representative rural site in Central Europe (Meteorological Observatory Lindenberg, Tauche, in North-East Germany) was used. The average values obtained for the ratios were 0.049 ± 0.010 for UVA/RS, 0.433 ± 0.044 for PAR/RS, and 0.259 ± 0.030 for NIR/RS. Thus, the UVA range contributed approximately 5% to global radiation, PAR 43%, and NIR 26%. Strong correlations were found between each spectral component and RS, with determination coefficients exceeding 0.90 in all cases, particularly for PAR. This suggests that, in the absence of direct spectral measurements, these components can be reliably estimated from RS. A seasonal pattern was also identified, with maximum values in warmer months and minimum values in colder ones, most notably for PAR/RS. In contrast, NIR/RS exhibited an inverse pattern, likely influenced by atmospheric water vapor. A long-term decreasing trend in these ratios was also identified, being most pronounced in the UVA/RS ratio. Additionally, atmospheric conditions significantly affected the spectral distribution of RS, with UVA and PAR proportions increasing under specific conditions, while NIR remained more stable. Under overcast conditions, the ratios for shorter wavelengths (UVA and PAR) increased, indicating higher scattering effects, while NIR was less affected.

Global biophysical products at decametric resolution derived from Sentinel-2 imagery have emerged as a promising dataset for fine-scale ecosystem modeling and agricultural monitoring. Evaluating uncertainties of different Sentinel-2 biophysical products over various regions and vegetation types is pivotal in the application of land surface models. In this study, we quantified the performance of Sentinel-2-derived Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), and Fractional Vegetation Cover (FVC) estimates using global ground observations with consistent measurement criteria. Our results show that the accuracy of vegetation and non-vegetated classification based on Sentinel-2 surface reflectance products is greater than 95%, which indicates the vegetation identification is favorable for the practical application of biophysical estimates, as several LAI, FAPAR, and FVC retrievals were derived for non-vegetated pixels. The rate of best retrievals is similar between LAI and FAPAR estimates, both accounting for 87% of all vegetation pixels, while it is almost 100% for FVC estimates. Additionally, the Sentinel-2 FAPAR and FVC estimates agree well with ground-measurements-derived (GMD) reference maps, whereas a large discrepancy is observed for Sentinel-2 LAI estimates by comparing with both GMD effective LAI (LAIe) and actual LAI (LAI) reference maps. Furthermore, the uncertainties of Sentinel-2 LAI, FAPAR and FVC estimates are 1.09 m2/m2, 1.14 m2/m2, 0.13 and 0.17 through comparisons to ground LAIe, LAI, FAPAR, and FVC measurements, respectively. Given the temporal difference between Sentinel-2 observations and ground measurements, Sentinel-2 LAI estimates are more consistent with LAIe than LAI values. The robustness of evaluation results can be further improved as long as more multi-temporal ground measurements across different regions are obtained. Overall, this study provides fundamental information about the performance of Sentinel-2 LAI, FAPAR, and FVC estimates, which imbues our confidence in the broad applications of these decametric products.

Solar-induced chlorophyll fluorescence (SIF), which can be used as a novel proxy for estimating gross primary production (GPP), can be effectively retrieved using ground-based, airborne and satellite measurements. Absorbed photosynthetically active radiation (APAR) is the key bridge linking SIF and GPP. Remotely sensed SIF at the canopy level ( S I F c a n o p y ) is only a part of the total SIF emission at the photosystem level. An SIF-based model for GPP estimation would be strongly influenced by the fraction of SIF photons escaping from the canopy ( f e s c ). Understanding the response of S I F c a n o p y to the absorbed photosynthetically active radiation absorbed by chlorophyll ( A P A R c h l ) is a key step in estimating GPP but, as yet, this has not been well explored. In this study, we aim to investigate the relationship between remotely sensed S I F c a n o p y and A P A R c h l based on simulations made by the Soil Canopy Observation Photosynthesis Energy fluxes (SCOPE) model and field measurements. First, the ratio of the fraction of the absorbed photosynthetically active radiation absorbed by chlorophyll ( fPAR c h l ) to the fraction of absorbed photosynthetically active radiation absorbed by green leaves ( fPAR g r e e n ) is investigated using a dataset simulated by the SCOPE model. The results give a mean value of 0.722 for Cab at 5 μg cm−2, 0.761 for Cab at 10 μg cm−2 and 0.795 for other Cab content (ranging from 0.71 to 0.81). The response of S I F c a n o p y to A P A R c h l is then explored using simulations corresponding to different biochemical and biophysical conditions and it is found that S I F c a n o p y is well correlated with A P A R c h l . At the O2-A band, for a given plant type, the relationship between S I F c a n o p y and A P A R c h l can be approximately expressed by a linear statistical model even for different values of the leaf area index (LAI) and chlorophyll content, whereas the relationship varies with the LAI and chlorophyll content at the O2-B band. Finally, the response of S I F c a n o p y to A P A R c h l for different leaf angle distribution (LAD) functions is investigated using field observations and simulations; the results show that f e s c is larger for a planophile canopy structure. The values of the ratio of S I F c a n o p y to A P A R c h l are 0.0092 ± 0.0020 , 0.0076 ± 0.0036 and 0.0052 ± 0.0004 μm−1 sr−1 for planophile vegetables/crops, planophile grass and spherical winter wheat, respectively, at the O2-A band. At the O2-B band, the ratios are 0.0063 ± 0.0014 , 0.0049 ± 0.0030 and 0.0033 ± 0.0004 μm−1 sr−1, respectively. The values of this ratio derived from observations agree with simulations, giving values of 0.0055 ± 0.0002 and 0.0068 ± 0.0001 μm−1 sr−1 at the O2-A band and 0.0032 ± 0.0002 and 0.0047 ± 0.0001 μm−1 sr−1 at the O2-B band for spherical and planophile canopies, respectively. Therefore, both the simulations and observations confirm that the relationship between S I F c a n o p y and APAR c h l is species-specific and affected by biochemical components and canopy structure, especially at the O2-B band. It is also very important to correct for reabsorption and scattering of the SIF radiative transfer from the photosystem to the canopy level before the remotely sensed S I F c a n o p y is linked to the GPP.

Carbon dioxide concentrating mechanisms (also known as inorganic carbon concentrating mechanisms; both abbreviated as CCMs) presumably evolved under conditions of low CO 2 availability. However, the timing of their origin is unclear since there are no sound estimates from molecular clocks, and even if there were, there are no proxies for the functioning of CCMs. Accordingly, we cannot use previous episodes of high CO 2 (e.g. the Palaeocene–Eocene Thermal Maximum) to indicate how organisms with CCMs responded. Present and predicted environmental change in terms of increased CO 2 and temperature are leading to increased CO 2 and HCO 3 − and decreased CO 3 2− and pH in surface seawater, as well as decreasing the depth of the upper mixed layer and increasing the degree of isolation of this layer with respect to nutrient flux from deeper waters. The outcome of these forcing factors is to increase the availability of inorganic carbon, photosynthetic active radiation (PAR) and ultraviolet B radiation (UVB) to aquatic photolithotrophs and to decrease the supply of the nutrients (combined) nitrogen and phosphorus and of any non-aeolian iron. The influence of these variations on CCM expression has been examined to varying degrees as acclimation by extant organisms. Increased PAR increases CCM expression in terms of CO 2 affinity, whilst increased UVB has a range of effects in the organisms examined; little relevant information is available on increased temperature. Decreased combined nitrogen supply generally increases CO 2 affinity, decreased iron availability increases CO 2 affinity, and decreased phosphorus supply has varying effects on the organisms examined. There are few data sets showing interactions amongst the observed changes, and even less information on genetic (adaptation) changes in response to the forcing factors. In freshwaters, changes in phytoplankton species composition may alter with environmental change with consequences for frequency of species with or without CCMs. The information available permits less predictive power as to the effect of the forcing factors on CCM expression than for their overall effects on growth. CCMs are currently not part of models as to how global environmental change has altered, and is likely to further alter, algal and aquatic plant primary productivity.

Uncertainty assessment of the moderate resolution imaging spectroradiometer (MODIS) leaf area index (LAI) and the fraction of photosynthetically active radiation absorbed by vegetation (FPAR) retrieval algorithm can provide a scientific basis for the usage and improvement of this widely-used product. Previous evaluations generally depended on the intercomparison with other datasets as well as direct validation using ground measurements, which mix the uncertainties from the model, inputs, and assessment method. In this study, we adopted the evaluation method based on three-dimensional radiative transfer model (3D RTM) simulations, which helps to separate model uncertainty and other factors. We used the well-validated 3D RTM LESS (large-scale remote sensing data and image simulation framework) for a grassland scene simulation and calculated bidirectional reflectance factors (BRFs) as inputs for the LAI/FPAR retrieval. The dependency between LAI/FPAR truth and model estimation serves as the algorithm uncertainty indicator. This paper analyzed the LAI/FPAR uncertainty caused by inherent model uncertainty, input uncertainty (BRF and biome classification), clumping effect, and scale dependency. We found that the uncertainties of different algorithm paths vary greatly (−6.61% and +84.85% bias for main and backup algorithm, respectively) and the “hotspot” geometry results in greatest retrieval uncertainty. For the input uncertainty, the BRF of the near-infrared (NIR) band has greater impacts than that of the red band, and the biome misclassification also leads to nonnegligible LAI/FPAR bias. Moreover, the clumping effect leads to a significant LAI underestimation (−0.846 and −0.525 LAI difference for two clumping types), but the scale dependency (pixel size ranges from 100 m to 1000 m) has little impact on LAI/FPAR uncertainty. Overall, this study provides a new perspective on the evaluation of LAI/FPAR retrieval algorithms.

Integrated crop-livestock-forestry systems are strategies to diversify production in the same area, aiming at higher sustainability. Corn is one of the most used crops in integrated systems due to its multiple uses on the farm. The objective of this study was to evaluate the effect of light restriction on morphological and productive characteristics of the forage components (corn for silage and pastures) in integrated livestock production systems. The experiment was carried out on Embrapa’s integrated systems in São Carlos, SP, Brazil, during two growing seasons. In the integrated crop-livestock (iCL) system corn was sown simultaneously with Piatã-grass. In the integrated crop-livestock- forestry (iCLF) system, besides corn and Piatã-grass, Eucalyptus urograndis trees were planted in single 15 × 2 m rows. Corn productivity and pasture yield and nutritive value were evaluated in iCL and iCLF systems. In iCLF, evaluations were made in four positions relative to the tree lines: 1.5, 3.75, 7.5, and 11.25 m. Photosynthetically active radiation (PAR) transmission by trees was also measured in the same positions. Results of the corn crop indicated that the position relative to the tree lines affected (P < 0.001) all morphological characteristics and production parameters. Production was not affected by position relative to the tree lines in the first year but was lower in the position closer to the trees in the second year. As an average of all positions evaluated in iCLF, corn DM production in the first agricultural year was 43.3% inferior compared to the second year (5266.1 vs. 9816.7 kg DM ha−1). In iCL, the average corn DM production for the two years was 10082 kg DM ha−1. For the corn, the relationship between PAR transmission and the relative yield (production in each position in iCLF divided by the production in iCL) was influenced by drought in the 2013/2014 growing season. Considering the results for the renovated pastures, position relative to the tree lines affected pasture height (P < 0.0001), specific leaf area (P < 0.0012), digestibility (P < 0.0085) and crude protein (P < 0.0001).