Multi-Crop Green LAI Estimation with a New Simple Sentinel-2 LAI Index (SeLI)

The spatial quantification of green leaf area index (LAIgreen), the total green photosynthetically active leaf area per ground area, is a crucial biophysical variable for agroecosystem monitoring. The Sentinel-2 mission is with (1) a temporal resolution lower than a week, (2) a spatial resolution of up to 10 m, and (3) narrow bands in the red and red-edge region, a highly promising mission for agricultural monitoring. The aim of this work is to define an easy implementable LAIgreen index for the Sentinel-2 mission. Two large and independent multi-crop datasets of in situ collected LAIgreen measurements were used. Commonly used LAIgreen indices applied on the Sentinel-2 10 m × 10 m pixel resulted in a validation R² lower than 0.6. By calculating all Sentinel-2 band combinations to identify high correlation and physical basis with LAIgreen, the new Sentinel-2 LAIgreen Index (SeLI) was defined. SeLI is a normalized index that uses the 705 nm and 865 nm centered bands, exploiting the red-edge region for low-saturating absorption sensitivity to photosynthetic vegetation. A R² of 0.708 (root mean squared error (RMSE) = 0.67) and a R² of 0.732 (RMSE = 0.69) were obtained with a linear fitting for the calibration and validation datasets, respectively, outperforming established indices. Sentinel-2 LAIgreen maps are presented.

Multi-sensor spectral synergies for crop stress detection and monitoring in the optical domain: A review

Remote detection and monitoring of the vegetation responses to stress became relevant for sustainable agriculture. Ongoing developments in optical remote sensing technologies have provided tools to increase our understanding of stress-related physiological processes. Therefore, this study aimed to provide an overview of the main spectral technologies and retrieval approaches for detecting crop stress in agriculture. Firstly, we present integrated views on: i) biotic and abiotic stress factors, the phases of stress, and respective plant responses, and ii) the affected traits, appropriate spectral domains and corresponding methods for measuring traits remotely. Secondly, representative results of a systematic literature analysis are highlighted, identifying the current status and possible future trends in stress detection and monitoring. Distinct plant responses occurring under shortterm, medium-term or severe chronic stress exposure can be captured with remote sensing due to specific light interaction processes, such as absorption and scattering manifested in the reflected radiance, i.e. visible (VIS), near infrared (NIR), shortwave infrared, and emitted radiance, i.e. solar-induced fluorescence and thermal infrared (TIR). From the analysis of 96 research papers, the following trends can be observed: increasing usage of satellite and unmanned aerial vehicle data in parallel with a shift in methods from simpler parametric approaches towards more advanced physically-based and hybrid models. Most study designs were largely driven by sensor availability and practical economic reasons, leading to the common usage of VIS-NIR-TIR sensor combinations. The majority of reviewed studies compared stress proxies calculated from single-source sensor domains rather than using data in a synergistic way. We identified new ways forward as guidance for improved synergistic usage of spectral domains for stress detection: (1) combined acquisition of data from multiple sensors for analysing multiple stress responses simultaneously (holistic view); (2) simultaneous retrieval of plant traits combining multi-domain radiative transfer models and machine learning methods; (3) assimilation of estimated plant traits from distinct spectral domains into integrated crop growth models. As a future outlook, we recommend combining multiple remote sensing data streams into crop model assimilation schemes to build up Digital Twins of agroecosystems, which may provide the most efficient way to detect the diversity of environmental and biotic stresses and thus enable respective management decisions.

Chlorophyll a fluorescence illuminates a path connecting plant molecular biology to Earth-system science

For decades, the dynamic nature of chlorophyll a fluorescence (ChlaF) has provided insight into the biophysics and ecophysiology of the light reactions of photosynthesis from the subcellular to leaf scales. Recent advances in remote sensing methods enable detection of ChlaF induced by sunlight across a range of larger scales, from using instruments mounted on towers above plant canopies to Earth-orbiting satellites. This signal is referred to as solar-induced fluorescence (SIF) and its application promises to overcome spatial constraints on studies of photosynthesis, opening new research directions and opportunities in ecology, ecophysiology, biogeochemistry, agriculture and forestry. However, to unleash the full potential of SIF, intensive cross-disciplinary work is required to harmonize these new advances with the rich history of biophysical and ecophysiological studies of ChlaF, fostering the development of next-generation plant physiological and Earth-system models. Here, we introduce the scale-dependent link between SIF and photosynthesis, with an emphasis on seven remaining scientific challenges, and present a roadmap to facilitate future collaborative research towards new applications of SIF.

Retrieval of canopy water content of different crop types with two new hyperspectral indices: Water Absorption Area Index and Depth Water Index

Crop canopy water content (CWC) is an essential indicator of the crop's physiological state. While a diverse range of vegetation indices have earlier been developed for the remote estimation of CWC, most of them are defined for specific crop types and areas, making them less universally applicable. We propose two new water content indices applicable to a wide variety of crop types, allowing to derive CWC maps at a large spatial scale. These indices were developed based on PROSAIL simulations and then optimized with an experimental dataset (SPARC03; Barrax, Spain). This dataset consists of water content and other biophysical variables for five common crop types (lucerne, corn, potato, sugar beet and onion) and corresponding top-of-canopy (TOC) reflectance spectra acquired by the hyperspectral HyMap airborne sensor. First, commonly used water content index formulations were analysed and validated for the variety of crops, overall resulting in a R² lower than 0.6. In an attempt to move towards more generically applicable indices, the two new CWC indices exploit the principal water absorption features in the near-infrared by using multiple bands sensitive to water content. We propose the Water Absorption Area Index (WAAI) as the difference between the area under the null water content of TOC reflectance (reference line) simulated with PROSAIL and the area under measured TOC reflectance between 911 and 1271 nm. We also propose the Depth Water Index (DWI), a simplified four-band index based on the spectral depths produced by the water absorption at 970 and 1200 nm and two reference bands. Both the WAAI and DWI outperform established indices in predicting CWC when applied to heterogeneous croplands, with a R² of 0.8 and 0.7, respectively, using an exponential fit. However, these indices did not perform well for species with a low fractional vegetation cover (< 30%). HyMap CWC maps calculated with both indices are shown for the Barrax region. The results confirmed the potential of using generically applicable indices for calculating CWC over a great variety of crops.

Gaussian processes retrieval of leaf parameters from a multi-species reflectance, absorbance and fluorescence dataset

Biochemical and structural leaf properties such as chlorophyll content (Chl), nitrogen content (N), leaf water content (LWC), and specific leaf area (SLA) have the benefit to be estimated through nondestructive spectral measurements. Current practices, however, mainly focus on a limited amount of wavelength bands while more information could be extracted from other wavelengths in the full range (400-2500nm) spectrum. In this research, leaf characteristics were estimated from a field-based multi-species dataset, covering a wide range in leaf structures and Chl concentrations. The dataset contains leaves with extremely high Chl concentrations (>100μgcm(-2)), which are seldom estimated. Parameter retrieval was conducted with the machine learning regression algorithm Gaussian Processes (GP), which is able to perform adaptive, nonlinear data fitting for complex datasets. Moreover, insight in relevant bands is provided during the development of a regression model. Consequently, the physical meaning of the model can be explored. Best estimates of SLA, LWC and Chl yielded a best obtained normalized root mean square error of 6.0%, 7.7%, 9.1%, respectively. Several distinct wavebands were chosen across the whole spectrum. A band in the red edge (710nm) appeared to be most important for the estimation of Chl. Interestingly, spectral features related to biochemicals with a structural or carbon storage function (e.g. 1090, 1550, 1670, 1730nm) were found important not only for estimation of SLA, but also for LWC, Chl or N estimation. Similar, Chl estimation was also helped by some wavebands related to water content (950, 1430nm) due to correlation between the parameters. It is shown that leaf parameter retrieval by GP regression is successful, and able to cope with large structural differences between leaves.

A field study on solar-induced chlorophyll fluorescence and pigment parameters along a vertical canopy gradient of four tree species in an urban environment

To better understand the potential uses of vegetation indices based on the sun-induced upward and downward chlorophyll fluorescence at leaf and at canopy scales, a field study was carried out in the city of Valencia (Spain). Fluorescence yield (FY) indices were derived for trees at different traffic intensity locations and at three canopy heights. This allowed investigating within-tree and between-tree variations of FY indices for four tree species. Several FY indices showed a significant (p < 0.05) and important effect of tree location for the species Morus alba (white mulberry) and Phoenix canariensis (Canary Island date palm). The upward FY parameters of M. alba, and the upward to downward ratios at 687 and 741 nm for both species, were significantly related to tree location. It was found that not the total chlorophyll (Chl) content, but rather the Chl a/b ratio showed the strongest correlations with several of the indices applied. Chl a/b was lowest at the bottom level of the highest traffic intensity location for both species due to an increased Chl b, indicating a larger light harvesting complex related to Photosystem II (LHCII) as a response to limiting light. The leaf deposits from traffic observed at this sampling location possibly led to a shading effect, resulting further in an adaptive response of the photosynthetic system and subsequent difference of FY indices. This study therefore indicated the importance of the size of LHCII on the fluorescence emission, observed under different traffic generated pollution conditions.

Contributing factors in foliar uptake of dissolved inorganic nitrogen at leaf level

We investigated the influence of leaf traits, rainwater chemistry, and pedospheric nitrogen (N) fertilisation on the aqueous uptake of inorganic N by physiologically active tree leaves. Leaves of juvenile silver birch and European beech trees, supplied with NH₄NO₃ to the soil at rates from 0 to 200 kg N ha(-1)y(-1), were individually exposed to 100 μl of artificial rainwater containing (15)NH₄(+) or (15)NO₃(-) at two concentration levels for one hour. In the next vegetative period, the experiment was repeated with NH₄(+) at the highest concentration only. The N form and the N concentration in the applied rainwater and, to a lesser extent, the pedospheric N treatment and the leaf traits affected the aqueous foliar N uptake. The foliar uptake of NH₄(+) by birch increased when leaves were more wettable. High leaf N concentration and leaf mass per area enhanced the foliar N uptake, and NO₃(-) uptake in particular, by birch. Variation in the foliar N uptake by the beech trees could not be explained by the leaf traits considered. In the first experiment, N fertilisation stimulated the foliar N uptake in both species, which was on average 1.42-1.78 times higher at the highest soil N dose than at the zero dose. However, data variability was high and the effect was not appreciable in the second experiment. Our data suggest that next to rainwater chemistry (N form and concentration) also forest N status could play a role in the partitioning of N entering the ecosystem through the soil and the canopy. Models of canopy uptake of aqueous N at the leaf level should take account of leaf traits such as wettability and N concentration.

Reprint of On the link between biomagnetic monitoring and leaf-deposited dust load of urban trees: relationships and spatial variability of different particle size fractions

Biomagnetic monitoring of urban tree leaves has proven to be a good estimator of ambient particulate matter. We evaluated its relevancy by determining leaf area normalised weight (mg m(-2)) and SIRM (A) of leaf-deposited particles within three different size fractions (>10 μm, 3-10 μm and 0.2-3 μm) and the SIRM of the leaf-encapsulated particles. Results showed that throughout the in-leaf season, the trees accumulated on average 747 mg m(-2) of dust on their leaves, of which 74 mg m(-2) was within the 0.2-10 μm (∼PM10) size range and 40 mg m(-2) within the 0.2-3 μm (∼PM3) size range. A significant correlation between the SIRM and weight of the surface-deposited particles confirms the potential of biomagnetic monitoring as a proxy for the amount of leaf-deposited particles. Spatial variation of both SIRM and weight throughout the street canyon suggests traffic and wind as key factors for respectively the source and distribution of urban particulates.

On the link between biomagnetic monitoring and leaf-deposited dust load of urban trees: relationships and spatial variability of different particle size fractions

Biomagnetic monitoring of urban tree leaves has proven to be a good estimator of ambient particulate matter. We evaluated its relevancy by determining leaf area normalised weight (mg m(-2)) and SIRM (A) of leaf-deposited particles within three different size fractions (>10 μm, 3-10 μm and 0.2-3 μm) and the SIRM of the leaf-encapsulated particles. Results showed that throughout the in-leaf season, the trees accumulated on average 747 mg m(-2) of dust on their leaves, of which 74 mg m(-2) was within the 0.2-10 μm (∼PM10) size range and 40 mg m(-2) within the 0.2-3 μm (∼PM3) size range. A significant correlation between the SIRM and weight of the surface-deposited particles confirms the potential of biomagnetic monitoring as a proxy for the amount of leaf-deposited particles. Spatial variation of both SIRM and weight throughout the street canyon suggests traffic and wind as key factors for respectively the source and distribution of urban particulates.

On the temporal variation of leaf magnetic parameters: seasonal accumulation of leaf-deposited and leaf-encapsulated particles of a roadside tree crown

Understanding the accumulation behaviour of atmospheric particles inside tree leaves is of great importance for the interpretation of biomagnetic monitoring results. In this study, we evaluated the temporal variation of the saturation isothermal remanent magnetisation (SIRM) of leaves of a roadside urban Platanus × acerifolia Willd. tree in Antwerp, Belgium. We hereby examined the seasonal development of the total leaf SIRM signal as well as the leaf-encapsulated fraction of the deposited dust, by washing the leaves before biomagnetic analysis. On average 38% of the leaf SIRM signal was exhibited by the leaf-encapsulated particles. Significant correlations were found between the SIRM and the cumulative daily average atmospheric PM10 and PM2.5 measurements. Moreover, a steady increase of the SIRM throughout the in-leaf season was observed endorsing the applicability of biomagnetic monitoring as a proxy for the time-integrated PM exposure of urban tree leaves. Strongest correlations were obtained for the SIRM of the leaf-encapsulated particles which confirms the dynamic nature of the leaf surface-accumulated particles.

In vivo photoprotection mechanisms observed from leaf spectral absorbance changes showing VIS-NIR slow-induced conformational pigment bed changes

Regulated heat dissipation under excessive light comprises a complexity of mechanisms, whereby the supramolecular light-harvesting pigment-protein complex (LHC) shifts state from light harvesting towards heat dissipation, quenching the excess of photo-induced excitation energy in a non-photochemical way. Based on whole-leaf spectroscopy measuring upward and downward spectral radiance fluxes, we studied spectrally contiguous (hyperspectral) transient time series of absorbance A(λ,t) and passively induced chlorophyll fluorescence F(λ,t) dynamics of intact leaves in the visible and near-infrared wavelengths (VIS-NIR, 400-800 nm) after sudden strong natural-like illumination exposure. Besides light avoidance mechanism, we observed on absorbance signatures, calculated from simultaneous reflectance R(λ,t) and transmittance T(λ,t) measurements as A(λ,t) = 1 - R(λ,t) - T(λ,t), major dynamic events with specific onsets and kinetical behaviour. A consistent well-known fast carotenoid absorbance feature (500-570 nm) appears within the first seconds to minutes, seen from both the reflected (backscattered) and transmitted (forward scattered) radiance differences. Simultaneous fast Chl features are observed, either as an increased or decreased scattering behaviour during quick light adjustment consistent with re-organizations of the membrane. The carotenoid absorbance feature shows up simultaneously with a major F decrease and corresponds to the xanthophyll conversion, as quick response to the proton gradient build-up. After xanthophyll conversion (t = 3 min), a kinetically slower but major and smooth absorbance increase was occasionally observed from the transmitted radiance measurements as wide peaks in the green (~ 550 nm) and the near-infrared (~ 750 nm) wavelengths, involving no further F quenching. Surprisingly, in relation to the response to high light, this broad and consistent VIS-NIR feature indicates a slowly induced absorbance increase with a sigmoid kinetical behaviour. In analogy to sub-leaf-level observations, we suggest that this mechanism can be explained by a structure-induced low-energy-shifted energy redistribution involving both Car and Chl. These findings might pave the way towards a further non-invasive spectral investigation of antenna conformations and their relations with energy quenching at the intact leaf level, which is, in combination with F measurements, of a high importance for assessing plant photosynthesis in vivo and in addition from remote observations.

Hyperspectral leaf reflectance of Carpinus betulus L. saplings for urban air quality estimation

In urban areas, the demand for local assessment of air quality is high. The existing monitoring stations cannot fulfill the needs. This study assesses the potential of hyperspectral tree leaf reflectance for monitoring traffic related air pollution. Hereto, 29 Carpinus betulus saplings were exposed to an environment with either high or low traffic intensity. The local air quality was estimated by leaf saturation isothermal remanent magnetization (SIRM). The VIS-NIR leaf reflectance spectrum (350-2500 nm) was measured using a handheld AgriSpec spectroradiometer (ASD Inc.). Secondary, leaf chlorophyll content index (CCI), specific leaf area (SLA) and water content (WC) were determined. To gain insight in the link between leaf reflectance and air quality, the correlation between SIRM and several spectral features was determined. The spectral features that were tested are plain reflectance values, derivative of reflectance, two-band indices using the NDVI formula and PCA components. Spectral reflectance for wavelength bands in the red and short wave IR around the red edge, were correlated to SIRM with Pearson correlations of up to R = -0.85 (R² = 0.72). Based on the spectral features and combinations thereof, binomial logistic regression models were trained to classify trees into high or low traffic pollution exposure, with classification accuracies up to 90%. It can be concluded that hyperspectral reflectance of C. betulus leaves can be used to detect different levels of air pollution within an urban environment.

Improving the remote estimation of soil organic carbon in complex ecosystems with Sentinel-2 and GIS using Gaussian processes regression

BACKGROUND AND AIMS

The quantitative retrieval of soil organic carbon (SOC) storage, particularly for soils with a large potential for carbon sequestration, is of global interest due to its link with the carbon cycle and the mitigation of climate change. However, complex ecosystems with good soil qualities for SOC storage are poorly studied.

METHODS

The interrelation between SOC and various vegetation remote sensing drivers is understood to demonstrate the link between the carbon stored in the vegetation layer and SOC of the top soil layers. Based on the mapping of SOC in two horizons (0-30 cm and 30-60 cm) we predict SOC with high accuracy in the complex and mountainous heterogeneous páramo system in Ecuador. A large SOC database (in weight % and in Mg/ha) of 493 and 494 SOC sampling data points from 0-30 cm and 30-60 cm soil profiles, respectively, were used to calibrate GPR models using Sentinel-2 and GIS predictors (i.e., Temperature, Elevation, Soil Taxonomy, Geological Unit, Slope Length and Steepness (LS Factor), Orientation and Precipitation).

RESULTS

In the 0-30 cm soil profile, the models achieved a R² of 0.85 (SOC%) and a R² of 0.79 (SOC Mg/ha). In the 30-60 cm soil profile, models achieved a R² of 0.86 (SOC%), and a R² of 0.79 (SOC Mg/ha).

CONCLUSIONS

The used Sentinel-2 variables (FVC, CWC, LCC/C_ab, band 5 (705 nm) and SeLI index) were able to improve the estimation accuracy between 3-21% compared to previous results of the same study area. CWC emerged as the most relevant biophysical variable for SOC prediction.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s11104-022-05506-1.

Leaf reflectance variation along a vertical crown gradient of two deciduous tree species in a Belgian industrial habitat

The reflectometry of leaf asymmetry is a novel approach in the bio-monitoring of tree health in urban or industrial habitats. Leaf asymmetry responds to the degree of environmental pollution and reflects structural changes in a leaf due to environmental pollution. This paper describes the boundary conditions to scale up from leaf to canopy level reflectance, by describing the variability of adaxial and abaxial leaf reflectance, hence leaf asymmetry, along the crown height gradients of two tree species. Our findings open a research pathway towards bio-monitoring based on the airborne remote sensing of tree canopies and their leaf asymmetric properties.

Multi-predictor mapping of soil organic carbon in the alpine tundra: a case study for the central Ecuadorian páramo

BACKGROUND

Soil organic carbon (SOC) affects essential biological, biochemical, and physical soil functions such as nutrient cycling, water retention, water distribution, and soil structure stability. The Andean páramo known as such a high carbon and water storage capacity ecosystem is a complex, heterogeneous and remote ecosystem complicating field studies to collect SOC data. Here, we propose a multi-predictor remote quantification of SOC using Random Forest Regression to map SOC stock in the herbaceous páramo of the Chimborazo province, Ecuador.

RESULTS

Spectral indices derived from the Landsat-8 (L8) sensors, OLI and TIRS, topographic, geological, soil taxonomy and climate variables were used in combination with 500 in situ SOC sampling data for training and calibrating a suitable predictive SOC model. The final predictive model selected uses nine predictors with a RMSE of 1.72% and a R² of 0.82 for SOC expressed in weight %, a RMSE of 25.8 Mg/ha and a R² of 0.77 for the model in units of Mg/ha. Satellite-derived indices such as VARIG, SLP, NDVI, NDWI, SAVI, EVI2, WDRVI, NDSI, NDMI, NBR and NBR2 were not found to be strong SOC predictors. Relevant predictors instead were in order of importance: geological unit, soil taxonomy, precipitation, elevation, orientation, slope length and steepness (LS Factor), Bare Soil Index (BI), average annual temperature and TOA Brightness Temperature.

CONCLUSIONS

Variables such as the BI index derived from satellite images and the LS factor from the DEM increase the SOC mapping accuracy. The mapping results show that over 57% of the study area contains high concentrations of SOC, between 150 and 205 Mg/ha, positioning the herbaceous páramo as an ecosystem of global importance. The results obtained with this study can be used to extent the SOC mapping in the whole herbaceous ecosystem of Ecuador offering an efficient and accurate methodology without the need for intensive in situ sampling.

In vivo detection of spectral reflectance changes associated with regulated heat dissipation mechanisms complements fluorescence quantum efficiency in early stress diagnosis

Early stress detection of crops requires a thorough understanding of the signals showing the very first symptoms of the alterations in the photosynthetic light reactions. Detection of the activation of the regulated heat dissipation mechanism is crucial to complement passively induced fluorescence to resolve ambuiguities in energy partitioning. Using leaf spectroscopy, we evaluated the capability of pigment spectral unmixing to calculate the fluorescence quantum efficiency (FQE) and simultaneously retrieve fast absorption changes in a drought and nitrogen deficiency experiment with tomato. In addition, active fluorescence measurements and pigment analyses of xanthophylls, carotenes and chlorophylls were conducted. We observed notable responses in noninvasive proximal sensing-retrieved FQE values under stress, but as expected, these alone were not enough to identify the constraints in photosynthetic efficiency. Reflectance-based detection of the 535-nm peak absorption change was able to complement FQE and indicate the activation of regulated heat dissipation for both stress treatments under growing light conditions. However, further complexity in the light harvesting energy regulation needs to be accounted for when considering additional light stress. Our results underscore the potential of complementary in vivo quantitative spectroscopy-based products in the early and nondestructive stress diagnosis of plants, marking the path for further applications.

Spatial and Temporal Analysis of Water Quality in High Andean Lakes with Sentinel-2 Satellite Automatic Water Products

The water of high Andean lakes is strongly affected by anthropic activities. However, due to its complexity this ecosystem is poorly researched. This study analyzes water quality using Sentinel-2 (S2) images in high Andean lakes with apparent different eutrophication states. Spatial and temporal patterns are assessed for biophysical water variables from automatic products as obtained from versions of C2RCC (Case 2 Regional Coast Color) processor (i.e., C2RCC, C2X, and C2X-COMPLEX) to observe water characteristics and eutrophication states in detail. These results were validated using in situ water sampling. C2X-COMPLEX appeared to be an appropriate option to study bodies of water with a complex dynamic of water composition. C2RCC was adequate for lakes with high transparency, typical for lakes of highlands with excellent water quality. The Yambo lake, with chlorophyll-a concentration (CHL) values of 79.6 ± 5 mg/m3, was in the eutrophic to hyper-eutrophic state. The Colta lake, with variable values of CHL, was between the oligotrophic to mesotrophic state, and the Atillo lakes, with values of 0.16 ± 0.1 mg/m3, were oligotrophic and even ultra-oligotrophic, which remained stable in the last few years. Automatic S2 water products give information about water quality, which in turn makes it possible to analyze its causes.