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 short-term, 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

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

Photoprotection dynamics observed at leaf level from fast temporal reflectance changes

Vegetation dynamically reacts to the available photosynthetically active radiation (PAR) by adjusting the photosynthetic apparatus to either a light harvesting or a photoprotective modus. When activating the photoprotection mechanism, either minor or major pigment-protein interactions may occur at the leaf level, resulting in different light absorption and consequently reflectance intensities. The reflectance changes were measured during sudden illumination transients designed to provoke fast adaptation to high irradiance. Different spectral reflectance change features were observed during different stages of photoprotection activation, extending over part of the visible spectral range (i.e. 490-650 nm). Due to this multiple wavelength reflectance modification, which affects also the reference band at 570 nm, the commonly used Photochemical Reflectance Index (PRI) is unable to trace and quantify such strong photoprotection mechanism. To quantify the entire photoprotection with a required accuracy, the spectral changes in the full visible range must be characterized

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/Cab, 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

A New Algorithm for the Retrieval of Sun Induced Chlorophyll Fluorescence of Water Bodies Exploiting the Detailed Spectral Shape of Water-Leaving Radiance

Sun induced chlorophyll fluorescence (SICF) emitted by phytoplankton provides considerable insights into the vital role of the carbon productivity of the earth's aquatic ecosystems. However, the SICF signal leaving a water body is highly affected by the high spectral variability of its optically active constituents. To disentangle the SICF emission from the water-leaving radiance, a new high spectral resolution retrieval algorithm is presented, which significantly improves the fluorescence line height (FLH) method commonly used so far. The proposed algorithm retrieves the reflectance without SICF contribution by the extrapolation of the reflectance from the adjacent regions. Then, the SICF emission curve is obtained as the difference of the reflectance with SICF, the one actually obtained by any remote sensor (apparent reflectance), and the reflectance without SICF, the one estimated by the algorithm (true reflectance). The algorithm first normalizes the reflectance spectrum at 780 nm, following the similarity index approximation, to minimize the variability due to other optically active constituents different from chlorophyll. Then, the true reflectance is estimated empirically from the normalized reflectance at three wavelengths using a machine learning regression algorithm (MLRA) and a cubic spline fitting adjustment. Two large reflectance databases, representing a wide range of coastal and ocean water components and scattering conditions, were independently simulated with the radiative transfer model HydroLight and used for training and validation of the MLRA fitting strategy. The best results for the high spectral resolution SICF retrieval were obtained using support vector regression, with relative errors lower than 2% for the SICF peak value in 81% of the samples. This represents a significant improvement with respect to the classic FLH algorithm, applied for OLCI bands, for which the relative errors were higher than 40% in 59% of the samples

Mapping Productivity and Essential Biophysical Parameters of Cultivated Tropical Grasslands from Sentinel-2 Imagery

Nitrogen (N) is the main nutrient element that maintains productivity in forages; it is inextricably linked to dry matter increase and plant support capacity. In recent years, high spectral and spatial resolution remote sensors, e.g., the European Space Agency (ESA)'s Sentinel satellite missions, have become freely available for agricultural science, and have proven to be powerful monitoring tools. The use of vegetation indices has been essential for crop monitoring and biomass estimation models. The objective of this work is to test and demonstrate the applicability of different vegetation indices to estimate the biomass productivity, the foliar nitrogen content (FNC), the plant height and the leaf area index (LAI) of several tropical grasslands species submitted to different nitrogen (N) rates in an experimental area of São Paulo, Brazil. Field reflectance data of Panicum maximum and Urochloa brizantha species' cultivars were taken and convoluted to the Sentinel-2 satellite bands. Subsequently, different vegetation indices (Normalized Difference Vegetation Index (NDI), Three Band Index (TBI), Difference light Height (DLH), Three Band Dall'Olmo (DO), and Normalized Area Over reflectance Curve (NAOC)) were tested for the experimental grassland areas, and composed of Urochloa decumbens and Urochloa brizantha grass species, which were sampled and destructively analyzed. Our results show the use of different relevant Sentinel-2 bands in the visible (VIS)-near infrared (NIR) regions for the estimation of the different biophysical parameters. The FNC obtained the best correlation for the TBI index combining blue, green and red bands with a determination coefficient (R2) of 0.38 and Root Mean Square Error (RMSE) of 3.4 g kg−1. The estimation of grassland productivity based on red-edge and NIR bands showed a R2 = 0.54 and a RMSE = 1800 kg ha−1. For the LAI, the best index was the NAOC (R2 = 0.57 and RMSE = 1.4 m2 m−2). High values of FNC, productivity and LAI based on different sets of Sentinel-2 bands were consistently obtained for areas under N fertilization

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

Impact of Structural, Photochemical and Instrumental Effects on Leaf and Canopy Reflectance Variability in the 500-600 nm Range

Current rapid technological improvement in optical radiometric instrumentation provides an opportunity to develop innovative measurements protocols where the remote quantification of the plant physiological status can be determined with higher accuracy. In this study, the leaf and canopy reflectance variability in the PRI spectral region (i.e., 500-600 nm) is quantified using different laboratory protocols that consider both instrumental and experimental set-up aspects, as well as canopy structural effects and vegetation photoprotection dynamics. First, we studied how an incorrect characterization of the at-target incoming radiance translated into an erroneous vegetation reflectance spectrum and consequently in an incorrect quantification of reflectance indices such as PRI. The erroneous characterization of the at-target incoming radiance translated into a 2% overestimation and a 31% underestimation of estimated chlorophyll content and PRI-related vegetation indexes, respectively. Second, we investigated the dynamic xanthophyll pool and intrinsic Chl vs. Car long-term pool changes affecting the entire 500-600 nm spectral region. Consistent spectral behaviors were observed for leaf and canopy experiments. Sun-adapted plants showed a larger optical change in the PRI range and a higher capacity for photoprotection during the light transient time when compared to shade-adapted plants. Outcomes of this work highlight the importance of well-established spectroscopy sampling protocols to detect the subtle photochemical features which need to be disentangled from the structural and biological effects

Retrieval of evapotranspiration from sentinel-2: Comparison of vegetation indices, semi-empirical models and SNAP biophysical processor approach

Remote sensing evapotranspiration estimation over agricultural areas is increasingly used for irrigation management during the crop growing cycle. Different methodologies based on remote sensing have emerged for the leaf area index (LAI) and the canopy chlorophyll content (CCC) estimation, essential biophysical parameters for crop evapotranspiration monitoring. Using Sentinel-2 (S2) spectral information, this studyperformeda comparative analysis of empirical (vegetation indices), semi-empirical (CLAIR model with fixed and calibrated extinction coefficient) and artificial neural network S2 products derived from the Sentinel Application Platform Software (SNAP) biophysical processor (ANN S2 products) approaches for the estimation of LAI and CCC. Four independent in situ collected datasets of LAI and CCC, obtained with standard instruments (LAI-2000, SPAD) and a smartphone application (PocketLAI), were used. The ANN S2 products present good statistics for LAI (R2 > 0.70, root mean square error (RMSE) 0.75, RMSE < 0.68 g/m2) retrievals. The normalized Sentinel-2 LAI index (SeLI) is the index that presents good statistics in each dataset (R2 > 0.71, RMSE < 0.78) and for the CCC, the ratio red-edge chlorophyll index (CIred-edge) (R2 > 0.67, RMSE < 0.62 g/m2). Both indices use bands located in the red-edge zone, highlighting the importance of this region. The LAI CLAIR model with a fixed extinction coefficient value produces a R2 > 0.63 and a RMSE < 1.47 and calibrating this coefficient for each study area only improves the statistics in two areas (RMSE 0.70). Finally, this study analyzed the influence of the LAI parameter estimated with the different methodologies in the calculation of crop potential evapotranspiration (ETc) with the adapted Penman–Monteith (FAO-56 PM), using a multi-temporal dataset. The results were compared with ETc estimated as the product of the reference evapotranspiration (ETo) and on the crop coefficient (Kc) derived fromFAO table values. In the absence of independent reference ET data, the estimated ETc with the LAI in situ values were considered as the proxy of the ground-truth. ETc estimated with the ANN S2 LAI product is the closest to the ETc values calculated with the LAI in situ (R2 > 0.90, RMSE < 0.41 mm/d). Our findings indicate the good validation of ANN S2 LAI and CCC products and their further suitability for the implementation in evapotranspiration retrieval of agricultural areas