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.

Hyperspectral response of agronomic variables to background optical variability: Results of a numerical experiment.

Understanding how biophysical and biochemical variables contribute to the spectral characteristics of vegetation canopies is critical for their monitoring. Quantifying these contributions, however, remains difficult due to extraneous factors such as the spectral variability of canopy background materials, including soil/crop-residue moisture, soil-type, and non-photosynthetic vegetation (NPV). This study focused on exploring the spectral response of two important agronomic variables (1) leaf chlorophyll content (Cab ) and (2) leaf area index (LAI) under various canopy backgrounds through a global sensitivity analysis of wheat-like canopy spectra simulated using the physically-based PROSAIL radiative transfer model. Our results reveal the following general findings: (1) the contribution of each agronomic variable to the simulated canopy spectral signature varies considerably with respect to the background optical properties; (2) the influence of the soil-type and NPV on the spectral response of canopy to Cab and LAI is more significant than that caused by soil/crop-residue moisture; (3) spectral bands at 560 and 704 nm remain sensitive to Cab while being least affected by the impacts of variations in the NPV, soil-type and moisture; (4) the near-infrared (NIR) spectral bands exhibit higher sensitivity to LAI and lower background effects only in the cases of soil/crop-residue moisture but are relatively strongly affected by soil-type and NPV. Comparative analysis of the correlations of twelve widely used vegetation indices with agronomic variables indicates that LICI (LAI-insensitive chlorophyll index) and Macc01 (Maccioni index) are more effective in estimating Cab , while OSAVI (optimized soil adjusted vegetation index) and MCARI2 (modified chlorophyll absorption ratio index 2) are better LAI predictors under the simulated background variability. Overall, our results highlight that background reflectance variability introduces considerable differences in the agronomic variables' spectral response, leading to inconsistencies in the VI- Cab /-LAI relationship. Further studies should integrate these results of spectral responsivity to develop trait-specific hyperspectral inversion models.

Understanding deep learning in land use classification based on Sentinel-2 time series.

The use of deep learning (DL) approaches for the analysis of remote sensing (RS) data is rapidly increasing. DL techniques have provided excellent results in applications ranging from parameter estimation to image classification and anomaly detection. Although the vast majority of studies report precision indicators, there is a lack of studies dealing with the interpretability of the predictions. This shortcoming hampers a wider adoption of DL approaches by a wider users community, as model's decisions are not accountable. In applications that involve the management of public budgets or policy compliance, a better interpretability of predictions is strictly required. This work aims to deepen the understanding of a recurrent neural network for land use classification based on Sentinel-2 time series in the context of the European Common Agricultural Policy (CAP). This permits to address the relevance of predictors in the classification process leading to an improved understanding of the behaviour of the network. The conducted analysis demonstrates that the red and near infrared Sentinel-2 bands convey the most useful information. With respect to the temporal information, the features derived from summer acquisitions were the most influential. These results contribute to the understanding of models used for decision making in the CAP to accomplish the European Green Deal (EGD) designed in order to counteract climate change, to protect biodiversity and ecosystems, and to ensure a fair economic return for farmers.

Near real-time vegetation anomaly detection with MODIS NDVI: Timeliness vs. accuracy and effect of anomaly computation options.

For food crises early warning purposes, coarse spatial resolution NDVI data are widely used to monitor vegetation conditions in near real-time (NRT). Different types of NDVI anomalies are typically employed to assess the current state of crops and rangelands as compared to previous years. Timeliness and accuracy of such anomalies are critical factors to an effective monitoring. Temporal smoothing can efficiently reduce noise and cloud contamination in the time series of historical observations, where data points are available before and after each observation to be smoothed. With NRT data, smoothing methods are adapted to cope with the unbalanced availability of data before and after the most recent data points. These NRT approaches provide successive updates of the estimation of the same data point as more observations become available. Anomalies compare the current NDVI value with some statistics (e.g. indicators of central tendency and dispersion) extracted from the historical archive of observations. With multiple updates of the same datasets being available, two options can be selected to compute anomalies, i.e. using the same update level for the NRT data and the statistics or using the most reliable update for the latter. In this study we assess the accuracy of three commonly employed 1 km MODIS NDVI anomalies (standard scores, non-exceedance probability and vegetation condition index) with respect to (1) delay with which they become available and (2) option selected for their computation. We show that a large estimation error affects the earlier estimates and that this error is efficiently reduced in subsequent updates. In addition, with regards to the preferable option to compute anomalies, we empirically observe that it depends on the type of application (e.g. averaging anomalies value over an area of interest vs. detecting "drought" conditions by setting a threshold on the anomaly value) and the employed anomaly type. Finally, we map the spatial pattern in the magnitude of NRT anomaly estimation errors over the globe and relate it to average cloudiness.

Ensemble approach for potential habitat mapping of invasive Prosopis spp. in Turkana, Kenya.

AIM: Prosopis spp. are an invasive alien plant species native to the Americas and well adapted to thrive in arid environments. In Kenya, several remote-sensing studies conclude that the genus is well established throughout the country and is rapidly invading new areas. This research aims to model the potential habitat of Prosopis spp. by using an ensemble model consisting of four species distribution models. Furthermore, environmental and expert knowledge-based variables are assessed. LOCATION: Turkana County, Kenya. METHODS: We collected and assessed a large number of environmental and expert knowledge-based variables through variable correlation, collinearity, and bias tests. The variables were used for an ensemble model consisting of four species distribution models: (a) logistic regression, (b) maximum entropy, (c) random forest, and (d) Bayesian networks. The models were evaluated through a block cross-validation providing statistical measures. RESULTS: The best predictors for Prosopis spp. habitat are distance from water and built-up areas, soil type, elevation, lithology, and temperature seasonality. All species distribution models achieved high accuracies while the ensemble model achieved the highest scores. Highly and moderately suitable Prosopis spp. habitat covers 6% and 9% of the study area, respectively. MAIN CONCLUSIONS: Both ensemble and individual models predict a high risk of continued invasion, confirming local observations and conceptions. Findings are valuable to stakeholders for managing invaded area, protecting areas at risk, and to raise awareness.