Reviews and syntheses: Remotely sensed optical time series for monitoring vegetation productivity

Vegetation productivity is a critical indicator of global ecosystem health and is impacted by human activities and climate change. A wide range of optical sensing platforms, from ground-based to airborne and satellite, provide spatially continuous information on terrestrial vegetation status and functioning. As optical Earth observation (EO) data are usually routinely acquired, vegetation can be monitored repeatedly over time, reflecting seasonal vegetation patterns and trends in vegetation productivity metrics. Such metrics include gross primary productivity, net primary productivity, biomass, or yield. To summarize current knowledge, in this paper we systematically reviewed time series (TS) literature for assessing state-of-the-art vegetation productivity monitoring approaches for different ecosystems based on optical remote sensing (RS) data. As the integration of solar-induced fluorescence (SIF) data in vegetation productivity processing chains has emerged as a promising source, we also include this relatively recent sensor modality. We define three methodological categories to derive productivity metrics from remotely sensed TS of vegetation indices or quantitative traits: (i) trend analysis and anomaly detection, (ii) land surface phenology, and (iii) integration and assimilation of TS-derived metrics into statistical and process-based dynamic vegetation models (DVMs). Although the majority of used TS data streams originate from data acquired from satellite platforms, TS data from aircraft and unoccupied aerial vehicles have found their way into productivity monitoring studies. To facilitate processing, we provide a list of common toolboxes for inferring productivity metrics and information from TS data. We further discuss validation strategies of the RS data derived productivity metrics: (1) using in situ measured data, such as yield; (2) sensor networks of distinct sensors, including spectroradiometers, flux towers, or phenological cameras; and (3) inter-comparison of different productivity metrics. Finally, we address current challenges and propose a conceptual framework for productivity metrics derivation, including fully integrated DVMs and radiative transfer models here labelled as “Digital Twin”. This novel framework meets the requirements of multiple ecosystems and enables both an improved understanding of vegetation temporal dynamics in response to climate and environmental drivers and enhances the accuracy of vegetation productivity monitoring

Reviews and syntheses:Remotely sensed optical time series for monitoring vegetation productivity

International audienceAbstract. Vegetation productivity is a critical indicator of global ecosystem health and is impacted by human activities and climate change. A wide range of optical sensing platforms, from ground-based to airborne and satellite, provide spatially continuous information on terrestrial vegetation status and functioning. As optical Earth observation (EO) data are usually routinely acquired, vegetation can be monitored repeatedly over time; reflecting seasonal vegetation patterns and trends in vegetation productivity metrics. Such metrics include e.g., gross primary productivity, net primary productivity, biomass or yield. To summarize current knowledge, in this paper, we systematically reviewed time series (TS) literature for assessing state-of-the-art vegetation productivity monitoring approaches for different ecosystems based on optical remote sensing (RS) data. As the integration of solar-induced fluorescence (SIF) data in vegetation productivity processing chains has emerged as a promising source, we also include this relatively recent sensor modality. We define three methodological categories to derive productivity metrics from remotely sensed TS of vegetation indices or quantitative traits: (i) trend analysis and anomaly detection, (ii) land surface phenology, and (iii) integration and assimilation of TS-derived metrics into statistical and process-based dynamic vegetation models (DVM). Although the majority of used TS data streams originate from data acquired from satellite platforms, TS data from aircraft and unoccupied aerial vehicles have found their way into productivity monitoring studies. To facilitate processing, we provide a list of common toolboxes for inferring productivity metrics and information from TS data. We further discuss validation strategies of the RS-data derived productivity metrics: (1) using in situ measured data, such as yield, (2) sensor networks of distinct sensors, including spectroradiometers, flux towers, or phenological cameras, and (3) inter-comparison of different productivity products or modelled estimates. Finally, we address current challenges and propose a conceptual framework for productivity metrics derivation, including fully-integrated DVMs and radiative transfer models here labelled as "Digital Twin". This novel framework meets the requirements of multiple ecosystems and enables both an improved understanding of vegetation temporal dynamics in response to climate and environmental drivers and also enhances the accuracy of vegetation productivity monitoring

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

Towards Detection of Cutting in Hay Meadows by Using of NDVI and EVI Time Series

The main requirement for preserving European hay meadows in good condition is through prerequisite cut management. However, monitoring these practices on a larger scale is very difficult. Our study analyses the use of MODIS vegetation indices products, namely EVI and NDVI, to discriminate cut and uncut meadows in Slovakia. We tested the added value of simple transformations of raw data series (seasonal statistics, first difference series), compared EVI and NDVI, and analyzed optimal periods, the number of scenes and the effect of smoothing on classification performance. The first difference series transformation saw substantial improvement in classification results. The best case NDVI series classification yielded overall accuracy of 85% with balanced rates of producer’s and user’s accuracies for both classes. EVI yielded slightly lower values, though not significantly different, although user accuracy of cut meadows achieved only 67%. Optimal periods for discriminating cut and uncut meadows lay between 16 May and 4 August, meaning only seven consecutive images are enough to accurately detect cutting in hay meadows. More importantly, the 16-day compositing period seemed to be enough for detection of cutting, which would be the time span that might be hopefully achieved by upcoming on-board HR sensors (e.g., Sentinel-2)

Towards Detection of Cutting in Hay Meadows by Using of NDVI and EVI Time Series

The main requirement for preserving European hay meadows in good condition is through prerequisite cut management. However, monitoring these practices on a larger scale is very difficult. Our study analyses the use of MODIS vegetation indices products, namely EVI and NDVI, to discriminate cut and uncut meadows in Slovakia. We tested the added value of simple transformations of raw data series (seasonal statistics, first difference series), compared EVI and NDVI, and analyzed optimal periods, the number of scenes and the effect of smoothing on classification performance. The first difference series transformation saw substantial improvement in classification results. The best case NDVI series classification yielded overall accuracy of 85% with balanced rates of producer’s and user’s accuracies for both classes. EVI yielded slightly lower values, though not significantly different, although user accuracy of cut meadows achieved only 67%. Optimal periods for discriminating cut and uncut meadows lay between 16 May and 4 August, meaning only seven consecutive images are enough to accurately detect cutting in hay meadows. More importantly, the 16-day compositing period seemed to be enough for detection of cutting, which would be the time span that might be hopefully achieved by upcoming on-board HR sensors (e.g., Sentinel-2)

Nationwide indicators reflecting the current problems ofthe agricultural landscape in Slovakia: large agricultural parcels, farm size structure and share of woody vegetation

The main problems of the agricultural landscape in Slovakia are the largest parcels in the EU, the share of agricultural land managed by large agro-holdings, and the low proportion of woody vegetation. The authors have analysed these issues using agricultural beneficiary data and remote sensing products. The parcel size and farm sizes were analysed from agricultural beneficiaries’ data, woody vegetation was analysed from the combined layer of Copernicus products and data from the Land Parcel Identification System. Regarding all three indicators, the situation is more problematic in the lowlands and partially in hilly areas. The proposed indicators could be applied to assess the impact of the new CAP (2023-2027), which supports the reduction of parcel size, improves conditions for small and medium farmers, and improves the share of green infrastructure in agricultural landscapes

Medidas e indicadores relacionados con la fenología a diferentes escalas espaciales: investigación de la información fenológica para la clasificación forestal utilizando datos SPOT VGT y MODIS NDVI

The main objective here is to investigate if leaf phenology indicators as derived from SPOT and MODIS NDVI time series can provide useful information for the detection, characterization and mapping of habitats, with specific reference to the General Habitat Category and Annex I (Natura 2000) schemes. The report is divided into three main parts. Part I ‘’Extraction and analysis of phenology indicators’’, using the Phenolo model of the Joint Research Centre (JRC, Ispra), and a phenological characterization and classification of test sites using Random Forest Classification, and an intercalibration of GHCs with MODIS-derived phenometrics. A set of 31 leaf phenology indicators (phenometrics) was extracted using JRC Phenolo model from time series of NDVI 10 day Maximum Value composites with 6 years of MODIS satellite data and 11 years of SPOT data. Classifications to discriminate deciduous and coniferous forest were performed in selected regions using MODIS satellite data. The main sources identified for low classification accuracy are both the large heterogeneity allowed by the GHC scheme for forests (tree cover proportion), and the low number of training points currently available from field survey. Part II ‘’Multi-temporal analysis of NDVI for grassland mapping and classification’’, focusses on two specific case study areas for grassland mapping. Part III ‘EO time series analysis to identify Annex I habitat types’ describing the processing of MODIS medium resolution time series with the HANTS algorithm and the use of it’s mathematical derived components for the identification of specific Annex I habitats, such as H9150 and H4060. A major limitation to identify those habitats is that the used times series have a minimum spatial resolution of 250 meters which can still be considered as too coarse for most habitats. Next to the fact that not all Annex I habitats have their unique phenology. A GHCs intercalibration strategy integrating EO-based phen

Assessing Non-Photosynthetic Cropland Biomass from Spaceborne Hyperspectral Imagery

Non-photosynthetic vegetation (NPV) biomass has been identified as a priority variable for upcoming spaceborne imaging spectroscopy missions, calling for a quantitative estimation of lignocellulosic plant material as opposed to the sole indication of surface coverage. Therefore, we propose a hybrid model for the retrieval of non-photosynthetic cropland biomass. The workflow included coupling the leaf optical model PROSPECT-PRO with the canopy reflectance model 4SAIL, which allowed us to simulate NPV biomass from carbon-based constituents (CBC) and leaf area index (LAI). PROSAIL-PRO provided a training database for a Gaussian process regression (GPR) algorithm, simulating a wide range of non-photosynthetic vegetation states. Active learning was employed to reduce and optimize the training data set. In addition, we applied spectral dimensionality reduction to condense essential information of non-photosynthetic signals. The resulting NPV-GPR model was successfully validated against soybean field data with normalized root mean square error (nRMSE) of 13.4% and a coefficient of determination (R2) of 0.85. To demonstrate mapping capability, the NPV-GPR model was tested on a PRISMA hyperspectral image acquired over agricultural areas in the North of Munich, Germany. Reliable estimates were mainly achieved over senescent vegetation areas as suggested by model uncertainties. The proposed workflow is the first step towards the quantification of non-photosynthetic cropland biomass as a next-generation product from near-term operational missions, such as CHIME

Comparison of Global and Continental Land Cover Products for Selected Study Areas in South Central and Eastern European Region

Land cover is one of the key terrestrial variables used for monitoring and as input for modelling in support of achieving the United Nations Strategical Development Goals. Global and Continental Land Cover Products (GCLCs) aim to provide the required harmonized information background across areas; thus, they are not being limited by national or other administrative nomenclature boundaries and their production approaches. Moreover, their increased spatial resolution, and consequently their local relevance, is of high importance for users at a local scale. During the last decade, several GCLCs were developed, including the Global Historical Land-Cover Change Land-Use Conversions (GLC), the Globeland-30 (GLOB), Corine-2012 (CLC) and GMES/ Copernicus Initial Operation High Resolution Layers (GIOS). Accuracy assessment is of high importance for product credibility towards incorporation into decision chains and implementation procedures, especially at local scales. The present study builds on the collaboration of scientists participating in the Global Observations of Forest Cover—Global Observations of Land Cover Dynamics (GOFC-GOLD), South Central and Eastern European Regional Information Network (SCERIN). The main objective is to quantitatively evaluate the accuracy of commonly used GCLCs at selected representative study areas in the SCERIN geographic area, which is characterized by extreme diversity of landscapes and environmental conditions, heavily affected by anthropogenic impacts with similar major socio-economic drivers. The employed validation strategy for evaluating and comparing the different products is detailed, representative results for the selected areas from nine SCERIN countries are presented, the specific regional differences are identified and their underlying causes are discussed. In general, the four GCLCs products achieved relatively high overall accuracy rates: 74–98% for GLC (mean: 93.8%), 79–92% for GLOB (mean: 90.6%), 74–91% for CLC (mean: 89%) and 72–98% for GIOS (mean: 91.6%), for all selected areas. In most cases, the CLC product has the lower scores, while the GLC has the highest, closely followed by GIOS and GLOB. The study revealed overall high credibility and validity of the GCLCs products at local scale, a result, which shows expected benefit even for local/regional applications. Identified class dependent specificities in different landscape types can guide the local users for their reasonable usage in local studies. Valuable information is generated for advancing the goals of the international GOFC-GOLD program and aligns well with the agenda of the NASA Land-Cover/Land-Use Change Program to improve the quality and consistency of space-derived higher-level products