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Temperate forests and grasslands have different drought response strategies. Trees often control their stomata to reduce water loss in order to prevent hydraulic failure and ensure the survival of their aboveground biomass. In contrast, grasses generally have a less strong stomatal control and maintain high photosynthesis and transpiration until the soil moisture gets depleted. That is when their leaves wilt and the grasslands see a reduction in their aboveground green biomass. Both the increased stomatal control and the reduction in aboveground biomass decrease the surface conductance, i.e. decrease the exchange of water and carbon between the leaves and the atmosphere. Therefore, the drought response of vegetation has major impacts on the land-atmosphere fluxes of water, energy, and carbon, as well as the development of droughts and heat waves.Here, we study to which extent the different drought responses of forests and grasslands are reflected in remote sensing data. We hypothesise that (i) for both forests and grasslands, there are drought-induced changes in thermal infrared based data (e.g., land surface temperature), because of the decreased surface conductance for both land cover types. Furthermore, we hypothesise that (ii) drought-induced changes in optical based indices (e.g. the normalized difference vegetation index) can be detected for grasslands but not for forests, because of the different drought response strategies of trees and grasses. In this study we jointly analyze site-scale and remote sensing data. We use eddy-covariance data for 52 forest sites and 11 grassland sites across the northern hemisphere to calculate the surface conductance, and we identify droughts from low soil moisture content and reduced surface conductance. Then we analyse how the drought response is reflected in thermal and optical indices derived from MODIS satellite data.The results show that our hypotheses are largely confirmed. The land surface temperature increases with drought-induced reductions in surface conductance for both forests and grasslands. By contrast, the optical indices show a much stronger response for grasslands than for forests. We conclude that the different canopy-level drought response strategies of trees and grasses are reflected in remote sensing data. Our study highlights that the joint investigation of multiple remote sensing data streams enables insights beyond the analyses of individual indices, such as a better understanding of the drought response strategies across land cover types. Further, a host of different satellite data should be used to monitor and study vegetation drought responses of forests and grasslands to ensure accurate inference on the implications on water, energy, and carbon fluxes.

Agriculture is the largest consumer of water worldwide, accounting for about 70% of the global freshwater withdrawals. Thus, crop water use efficiency and impacts of water stress on crop water consumption are the key concerns for agricultural water management.Present study investigates the variability of evapotranspiration (ET) and crop water use efficiency by integrating very high spatial resolution (1 – 4 m) thermal infrared (TIR) data from airborne measurements and visible to near infrared data from Planet satellite with a numerical water-energy balance model and a diagnostic surface energy balance model. The analysis is done for an intensive agriculture area in central Italy near the city of Modena, where several fruit trees fields are present along with fresh vegetables. An intensive airborne campaign was organized in the summer of 2022 for three consecutive days in July. A hyperspectral TIR camera (Telops Hyper-Cam LW) has been operated at a spectral resolution of 8 cm-1, resulting in 64 wavebands, and covering a wavelength range of 850 cm-1 to 1350 cm-1 (7,39 µm – 11,8 µm). During the 3 days of flight acquisitions, three overpasses per day are planned: 9:00, 12:30 and 16:00 h, respectively and two areas were intensively surveyed at both 4 and 1 m spatial resolution. Planet data at 3.7 m spatial resolution were used to derive different vegetation indices, such as vegetation fraction coverage, NDVI and leaf area index. During airborne overpasses ground data of spectral reflectance, vegetation variables, LST and soil water content (SM) were collected in different fields. In addition, two different pear trees fields were monitored with an eddy covariance station and soil moisture profile measurements, respectively.To investigate the diurnal and spatial patterns of evapotranspiration, soil moisture variability and crop water use efficiency, we used two numerical models: the surface energy balance model STIC based on Penman-Monteith and Shuttleworth-Wallace (Mallick et al., 2018) and the water-energy balance model FEST-EWB which computes continuously in time and is distributed in space soil moisture and evapotranspiration fluxes solving for a land surface temperature that closes the energy–water balance equations (Corbari et al., 2011).Differences and similarities in ET estimates have been analysed from the two models for different soil moisture conditions and crop types, considering crop water use efficiency and water stress, and have been compared to eddy covariance measurements for accuracy evaluation considering both instantaneous and daily data. The assimilation of instantaneous estimates of ET into the water-energy balance model allowed to directly derived soil moisture maps at high spatial resolution which have been found in agreement with ground SM measurements.

Canopy nitrogen content (CNC, kg/ha) provides crucial information for site-specific crop fertilization and the usability of Sentinel-2 (S2) satellite data for CNC monitoring at high fertilization levels in managed agricultural fields is still underexplored. Winter wheat samples were collected in France and Belgium in 2017 (n = 126) and 2018 (n = 18), analysed for CNC and S2-spectra were extracted at the sample locations. A comparison of three established remote sensing methods to retrieve CNC was carried out: (1) look-up-table (LUT) inversion of the canopy reflectance model PROSAIL, (2) Partial Least Square Regression (PLSR) and (3) nitrogen-sensitive vegetation indices (VI). The spatial and temporal model transferability to new data was rigorously assessed. The PROSAIL-LUT approach predicted CNC with a root mean squared error of 33.9 kg/ha on the 2017 dataset and a slightly larger value of 36.8 kg/ha on the 2018 dataset. Contrary, PLSR showed an error of 27.9 kg N/ha (R2 = 0.52) in the calibration dataset (2017) but a substantially larger error of 38.4 kg N/ha on the independent dataset (2018). VIs revealed calibration errors were slightly larger than the PLSR results but showed much higher validation errors for the independent dataset (> 50 kg/ha). The PROSAIL inversion was more stable and robust than the PLSR and VI methods when applied to new data. The obtained CNC maps may support farmers in adapting their fertilization management according to the actual crop nitrogen status.

Hyperspectral cameras onboard unmanned aerial vehicles (UAVs) have recently emerged for monitoring crop traits at the sub-field scale. Different physical, statistical, and hybrid methods for crop trait retrieval have been developed. However, spectra collected from UAVs can be confounded by various issues, including illumination variation throughout the crop growing season, the effect of which on the retrieval performance is not well understood at present. In this study, four retrieval methods are compared, in terms of retrieving the leaf area index (LAI), fractional vegetation cover (fCover), and canopy chlorophyll content (CCC) of potato plants over an agricultural field for six dates during the growing season. We analyzed: (1) The standard look-up table method (LUTstd), (2) an improved (regularized) LUT method that involves variable correlation (LUTreg), (3) hybrid methods, and (4) random forest regression without (RF) and with (RFexp) the exposure time as an additional explanatory variable. The Soil–Leaf–Canopy (SLC) model was used in association with the LUT-based inversion and hybrid methods, while the statistical modelling methods (RF and RFexp) relied entirely on in situ data. The results revealed that RFexp was the best-performing method, yielding the highest accuracies, in terms of the normalized root mean square error (NRMSE), for LAI (5.36%), fCover (5.87%), and CCC (15.01%). RFexp was able to reduce the effects of illumination variability and cloud shadows. LUTreg outperformed the other two retrieval methods (hybrid methods and LUTstd), with an NRMSE of 9.18% for LAI, 10.46% for fCover, and 12.16% for CCC. Conversely, LUTreg led to lower accuracies than those derived from RF for LAI (5.51%) and for fCover (6.23%), but not for CCC (16.21%). Therefore, the machine learning approaches—in particular, RF—appear to be the most promising retrieval methods for application to UAV-based hyperspectral data.

The poster presents the development of the remote sensing forest vitality literature published between 1986-2022.

This article is based upon work from COST Action CA17134 Optical synergies for spatiotemporal SENsing of Scalable ECOphysiological traits (SENSECO), supported by COST. It was supported by European Plant Phenotyping Network (EPPN2020: Grant Agreement 731013), from EMPHASIS-PREP (Grant Agreement: 739514), from EOSC-Life (Grant Agreement: 824087). KB was funded within the EnMAP scientific preparation program (DLR Space and BMWi, Grant Number 50EE1923).

Spatial information on crop status and development is required by agricultural managers for a site specific and adapted management. Here, a prototype of a web application is presented for the visualization of biomass production of maize (Zea mays). The web application displays past biomass development and future predictions for user-defined regions of interest along with summary statistics. Biomass is modelled using the crop growth model (CGM) APSIM (Agricultural Production Systems Simulator) using meteorological data from 2001 to 2014. Information on current crop status and subfield heterogeneity is assimilated into APSIM through high-resolution optical satellite imagery. The use of recent satellite data and regional, historical meteorological data increases the reliability of the biomass information provided. Through its unique combination of high-resolution satellite imagery together with mechanistic crop growth modelling, this web application can overcome the often sparse temporal or sparse spatial resolution of biomass information, which is based on remote sensing images or on crop growth modelling alone. The prototype presented, with its high resolution biomass maps, can be the basis for variable rate application as farmers can react site-specifically to plant development.

Vegetation plays an important role in the exchange of water between the land surface and the atmosphere through evaporation and redistribution of water. Hence, changes in vegetation cover alter the terrestrial hydrological cycle. Large-scale forest restoration is an effective climate change mitigation strategy through carbon sequestration and is expected to impact the water availability. A better understanding of the impact of reforestation is needed, given the numerous different reforestation missions.Our study aims to provide an estimation of the hydrological effects of 900 million hectares of reforestation, called the ‘global tree restoration potential’ (Bastin et al., 2019). We include the effects of forest planting on evaporation and moisture recycling, where evaporation effects local water availability, and moisture recycling effects both local and remote water availability. We used the conventional Budyko’s moisture index framework to calculate the effects of reforestation on evaporation, and afterwards we used the UTrack dataset to calculate the changes in precipitation. The UTrack dataset presents the monthly climatological mean atmospheric moisture flows from evaporation to precipitation and is created using the Lagrangian moisture tracking model UTrack (Tuinenburg et al., 2020).The results show that reforesting the ‘global tree restoration potential’ would effect water availability for most of the Earth’s surface. The global mean increase in terrestrial evaporation is 8 mm yr-1. The increase in evaporation is highest around the equator (on average 20 mm yr-1), with local maximum changes of up to 200 mm yr-1. This is related to a relatively high restoration potential in low latitude areas, and a generally large evaporation response in high precipitation regions. Enhanced moisture recycling has the potential to partly compensate for this decreased water availability by increasing the downwind precipitation. Bastin, J.-F., Finegold, Y., Garcia, C., Mollicone, D., Rezende, M., Routh, D., Zohner, C.M., Crowther, T.W. The global tree restoration potential. Science, 365, 76-79, http://doi.org/10.1126/science.aax0848, 2019.Tuinenburg, O. A., Theeuwen, J. J. E., and Staal, A.: High-resolution global atmospheric moisture connections from evaporation to precipitation, Earth Syst. Sci. Data, 12, 3177–3188, https://doi.org/10.5194/essd-12-3177-2020, 2020.

Unmanned aerial vehicle-based (UAV) hyperspectral imagery is of great significance to estimate crop attributes at a landscape scale, which is required for many environmental and agricultural applications. Multiple methods have been proposed such as empirical regression, radiative transfer, and hybrid models to derive target information (leaf area index (LAI), canopy chlorophyll content (CCC), and fractional vegetation cover (fCover)). Yet, it remains a challenge to select the most suitable method, since each method has its respective advantages and disadvantages. In this study, a hybrid strategy is proposed, as it combines the flexibility of regression with the universality of radiative transfer models (RTM) compared to other retrieval methods concerning model accuracy, computational efficiency under varying sample sizes and different levels of artificial noise. Two datasets of canopy spectra were simulated from two types of Look-up-tables (LUTs) for simulating a range of canopy reflectance-based on a set of input parameters from a Soil-Leaf-canopy RTM. The first type (LUTstd) was derived from a set of independent input parameters, while the other type (LUTreg) relied on the variable correlations by using the Cholesky algorithm. The LUTs were used for training linear and non-linear nonparametric regression algorithms for estimating the relevant parameters for characterizing 27 potato plots. Subsequently, the best approach of non-parametric regression methods was applied to UAV-based hyperspectral data for mapping of crop properties.Results showed for LAI and fCover estimates that the principal component regression, partial least square regression, and least squares regression line (PCR, PLSR and LSLR) outperformed any of machine learning regression algorithms (MLRAs) and LUT inversion approaches. Besides, analysis of multiple LUT sizes ranging from 1000 to 17280 revealed that the 1000 simulations were sufficient for training LUTs. Also, adding 1% of noise to the simulations was adequate to imitate the uncertainty of UAV data. By using the independent ground data for validation, the PCR and PLSR methods yielded the lowest errors (R²= 0.81, NRMSE=11.47% for LUTreg than LUTstd (R²= 0.51, NRMSE= 22.61%). Regarding fCover, the accuracy of linear non-parametric and LUT-inversion approaches in LUTreg (R²=0.75 and NRMSE=14.53% for PLSR and R²= 0.78 and NRMSE=14.37% for LUT inversion based) was increased slightly rather than the results obtained from MLRAs (R²= 0.76 and NRMSE= 14.74% for kernel ridge regression (KRR)). Regarding CCC, the best result was obtained using Random forest of tree bagger (RFTB) and fit ensemble (RFFE) for both LUTs. The accuracy of LUTreg did not improve as much as LUTstd through changing sample sizes (R²= 0.80, NRMSE= 14.71% for LUTreg and R²= 0.81, NRMSE= 13.93% for LUTstd). In terms of processing speed, the linear non-parametric methods were the fastest one as compared to MLRAs (PCR=0.0097 and PLSR=0.013 seconds). In conclusion, compared to the two analyzed hybrid strategies (Linear and non-linear non-parametric regression), the use of LUT-inversion is not recommended for large images because of low prediction accuracy and slow processing speed.Keywords: SLC model, LUT inversion based, linear non-parametric regression, machine learning, hybrid model, Leaf area index, fractional vegetation cover, leaf and canopy chlorophyll content.

We present an implementation of a time series analysis toolbox for remote sensing imagery in R which has been largely funded by the European Space Agency within the PROBA-V MEP Third Party Services project. The toolbox is developed according to the needs of the time series analysis community. The data is provided by the PROBA-V mission exploitation platform (MEP) at VITO. The toolbox largely builds on existing specialized R packages and functions for raster and time series analysis combining these in a common framework.In order to ease access and usage of the toolbox, it has been deployed in the MEP Spark Cluster to bring the algorithm to the data. All functions are also wrapped in a Web Processing Service (WPS) using 52°North’s WPS4R extension for interoperability across web platforms. The WPS can be orchestrated in the Automatic Service Builder (ASB) developed by Space Applications. Hence, the space-time analytics developed in R can be integrated into a larger workflow potentially integrating external data and services. The WPS provides a Webclient including a preview of the results in a map window for usage within the MEP. Results are offered for download or through Web Mapping and Web Coverage Services (WMS, WCS) provided through a Geoserver instance.Through its interoperability features the EOTSA toolbox provides a contribution towards collaborative science.

Vegetation plays a vital role in the ecological functioning of terrestrial and coastal ecosystems. Remote sensing generally provides timely and accurate information to manage ecosystems sustainably and effectively. In this respect, thermal infrared (TIR, 3–14 µm) remote sensing data form a valuable data source for vegetation studies. The TIR data provides unique information compared to other parts of the electromagnetic spectrum. This article aims to gather and review the most relevant information obtained using TIR remote sensing data for terrestrial vegetation at leaf and canopy levels using laboratory/field-based, airborne and spaceborne platforms. We address this topic from various angles, particularly focusing on vegetation discrimination as well as the quantification of water stress by means of canopy temperature and spectral emissivity. In addition, attempts to associate TIR spectral features with vegetation biochemical compounds, as well as the retrieval of vegetation biochemical and biophysical parameters, are reviewed. Research needs and requirements for successful use of remote sensing in vegetation studies across the TIR region, as well as significant challenges, are also discussed. Our review reveals that, despite the increasing interest among remote sensing experts in using TIR data, there are still large gaps in our understanding and interpretation of TIR imagery. Some inconsistent findings and contradictory observations have come to light in different levels (i.e., leaf and canopy levels). In addition, our review shows that airborne and TIR hyperspectral-based studies are currently limited due to cost, particularly across large spatial extents. It can be concluded that TIR remote sensing of vegetation offers unique insights in understanding terrestrial vegetation (e.g., vegetation water stress and retrieval of biophysical parameters). TIR is complementary to other remote sensing data sources, with a high potential for fusing data from different parts of the spectrum. However, we highlight challenges obtaining consistent, meaningful and accurate results for land surface temperature and land surface emissivity retrieval.

There is a need for a better understanding of the link between vegetation characteristics and tree transpiration to facilitate satellite derived transpiration estimation. Many studies use the normalized difference vegetation index (NDVI), a proxy for tree biophysical characteristics, to estimate evapotranspiration. In this study we investigated the link between sap velocity and 30 m resolution Landsat derived NDVI for twenty days during two contrasting precipitation years in a temperate deciduous forest catchment. Sap velocity was measured in the Attert catchment in Luxembourg in 25 plots of 20 × 20 m covering three geologies with sensors installed in 2–4 trees per plot. The results show that sap velocity and NDVI were significantly positively correlated in April, i.e., NDVI successfully captured the pattern of sap velocity during the phase of green-up. After green-up, a significant negative correlation was found during half of the studied days. During a dry period, sap velocity was uncorrelated to NDVI, but influenced by geology and aspect. In summary, in our study area, the correlation between sap velocity and NDVI was not constant, but varied with phenology and water availability. The same behaviour was found for the Enhanced Vegetation Index (EVI). This suggests that methods using NDVI or EVI to predict small-scale variability in (evapo)transpiration should be carefully applied and that NDVI and EVI cannot be used to scale sap velocity to stand level transpiration in temperate forest ecosystems.

Canopy and aerodynamic conductances (gC and gA) are two of the key land surface biophysical variables that control the land surface response of land surface schemes in climate models. Their representation is crucial for predicting transpiration (λET) and evaporation (λEE) flux components of the terrestrial latent heat flux (λE), which has important implications for global climate change and water resource management. By physical integration of radiometric surface temperature (TR) into an integrated framework of the Penman–Monteith and Shuttleworth–Wallace models, we present a novel approach to directly quantify the canopy-scale biophysical controls on λET and λEE over multiple plant functional types (PFTs) in the Amazon Basin. Combining data from six LBA (Large-scale Biosphere-Atmosphere Experiment in Amazonia) eddy covariance tower sites and a TR-driven physically based modeling approach, we identified the canopy-scale feedback-response mechanism between gC, λET, and atmospheric vapor pressure deficit (DA), without using any leaf-scale empirical parameterizations for the modeling. The TR-based model shows minor biophysical control on λET during the wet (rainy) seasons where λET becomes predominantly radiation driven and net radiation (RN) determines 75 to 80 % of the variances of λET. However, biophysical control on λET is dramatically increased during the dry seasons, and particularly the 2005 drought year, explaining 50 to 65 % of the variances of λET, and indicates λET to be substantially soil moisture driven during the rainfall deficit phase. Despite substantial differences in gA between forests and pastures, very similar canopy–atmosphere "coupling" was found in these two biomes due to soil moisture-induced decrease in gC in the pasture. This revealed the pragmatic aspect of the TR-driven model behavior that exhibits a high sensitivity of gC to per unit change in wetness as opposed to gA that is marginally sensitive to surface wetness variability. Our results reveal the occurrence of a significant hysteresis between λET and gC during the dry season for the pasture sites, which is attributed to relatively low soil water availability as compared to the rainforests, likely due to differences in rooting depth between the two systems. Evaporation was significantly influenced by gA for all the PFTs and across all wetness conditions. Our analytical framework logically captures the responses of gC and gA to changes in atmospheric radiation, DA, and surface radiometric temperature, and thus appears to be promising for the improvement of existing land–surface–atmosphere exchange parameterizations across a range of spatial scales.

Canopy chlorophyll content (CCC) is an essential ecophysiological variable for photosynthetic functioning. Remote sensing of CCC is vital for a wide range of ecological and agricultural applications. The objectives of this study were to explore simple and robust algorithms for spectral assessment of CCC. Hyperspectral datasets for six vegetation types (rice, wheat, corn, soybean, sugar beet and natural grass) acquired in four locations (Japan, France, Italy and USA) were analysed. To explore the best predictive model, spectral index approaches using the entire wavebands and multivariable regression approaches were employed. The comprehensive analysis elucidated the accuracy, linearity, sensitivity and applicability of various spectral models. Multivariable regression models using many wavebands proved inferior in applicability to different datasets. A simple model using the ratio spectral index (RSI; R815, R704) with the reflectance at 815 and 704 nm showed the highest accuracy and applicability. Simulation analysis using a physically based reflectance model suggested the biophysical soundness of the results. The model would work as a robust algorithm for canopy-chlorophyll-metre and/or remote sensing of CCC in ecosystem and regional scales. The predictive-ability maps using hyperspectral data allow not only evaluation of the relative significance of wavebands in various sensors but also selection of the optimal wavelengths and effective bandwidths.

Although shrublands, savannas and grasslands account for 37% of the world’s terrestrial area, not many studies have analysed the role of these ecosystems in the global carbon cycle at a regional scale. The MODIS Gross Primary Production (GPP) product is used here to help bridge this gap. In this study, the agreement between the MODIS GPP product (GPPm) and the GPP Eddy Covariance tower data (GPPec) was tested for six different sites in temperate and dry climatic regions (three grasslands, two shrublands and one evergreen forest). Results of this study show that for the non-forest sites in water-limited areas, GPPm is well correlated with GPPec at annual scales (r2 = 0.77, n = 12, SEE = 149.26 g C∙m−2∙year−1), although it tends to overestimate GPP and it is less accurate in the sites with permanent water restrictions. The use of biome-specific models based on precipitation measurements at a finer spatial resolution than the Data Assimilation Office (DAO) values can increase the accuracy of these estimations. The seasonal dynamics and the beginning and end of the growing season were well captured by GPPm for the sites where (i) the productivity was low throughout the year or (ii) the changes in the flux trend were abrupt, usually due to the restrictions in water availability. The agreement between GPPec and GPPm in non-forested sites was lower on a weekly basis than at an annual scale (0.44 ≤ r2 ≤ 0.49), but these results were improved by including meteorological data at a finer spatial scale, and soil water content and temperature measurements in the model developed to predict GPPec (0.52 ≤ r2 ≤ 0.65).

Complex crop growth models (CGM) require a large number of input parameters, which can cause large errors if they are uncertain. Furthermore, they often lack spatial information. The coupling of a CGM with a radiative transfer model offers the possibility to assimilate remote sensing data while taking into account uncertainties in input parameters. A particle filter was used to assimilate satellite data into a CGM coupled with a leaf-canopy radiative transfer model to update biomass simulations of maize. The synthetic experiment set up to test the reliability of the procedure, highlighted the importance of the acquisition time. The real case study with RapidEye observations confirmed these findings. Data assimilation increased the accuracy of biomass predictions in the majority of the six maize fields where biomass validation data was available, with improvements of up to 15%. The smallest and largest errors in biomass prediction after assimilation were 82?kg/ha and 2116?kg/ha, respectively. Furthermore, data assimilation enabled the production of biomass maps showing detailed spatial variability. Data assimilation using a particle filter for biomass estimation was conducted.Proof of concept with synthetic case studies.Multispectral satellite data (visible and near infrared) was found to be suitable for data assimilation.Assimilation of satellite data allowed biomass prediction on a pixel basis.