EAGLE 2006 – Multi-purpose, multi-angle and multi-sensor in-situ and airborne campaigns over grassland and forest

EAGLE2006 - an intensive field campaign - was carried out in the Netherlands from the 8th until the 18th of June 2006. Several airborne sensors - an optical imaging sensor, an imaging microwave radiometer, and a flux airplane – were used and extensive ground measurements were conducted over one grassland (Cabauw) site and two forest sites (Loobos & Speulderbos) in the central part of the Netherlands, in addition to the acquisition of multi-angle and multi-sensor satellite data. The data set is both unique and urgently needed for the development and validation of models and inversion algorithms for quantitative surface parameter estimation and process studies. EAGLE2006 was led by the Department of Water Resources of the International Institute for Geo-Information Science and Earth Observation and originated from the combination of a number of initiatives coming under different funding. The objectives of the EAGLE2006 campaign were closely related to the objectives of other ESA Campaigns (SPARC2004, Sen2Flex2005 and especially AGRISAR2006). However, one important objective of the campaign is to build up a data base for the investigation and validation of the retrieval of bio-geophysical parameters, obtained at different radar frequencies (X-, C- and L-Band) and at hyperspectral optical and thermal bands acquired over vegetated fields (forest and grassland). As such, all activities were related to algorithm development for future satellite missions such as Sentinels and for satellite validations for MERIS, MODIS as well as AATSR and ASTER thermal data validation, with activities also related to the ASAR sensor on board ESA’s Envisat platform and those on EPS/MetOp and SMOS. Most of the activities in the campaign are highly relevant for the EU GEMS EAGLE project, but also issues related to retrieval of biophysical parameters from MERIS and MODIS as well as AATSR and ASTER data were of particular relevance to the NWO-SRON EcoRTM project, while scaling issues and complementary between these (covering only local sites) and global sensors such as MERIS/SEVIRI, EPS/MetOP and SMOS were also key elements for the SMOS cal/val project and the ESA-MOST DRAGON programme. This contribution describes the mission objectives and provides an overview of the airborne and field campaigns

Target‐oriented habitat and wildlife management: estimating forage quantity and quality of semi‐natural grasslands with Sentinel‐1 and Sentinel‐2 data

Semi‐natural grasslands represent ecosystems with high biodiversity. Their conservation depends on the removal of biomass, for example, through grazing by livestock or wildlife. For this, spatially explicit information about grassland forage quantity and quality is a prerequisite for efficient management. The recent advancements of the Sentinel satellite mission offer new possibilities to support the conservation of semi‐natural grasslands. In this study, the combined use of radar (Sentinel‐1) and multispectral (Sentinel‐2) data to predict forage quantity and quality indicators of semi‐natural grassland in Germany was investigated. Field data for organic acid detergent fibre concentration (oADF), crude protein concentration (CP), compressed sward height (CSH) and standing biomass dry weight (DM) collected between 2015 and 2017 were related to remote sensing data using the random forest regression algorithm. In total, 102 optical‐ and radar‐based predictor variables were used to derive an optimized dataset, maximizing the predictive power of the respective model. High R2 values were obtained for the grassland quality indicators oADF (R2 = 0.79, RMSE = 2.29%) and CP (R2 = 0.72, RMSE = 1.70%) using 15 and 8 predictor variables respectively. Lower R2 values were achieved for the quantity indicators CSH (R2 = 0.60, RMSE = 2.77 cm) and DM (R2 = 0.45, RMSE = 90.84 g/m²). A permutation‐based variable importance measure indicated a strong contribution of simple ratio‐based optical indices to the model performance. In particular, the ratios between the narrow near‐infrared and red‐edge region were among the most important variables. The model performance for oADF, CP and CSH was only marginally increased by adding Sentinel‐1 data. For DM, no positive effect on the model performance was observed by combining Sentinel‐1 and Sentinel‐2 data. Thus, optical Sentinel‐2 data might be sufficient to accurately predict forage quality, and to some extent also quantity indicators of semi‐natural grassland

Optimal Exploitation of the Sentinel-2 Spectral Capabilities for Crop Leaf Area Index Mapping

The continuously increasing demand of accurate quantitative high quality information on land surface properties will be faced by a new generation of environmental Earth observation (EO) missions. One current example, associated with a high potential to contribute to those demands, is the multi-spectral ESA Sentinel-2 (S2) system. The present study focuses on the evaluation of spectral information content needed for crop leaf area index (LAI) mapping in view of the future sensors. Data from a field campaign were used to determine the optimal spectral sampling from available S2 bands applying inversion of a radiative transfer model (PROSAIL) with look-up table (LUT) and artificial neural network (ANN) approaches. Overall LAI estimation performance of the proposed LUT approach (LUTN₅₀) was comparable in terms of retrieval performances with a tested and approved ANN method. Employing seven- and eight-band combinations, the LUTN₅₀ approach obtained LAI RMSE of 0.53 and normalized LAI RMSE of 0.12, which was comparable to the results of the ANN. However, the LUTN50 method showed a higher robustness and insensitivity to different band settings. Most frequently selected wavebands were located in near infrared and red edge spectral regions. In conclusion, our results emphasize the potential benefits of the Sentinel-2 mission for agricultural applications

Evaluation of Sentinel-2 Red-Edge Bands for Empirical Estimation of Green LAI and Chlorophyll Content

ESA’s upcoming satellite Sentinel-2 will provide Earth images of high spatial, spectral and temporal resolution and aims to ensure continuity for Landsat and SPOT observations. In comparison to the latter sensors, Sentinel-2 incorporates three new spectral bands in the red-edge region, which are centered at 705, 740 and 783 nm. This study addresses the importance of these new bands for the retrieval and monitoring of two important biophysical parameters: green leaf area index (LAI) and chlorophyll content (Ch). With data from several ESA field campaigns over agricultural sites (SPARC, AgriSAR, CEFLES2) we have evaluated the efficacy of two empirical methods that specifically make use of the new Sentinel-2 bands. First, it was shown that LAI can be derived from a generic normalized difference index (NDI) using hyperspectral data, with 674 nm with 712 nm as best performing bands. These bands are positioned closely to the Sentinel-2 B4 (665 nm) and the new red-edge B5 (705 nm) band. The method has been applied to simulated Sentinel-2 data. The resulting green LAI map was validated against field data of various crop types, thereby spanning a LAI between 0 and 6, and yielded a RMSE of 0.6. Second, the recently developed “Normalized Area Over reflectance Curve” (NAOC), an index that derives Ch from hyperspectral data, was studied on its compatibility with simulated Sentinel-2 data. This index integrates the reflectance curve between 643 and 795 nm, thereby including the new Sentinel-2 bands in the red-edge region. We found that these new bands significantly improve the accuracy of Ch estimation. Both methods emphasize the importance of red-edge bands for operational estimation of biophysical parameters from Sentinel-2

Estimating leaf area index and aboveground biomass of grazing pastures using Sentinel-1, Sentinel-2 and Landsat images

Grassland degradation has accelerated in recent decades in response to increased climate variability and human activity. Rangeland and grassland conditions directly affect forage quality, livestock production, and regional grassland resources. In this study, we examined the potential of integrating synthetic aperture radar (SAR, Sentinel-1) and optical remote sensing (Landsat-8 and Sentinel-2) data to monitor the conditions of a native pasture and an introduced pasture in Oklahoma, USA. Leaf area index (LAI) and aboveground biomass (AGB) were used as indicators of pasture conditions under varying climate and human activities. We estimated the seasonal dynamics of LAI and AGB using Sentinel-1 (S1), Landsat-8 (LC8), and Sentinel-2 (S2) data, both individually and integrally, applying three widely used algorithms: Multiple Linear Regression (MLR), Support Vector Machine (SVM), and Random Forest (RF). Results indicated that integration of LC8 and S2 data provided sufficient data to capture the seasonal dynamics of grasslands at a 10–30-m spatial resolution and improved assessments of critical phenology stages in both pluvial and dry years. The satellite-based LAI and AGB models developed from ground measurements in 2015 reasonably predicted the seasonal dynamics and spatial heterogeneity of LAI and AGB in 2016. By comparison, the integration of S1, LC8, and S2 has the potential to improve the estimation of LAI and AGB more than 30% relative to the performance of S1 at low vegetation cover (LAI \u3c 2m2/m2, AGB \u3c 500 g/m2) and optical data of LC8 and S2 at high vegetation cover (LAI \u3e 2m2/m2, AGB \u3e 500 g/m2). These results demonstrate the potential of combining S1, LC8, and S2 monitoring grazing tallgrass prairie to provide timely and accurate data for grassland management

Detection of grassland mowing frequency using time series of vegetation indices from Sentinel-2 imagery

5openInternationalItalian coauthor/editorManagement intensity deeply influences meadow structure and functioning, therefore affecting grassland ecosystem services. Conservation and management measures, including European Common Agricultural Policy subsidies, should therefore be based on updated and publicly available data about management intensity. The mowing frequency is a crucial trait to describe meadows management intensity, but the potential of using vegetation indices from Sentinel-2 imagery for its retrieval has not been fully exploited. In this work we developed on the Google Earth Engine platform a four-phases algorithm to identify mowing frequency, including i) vegetation index time-series computing, ii) smoothing and resampling, iii) mowing detection, and iv) majority analysis. Mowing frequency during 2020 of 240 ha of grassland fields in the Italian Alps was used for algorithm optimization and evaluation. Six vegetation indexes (EVI, GVMI, MTCI, NDII, NDVI, RENDVI783.740) were tested as input to the proposed algorithm. The Normalized Difference Infrared Index (NDII) showed the best performance, resulting in mean absolute error of 0.07 and 93% overall accuracy on average at the four sites used for optimization, at pixel resolution. A slightly lower accuracy (mean absolute error = 0.10, overall accuracy = 90%) was obtained aggregating the maps to management parcels. The algorithm showed a good generalization ability, with a similar performance between global and local optimization and an average mean absolute error of 0.12 and an overall accuracy of 89% on average on the sites not used for parameters optimization. The lowest accuracies occurred in intensively managed grasslands surveyed by one satellite orbit only. This study demonstrates the suitability of the proposed algorithm to monitor very fragmented grasslands in complex mountain ecosystems. Google Earth Engine was used to develop the model and will enable researchers, agencies and practitioners to easily and quickly apply the code to map grassland mowing frequency for extensive grasslands protection and conservation, for mowing event verification, or for forage system characterization.openAndreatta, Davide; Gianelle, Damiano; Scotton, Michele; Vescovo, Loris; Dalponte, MicheleAndreatta, D.; Gianelle, D.; Scotton, M.; Vescovo, L.; Dalponte, M

Assessment of maize nitrogen uptake from PRISMA hyperspectral data through hybrid modelling

The spaceborne imaging spectroscopy mission PRecursore IperSpettrale della Missione Applicativa (PRISMA), launched on 22 March 2019 by the Italian Space Agency, opens new opportunities in many scientific domains, including precision farming and sustainable agriculture. This new Earth Observation (EO) data stream requires new-generation approaches for the estimation of important biophysical crop variables (BVs). In this framework, this study evaluated a hybrid approach, combining the radiative transfer model PROSAIL-PRO and several machine learning (ML) regression algorithms, for the retrieval of canopy chlorophyll content (CCC) and canopy nitrogen content (CNC) from synthetic PRISMA data. PRISMA-like data were simulated from two images acquired by the airborne sensor HyPlant, during a campaign performed in Grosseto (Italy) in 2018. CCC and CNC estimations, assessed from the best performing ML algorithms, were used to define two relations with plant nitrogen uptake (PNU). CNC proved to be slightly more correlated to PNU than CCC (R-2 = 0.82 and R-2 = 0.80, respectively). The CNC-PNU model was then applied to actual PRISMA images acquired in 2020. The results showed that the estimated PNU values are within the expected ranges, and the temporal trends are compatible with plant phenology stages

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