Tropical moist forests face persistent threats from human activities, necessitating their continuous monitoring. As optical satellite assessments struggle with cloud cover, Sentinel-1 (S1) C-band SAR archive provides a reliable alternative for yearly-based forest loss evaluation. This study introduces the MeanBthpercentile composites. As a second step, the proposed method is calibrated and assessed on two distinct regions of the Democratic Republic of Congo to assess its transferability. Ongoing calibration results reveal a significant reduction in false positives, with a peak F-score of 76.46%, with balanced false positive and false negative, ensuring a relevant estimation of the forest loss surface area. Ongoing accuracy assessments of the index from independent optical data promise nuanced insights into metric performance, expanding the applicability of the model to diverse landscapes.

This article aims at investigating the hidden Markov model (HMM) approach for the automated processing of classified satellite images for land cover and land-use change (LCLUC). HMM’s account for transitions between classes at the same location, but that cannot be directly observed due to classification errors. Using a set of transition and emission probabilities, HMM’s allow filtering out errors and recovering the actual sequence of LCLUC, which are typically overestimated when directly estimated from the classified images. After presenting the HMM framework, the methodology is illustrated on three 300-m annual time series of classified images from 2003 to 2019 over$756\times756$km2areas in Brazil, People’s Republic of China, and Mali. It is shown how the emission and transition probabilities can be estimated from these time series using a simple Viterbi training, alleviating computationally demanding algorithms. Special attention is paid to the processing of missing observations caused by clouds. Combining these three datasets with a simulation study, it is concluded that the HMM emission and transition probabilities can be estimated with low biases and variances thanks to the vast number (hundreds of thousands) of pixels at hand. The speed of the Viterbi training and decoding steps makes it possible to consider large-scale land cover mapping at moderate or even high spatial resolution as long as the legend of the LCLUC involves a reasonable number of classes like the six main Intergovernmental Panel on Climate Change (IPCC) land categories.

Green area index (GAI) is a key indicator of crop status and is therefore fundamental for crop monitoring and yield forecasting. A common tool for its retrieval from synthetic aperture radar (SAR) data in an operational context is the inversion of the Water Cloud Model (WCM). This semi-empirical model, however, does not specifically account for the anisotropy induced by the maize rows. This study makes use of extensive ground-truth measurements and synchronous high-resolution fully-polarimetric airborne SAR data in L-band to show the impact of the maize row orientation relative to the SAR sensor beam on backscattering measurements and on GAI retrieval accuracy via the WCM. However, with respect to the latter, a larger data set with a broader range of SAR and in situ measurement values would be needed to support these results.

Theoretical studies have shown that the use of simultaneous mono- and bistatic synthetic aperture radar (SAR) data could be beneficial to agriculture and soil moisture monitoring. This study makes use of extensive ground-truth measurements and synchronous high-resolution fully-polarimetric mono- and bistatic airborne SAR data in L-band to assess and compare the sensitivity of mono- and multistatic systems to maize crop variables, soil moisture, and surface roughness. Its results suggest that bistatic data, even with a very small bistatic angle, provide valuable additional information for maize crop biophysical parameter retrieval. However, this does not appear to be the case for soil moisture retrieval over bare soils.

Climate and biodiversity challenges require a precise characterization of tropical rainforests in terms of floristic, physiognomy and carbon. This study over the Congo Basin proposes to investigate the compatibility of forest type mapping with the lastly biophysical products such as the tree height, density and maximum potential diameter. First, a map at 20m spatial resolution based on Sentinels data depicting 13 forest types within the humid forest biome of the Central Africa is described. A regionally harmonized typology has been defined with the national experts in the context of the Observatoire des Forêts d’ Afrique Centrale. A crucial mapping step was the production of a cloud-free artifact-free Sentinel-2 mosaic for one of the cloudiest regions in the world. Secondly, the biophysical variables are analyzed for each of the different forest types highlighting their complementarities and their discrepancies and characterizing further in details the diversity of forest stands in Central Africa.

Measures to slow the spread of COVID-19 are affecting the food supply chain in many ways including the availability of inputs, labor, transport, and cross-border trade. Earth Observation (EO) from satellites can provide timely and transparent evidence on the extent and impact these measures have on the agricultural activities and related food systems. EO capabilities required to address information needs related to global food supply, national harvesting statistics, national relief programs and labor-intensive crop production were made available over the trilateral COVID-19 Earth Observing Dashboard. The presented use cases make full use of the combined satellites fleet of NASA, ESA and JAXA as well as the expertise of the EO community.

Different aspects of the Earth's surface, including land cover and land cover change, are now mapped at a global scale regularly. The endorsement of one cartographic product among all by users depends notably on their quality. Recommendations on their validation were established by the Committee on Earth Observation Satellites Working Group on Calibration and Validation. These have been applied commonly to the validation of global land cover products. The validation of land cover change at the global scale is still in its early stages. Clear recommendations on the systematic comparison of products against each other have yet to be defined and recognized as standards. Here, we share the lessons learned from the European Space Agency Climate Change Initiative Medium and High-Resolution land cover projects in applying these recommendations and tailoring sampling schemes to the validation of the land cover itself, the land cover change and inter-product comparisons.

Grassland use intensity has an impact on their ecological value as habitats. The precocity and frequency of mowing events are major factors of grassland use intensity. Grassland mowing detection through remote sensing can thereby be a great asset for large scale habitat monitoring. A grassland mowing product, based on Sentinel-1 and Sentinel-2 time series, was developed recently in the frame of ESA's Sentinels for Common Agricultural Policy (Sen4CAP) project. The aim of this study is to assess the performances of this Sen4CAP mowing algorithm on managed grasslands in Belgium. Based on a large reference data set, collected through field observations in 2019, this study shows that the product detects 79% mowing events in managed grasslands and that its confidence level estimation is strongly correlated to the detection precision. Overall, the Sen4CAP grassland mowing product represents a great potential for grassland use intensity assessment in the context of large scale monitoring of biodiversity habitat.

Cloudy conditions impede and reduce the utility of optical imagery. With the launch of Sentinel-1A and B, the ongoing availability of RADARSAT-2 imagery, and the expected launch of the RADARSAT Constellation Mission (RCM), dense time series of C-band Synthetic Aperture Radar (SAR) data will now be readily available. For crop classification and mapping, SAR imagery has yet to be used to its full potential and has generally been combined with optical imagery. The JECAM SAR Inter-Comparison Experiment is a multi-year, multi-partner project that aims to compare global methods for SAR-based crop monitoring and inventory. Sets of dense time-series SAR imagery which include RADARSAT-2 and Sentinel-1 data were prepared for this experiment. AAFC's operational Decision Tree (DT) and newly implemented Random Forest (RF) classification methodologies were applied to these SAR only data-stacks, and to optimized, traditional data-stacks of optical/SAR combinations. This paper outlines the results of these dense time-series classifications and how these results were affected by changing numbers of agriculture classes, numbers of available SAR imagery and numbers of training and validation data points for individual crop types. In general, for the dense time-series SAR stacks, overall accuracies of greater than 85%, a typical operational goal, were obtained for 6 of 12 sites. These results have important operational implications for particularly cloudy regions where the availability of optical imagery is limited.

Evolution in the Copernicus Space Component (CSC) is foreseen in the mid-2020s to meet priority Copernicus user needs not addressed by the existing infrastructure, and/or to reinforce services by monitoring capability in the thematic domains of CO2, polar, and agriculture/forestry. This evolution will be synergetic with the enhanced continuity of services for the next generation of CSC. The “High Spatio-Temporal Resolution Land Surface Temperature Monitoring (LSTM) Mission”, identified as one of the CSC Expansion High Priority Candidate Missions (HPCM), currently undergoes an ESA preparatory phase (phase A/B1) study to establish mission feasibility. The LSTM mission shall provide enhanced measurements of land surface temperature with a focus responding to user requirements related to agricultural monitoring.

Agriculture is one of the key areas where Remote Sensing (RS) techniques can be efficiently implemented for solving wide range of tasks (crop mapping, crop monitoring, crop yield forecasting etc.) on regular basis. Sentinel mission represents really new opportunities in agricultural domain - free of charge for non-commercial use satellite images with 10-20 m spatial resolution, 5-day revisit frequency with global coverage and compatibility to the Landsat missions. In this paper we present the results of Sentinel-2 national demonstration project in Ukraine executed during vegetation period of 2016 and coordinated by Universite catholique de Louvain (UCL). Within this demonstration Ukraine was selected as one of three sites for national demonstration due to high variability of agroclimatic conditions, relatively big fields and wide range of major crops over the territory of the country.

Developing better agricultural monitoring capabilities based on Earth Observation data is critical for strengthening food production information and market transparency. In 2014, the European Space Agency launched the Sentinel-2 for Agriculture project which aims at preparing the exploitation of Sentinel-2 data for agriculture monitoring through the development of an open source system able to generate relevant agricultural products. In order to meet this objective, the project carried out a benchmarking exercise to identify the best algorithms that will be in this system. For each product, a minimum of five algorithms were tested over 12 sites globally distributed. This paper gives a general overview of the project and presents in detail the benchmarking.

Crop area extent estimates and crop type maps provide crucial information for agricultural monitoring and management. Remote sensing imagery in general and, more specifically, high temporal and high spatial resolution data as the ones which will be available with upcoming systems such as Sentinel-2 constitute a major asset for this kind of application. The goal of this paper is to assess to which extent state of the art supervised classification methods can be applied to high resolution multi-temporal optical imagery to produce accurate crop type maps at the global scale. Five concurrent strategies for automatic crop type map production have been selected and benchmarked using SPOT4 (Take5) and LANDSAT8 data over 12 test sites spread all over the globe. The results show that a Random Forest classifier operating on linearly temporally gap-filled images can achieve overall accuracies above 80% for most sites. The approach is fully automatic.

The exploitation of new high revisit frequency earth observations by the future Sentinel-2 satellite is clearly an important opportunity for global agricultural monitoring. In this context, the Sentinel-2Agriculture project aims at producing algorithms working on large geographical areas having different climates and different agricultural systems. In the framework of this project, the construction of a near-real-time deliverable cropland mask product has been studied here. A set of 12 selected test sites are used to benchmark the proposed method with regard to the diversity of agro-ecological context, the various landscape patterns, the different agriculture practices and the actual satellite observation conditions. The classification results yield very promising accuracies achieving around 90 % at the end of the agricultural season.

Accessibility is a key driving factor for economic development, social welfare, resources management, and land use planning. In many studies, modeling accessibility relies on proxy variables such as estimated travel time to selected destinations. In developing countries, estimating the travel time is hindered by scarce information about the transportation network, making it necessary to take into account off-network travel coupled with considerations of multimodal options available within the existing network. This research proposes such a hybrid approach that computes the travel time to selected destinations by optimizing together a fully modeled multimodal network and off-network travel. The model was applied in a region around Kisangani located in northeastern Democratic Republic of the Congo. Travel times to Kisangani from the hybrid approach were found to be in close agreement with field-based information (R 2 = 0.98). The developed approach also proved to better support real-world transportation constraints (such as transfer points between travel modes or barriers) than cost-distance-based travel-time modeling. Demonstration results from the hybrid approach highlight the potential for impact assessment of road construction or rehabilitation, development of secondary towns or markets, and for land use planning in general.

This paper presents a multiannual comparison at regional scale of currently available 1-km global leaf area index (LAI) products with crop-specific green area index (GAI) retrieved from 250-m spatial resolution imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS). The crop-specific GAI product benefits from the following extra processing steps: 1) spatial filtering of time series based on pixel purity; 2) transforming the time scale to thermal time; and 3) fitting a canopy structural dynamic model to smooth out the signal. In order to perform a rigorous comparison, these steps were also applied to the 1-km LAI products, namely, MODIS LAI (MCD15) and LAI produced in the CYCLOPES (Carbon cYcle and Change in Land Observational Products from an Ensemble of Satellites) project. A simple indicator was also designed to quantify the increase in temporal smoothness that can thus be obtained. The results confirm that, for winter wheat, the 250-m GAI product provides a more realistic description of the time course of the biophysical variable in terms of reaching higher values, grasping the variability, and providing smoother time series. However, the use of thermal time and pixel purity also improves the temporal consistency and coherence of the 1-km products. Overall, the results of this study suggest that these techniques could be valuable in harmonizing remote sensing data coming from different sources with varying spatial and temporal resolution for enhanced vegetation monitoring.

There is an error in the above-named article [ibid.,vol. 51, no. 4, pp. 2119-2127, Apr. 2013] regarding the definition and the implementation of equation (3), defining the proposed temporal smoothing index (TSI). The correct formula is provided. These corrections do not change any of the general conclusions of the paper, but some of the comments regarding the interpretation of this table are revised.

The ESA Climate Change Initiative land cover project focuses on the deriving land cover information driven by requirements for observing Essential Climate Variables. Consultation mechanisms were established with the climate modelling community in order to identify its specific needs in terms of satellite-based global land cover products. Key findings were the needs for successive land cover maps stable over time. As response, an innovative global land cover mapping approach, based on multi-year MERIS and SPOT-Vegetation datasets is proposed. Pre-processing and classification chains able to handle huge amount of data have been developed and a first global land cover map associated to the 2008-2010 epoch is being produced.

In tropical regions, the seasonal phenology and the interannual variability of carbon fluxes remain poorly understood, and its representation in global vegetation models highly simplified. However, previous field studies have explored the temporal dynamics of Amazonian vegetation and have shown unexpected and significant seasonal pattern in this ecosystem. Moreover, as a major component of the global terrestrial carbon cycle, the phenological behaviour of this tropical rainforest can significantly influence global dynamics of carbon fluxes and climate. In this context, it is crucial to detect the vegetation phenology in this region. However, field studies are rare and provide local information. By contrast, satellite data from medium resolution sensor offer the advantage of spatial and temporal resolution well adapted to phenological studies. Here, we explore the seasonal characterization of leaf phenology in the Amazon basin through vegetation indices, using a long time series of SPOT-VEGETATION data (2000-2010) at a spatial resolution of 1 km. The analysis is performed locally at a fluxtower site (Santarém) and at the basin scale. Temporal profiles are preliminary analyzed concerning potential artifacts on the cycle observed such as aerosols contamination or BRDF effects. The results indicate that the EVI is better suited than the NDVI to follow the vegetation dynamics in this region. An increase of this index is clearly observed during the dry season, suggesting a higher photosynthetic capacity as it matches higher gross primary production values measured from the fluxtower. Comparing with climate conditions, it is suggested that trees tend to produce new leaves in the dry season to optimize access to light and maximize the carbon uptake.

Geographic object-based image analysis is an image-processing method where groups of spatially adjacent pixels are classified as if they were behaving as a whole unit. This approach raises concerns about the way subsequent validation studies must be conducted. Indeed, classical point-based sampling strategies based on the spatial distribution of sample points (using systematic, probabilistic or stratified probabilistic sampling) do not rely on the same concept of objects and may prove to be less appropriate than the methods explicitly built on the concept of objects used for the classification step. In this study, an original object-based sampling strategy is compared with other approaches used in the literature for the thematic accuracy assessment of object-based classifications. The new sampling scheme and sample analysis are founded on a sound theoretical framework based on few working hypotheses. The performance of the sampling strategies is quantified using simulated object-based classifications results of a Quickbird imagery. The bias and the variance of the overall accuracy estimates were used as indicators of the method's benefits. The main advantage of the object-based predictor of the overall accuracy is its performance: for a given confidence interval, it requires fewer sampling units than the other methods. In many cases, this can help to noticeably reduce the sampling effort. Beyond the efficiency, more conceptual differences between point-based and object-based samplings are discussed. First, geolocation errors do not influence the object-based thematic accuracy as they do for point-based accuracy. These errors need to be addressed independently to provide the geolocation precision. Second, the response design is more complex in object-based accuracy assessment. This is interesting for complex classes but might be an issue in case of large segmentation errors. Finally, there is a larger likelihood to reach the minimum sample size for each class with an object-based sampling than in a point-based sampling. Further work is necessary to reach the same suitability than point-based sampling for pixel-based classification, but this pioneer study shows that object-based sampling could be implemented within a statistically sound framework.

This research investigates the adequacy of a remote sensing instrument's spatial resolution for monitoring crop growth over agricultural landscapes with different spatial patterns. The approach is based on the postulate that time series of a subset of pixels can characterize crop growth over a small zone with similar agro-climatic growing conditions. The point spread function (PSF) is explicitly taken into account in order to identify, at different scales, the pixels whose effective instantaneous field of view (EIFOV) falls within the larger fields of the target crop. This pixel sampling approach enables the resolution to be much coarser than what would be recommended by the predominant scale of spatial variation of the image. Since monitoring crop growth is often done to evaluate the total regional crop production, the method is extended to explore the resolution necessary for crop area estimation.

In the description of agricultural soil roughness, the hypothesis of surface isotropy is currently admitted, and linear measurements are often used to characterize the soil roughness considered as a single-scale process. However, multiscale roughness is frequently observed, and tillage practices created oriented roughness. This paper presents a new technique to measure precisely the bidimensional soil roughness. Digital elevation model derived using photogrammetric technique reproduces the millimeter-scale height variations of three different soil surfaces (ploughed, smoothed, and row structured field) over about 8 m2. A single surface measurement is sufficient to accurately measure the soil roughness parameters. Geostatistic parameterization allows the measurement of the roughness anisotropy. For smooth surface, a two-scale roughness is observed. Anisotropy is observed in the larger scale roughness. The proposed method allows the computation of the bidimensional correlation function, which is required by the integral equation method model for the simulation of the SAR signal over anisotropic soil surfaces.

The Globcover initiative comprises the development and demonstration of a service that in first instance produces a global land cover map for year 2005/2006. Globcover uses MERIS fine resolution (300 m) mode data acquired between mid 2005 and mid 2006 and, for maximum user benefit, the thematic legend is compatible with the UN land cover classification system (LCCS). This new product updates and complements the other existing comparable global products, such as the global land cover map at 1 km resolution for the year 2000 (GLC2000) produced by JRC. It is expected to improve such previous global product, in particular because of the finer spatial resolution. The Globcover project is an initiative of ESA in cooperation with an international network of partner including EEA, FAO, GOFC-GOLD, IGBP, JRC and UNEP.

This paper assess the possibilities of the synthetic aperture radar (SAR) sensors currently in orbit for the maize monitoring defining the configurations (polarization and incidence angles at C-band) maximizing the sensitivity to plant growth and reducing the impact of the soil moisture on the signal. Temporal evolution of the signal was simulated in all the possible configurations using the radiative transfer model developed by the University of Rome "Tor Vergata." The input parameters came from an intensive field campaign providing a detailed description of maize crop over the Belgian Loamy site all along the 2003 growing season. The model was validated for vertical (VV) and horizontal (HH) polarization using ERS, ENVISAT, and RADARSAT observations. The C-band SAR signal in single polarization was found to be sensitive to crop growth till the leaf area index (LAI) reached 4.6 m/sup 2//m/sup 2/, while the soil moisture influenced the signal for sparsely vegetated fields (LAI<2.7 m/sup 2//m/sup 2/). Dual-polarizations indexes were found sensitive to maize growth and less sensitive to soil moisture variations. The VV/VH polarization ratios computed from signal recorded at high incidence angle (35/spl deg/ to 45/spl deg/) could be considered to assess the crop growth till LAI reached 4.9 m/sup 2//m/sup 2/ with low sensitivity to soil moisture. At the beginning of growth, the emergence of maize plants could be detected using the copolarized ratio (VV/HH) computed at low incidence angle. These indexes allow discriminating various crop conditions at a given date between fields of a same region.

Several efforts, aimed at developing and refining crop backscattering models, have been done during the last years. Although important advances have been achieved, it is recognized that further work is required, both in the electromagnetic characterization of single scatterers and in the combination of contributions. This work is focused on the description of leaf geometry and of the internal structure of stems. Recently developed routines, able to model the scattering cross sections of curved sheets and hollow cylinders, are adopted for this purpose and run within the multiple-scattering model developed at the University of Rome "Tor Vergata". Input parameters are taken from experimental campaigns. In particular, ground data collected over a maize field at the Central Plain site in 1988, over wheat and maize fields at the Loamy site in 2003, and over wheat fields at the Matera site in 2001 and 2003 are considered. The multitemporal backscattering coefficients at C-band are simulated. The results obtained under different assumptions are compared to each other, and with C-band radar signatures collected over the same fields. The influence of some critical factors, affecting crop backscattering, is discussed. It is demonstrated that a more detailed scatterer characterization may improve the model accuracy, especially in the case of hollow stems.

In the description of soil roughness for SAR monitoring, the hypothesis of surface isotropy was currently admitted and in-situ roughness measurements were recorded along linear profiles. However, This work showed for agricultural soil surfaces this hypothesis is not valid and anisotropic surface must be assumed. Stereoscopic pairs of vertical photographs were acquired to produce digital elevation models (DEM) for the soil roughness description. This work presents a measurements method to describe the bi-dimensional soil roughness and discuss 3 advantages of the photogrammetric method compared to the linear measurements, (i) a high variability of the roughness parameters estimated by linear profiles was observed. Using the DEM, a high number of profiles were drawn for a given direction. A more accurate estimation of rms and correlation length was obtained averaging several profiles, (ii) the anisotropy were measured and represented by a directional variograms. (iii) The anisotropic roughness were decomposed in 2 models: the isotropic random roughness and the anisotropy induced by the sowing rows.

A new method has been developed in order to automatically detect land cover changes in forested areas on a multitemporal dataset. From a multitemporal segmentation on the calibrated reflectance of all images, unchanged but especially the changed stands are accurately delineated. Stands are characterized by features extracted from the reflectance difference images. As these features for the changed objects will appear as outliers with respect to the ones for unchanged objects, they are identified through a multivariate iterative trimming procedure. The method, which was tested in eastern Belgian forest using three SPOT HRV images covering a decade, could detect accurately clearcuts and regenerations on both coniferous and hardwood. The performance of this method of change detection, measured by the detection accuracy, was proved to be higher (85 to 95 %) than a particular multidate classification, named RGB-NDVI (49 to 65%). The originality of this study is (i) the fact that an object-based approach is used instead of the classical pixel-based methods, and (ii) the automation of the process.