Project For On-Board Autonomy-Vegetation Research Articles

Fine-scale multi-temporal and spatial analysis of agricultural drought in agro-ecological regions of Zimbabwe

This study utilised multi-temporal satellite data to characterize droughts across the five Agro-ecological zones (AEZ) of Zimbabwe at monthly, seasonal, sub-seasonal and annual scales (2000 to 2018) using a combination of Satellite Pour l'Observation de la Terre (SPOT) Vegetation (VGT) and Project for On-Board Autonomy-Vegetation (PROBA-V) derived Vegetation Condition Index (VCI). The characterized droughts were validated, using maize yield and meteorological station derived Standardised Precipitation Index (SPI). The study showed differential spatial and temporal expression of drought in Zimbabwe, over the 19-year study period. November had the highest frequency of drought while the first quarter of the season experienced more droughts than the second quarter. Spatially, virtually all AEZ in Zimbabwe are prone to droughts and the frequency is increasingly, with the Agro-ecological region five (southern Zimbabwe) being the worst affected. Further, the study found a strong positive relationship between the VCI and maize yield (r = 0.701, P = 0.000), demonstrating the utility of the index in drought monitoring at national level. Overall, findings from this work highlight the significance of using multi-date and national-scale analysis of agricultural drought occurrence and trends. Moreover, this work provides critical baseline information for developing drought resilience and mitigation strategies in drought-prone areas.

Crop Mapping with Combined Use of European and Chinese Satellite Data

Agricultural landscapes are characterized by diversity and complexity, which makes crop mapping at a regional scale a top priority for different purposes such as administrative decisions and farming management. Project 32194 of the Dragon 4 Program was implemented to meet the requirements of crop mapping, with the specific objective to develop suitable approaches for precise crop mapping with combined uses of European and Chinese high- and medium-resolution satellite images. Two sub-projects were involved in the project. The first was to focus on the use of time series high-resolution satellite data, including Sentinel-2 (S2, European satellite data) and Gaofen-1 (GF-1, Chinese satellite data), due to their similar spectral bands for Earth observation, while the second was to focus on medium-resolution data sources, i.e., the European Project for On-Board Autonomy–Vegetation (PROBA-V) and Chinese Fengyun-3 Medium Resolution Spectral Imager (FY-3 MERSI) satellite data, also due to their similar spectral channels. The approach of the European Space Agency (ESA) Sent2Agri project for crop mapping was adapted in the first sub-project and applied to the Yellow River irrigated district (YERID) of Ningxia in northwest China in order to assess its ability to accurately identify crop types in China. The goal of the second sub-project was to explore the potential of both European and Chinese medium-resolution satellite data for crop assessment in a large area. Methods to handle the data and retrieve the required information for the precise crop mapping were developed in the study, including the adaptation of the ESA approach to GF-1 data and the application of algorithms for classification. A scheme for the validation of the crop mapping was developed in the study. The results of implementing the scheme to the YERID in Ningxia indicated that the overall accuracies of crop mapping with S2 and GF-1 can be high, up to 94–97%, and the mapping had an accuracy of 88% with the PROBA-V and FY3B-MERSI data. The very high accuracy suggests the possibility of precise crop mapping with the combined use of time series high- and medium-resolution satellite data when suitable approaches are chosen to handle the data for the classification of crop types.

A New Technique Based on Convolutional Neural Networks to Measure the Energy of Protons and Electrons With a Single Timepix Detector

The Timepix chip has been exposed to the outer space for the first time with the Space Application of Timepix-based Radiation Monitor (SATRAM) instrument on Project for On-Board Autonomy Vegetation (Proba-V), a European Space Agency's (ESA) satellite. The objective of this study is to develop a new technique to improve the separation of protons and electrons, which are detected by the single-layer Timepix detector in SATRAM. The current identification method proposed by Gohl et al. (2019) is based on pattern recognition and stopping power measurements. In this article, the limitations of this method are discussed. A new method based on neural network trained with Geant4 data is proposed. Its validation with SATRAM data is presented. Similarly, a neural network trained with Geant4 data is introduced. Its purpose is to deduce the particles' incident energy using the energy deposited in the Timepix.

Mapping Burned Areas of Mato Grosso State Brazilian Amazon Using Multisensor Datasets

Quantifying forest fires remain a challenging task for the implementation of public policies aimed to mitigate climate change. In this paper, we propose a new method to provide an annual burned area map of Mato Grosso State located in the Brazilian Amazon region, taking advantage of the high spatial and temporal resolution sensors. The method consists of generating the vegetation, soil, and shade fraction images by applying the Linear Spectral Mixing Model (LSMM) to the Landsat-8 OLI (Operational Land Imager), PROBA-V (Project for On-Board Autonomy–Vegetation), and Suomi NPP-VIIRS (National Polar-Orbiting Partnership-Visible Infrared Imaging Radiometer Suite) datasets. The shade fraction images highlight the burned areas, in which values are represented by low reflectance of ground targets, and the mapping was performed using an unsupervised classifier. Burned areas were evaluated in terms of land use and land cover classes over the Amazon, Cerrado and Pantanal biomes in the Mato Grosso State. Our results showed that most of the burned areas occurred in non-forested areas (66.57%) and old deforestation (21.54%). However, burned areas over forestlands (11.03%), causing forest degradation, reached more than double compared with burned areas identified in consolidated croplands (5.32%). The results obtained were validated using the Sentinel-2 data and compared with active fire data and existing global burned areas products, such as the MODIS (Moderate Resolution Imaging Spectroradiometer product) MCD64A1 and MCD45A1, and Fire CCI (ESA Climate Change Initiative) products. Although there is a good visual agreement among the analyzed products, the areas estimated were quite different. Our results presented correlation of 51% with Sentinel-2 and agreement of r2 = 0.31, r2 = 0.29, and r2 = 0.43 with MCD64A1, MCD45A1, and Fire CCI products, respectively. However, considering the active fire data, it was achieved the better performance between active fire presence and burn mapping (92%). The proposed method provided a general perspective about the patterns of fire in various biomes of Mato Grosso State, Brazil, that are important for the environmental studies, specially related to fire severity, regeneration, and greenhouse gas emissions.

Determining paddy field spatiotemporal distribution and temperature influence using remote sensing in Songnen Plain, Northeastern China

This study investigates changes in the spatial distributions of paddy fields in the Songnen Plain (China’s largest commodity grain-producing area) using the Satellite Pour l’Observation de la Terre–Vegetation (SPOT VEGETATION) time series product data to simulate the phenological characteristics of paddy rice growth. According to the analysis, the characteristics of paddy fields are different from other vegetation cover around late May (15th 10-day). This difference can be used to improve accuracy in identifying paddy fields. Historical maps (1999–2013) are extracted successfully in this work. Based on the results, a trend map for changes in paddy field areas is generated using a 10-km grid. Based on the verification of paddy field extractions in 2019 using the Project for On-Board Autonomy-Vegetation (PROBA VEGETATION), the trend map is strongly indicative for predicting future spatial distribution changes of paddy fields. These results can be used to provide a reliable theoretical basis for environmental research related to the growth of paddy rice. This study also verifies that paddy rice cultivation is one of the main causes of rising temperatures in the study area.

Maximum Fraction Images Derived from Year-Based Project for On-Board Autonomy-Vegetation (PROBA-V) Data for the Rapid Assessment of Land Use and Land Cover Areas in Mato Grosso State, Brazil

This paper presents a new approach for rapidly assessing the extent of land use and land cover (LULC) areas in Mato Grosso state, Brazil. The novel idea is the use of an annual time series of fraction images derived from the linear spectral mixing model (LSMM) instead of original bands. The LSMM was applied to the Project for On-Board Autonomy-Vegetation (PROBA-V) 100-m data composites from 2015 (~73 scenes/year, cloud-free images, in theory), generating vegetation, soil, and shade fraction images. These fraction images highlight the LULC components inside the pixels. The other new idea is to reduce these time series to only six single bands representing the maximum and standard deviation values of these fraction images in an annual composite, reducing the volume of data to classify the main LULC classes. The whole image classification process was conducted in the Google Earth Engine platform using the pixel-based random forest algorithm. A set of 622 samples of each LULC class was collected by visual inspection of PROBA-V and Landsat-8 Operational Land Imager (OLI) images and divided into training and validation datasets. The performance of the method was evaluated by the overall accuracy and confusion matrix. The overall accuracy was 92.4%, with the lowest misclassification found for cropland and forestland (<9% error). The same validation data set showed 88% agreement with the LULC map made available by the Landsat-based MapBiomas project. This proposed method has the potential to be used operationally to accurately map the main LULC areas and to rapidly use the PROBA-V dataset at regional or national levels.

Coastal Turbidity Derived From PROBA-V Global Vegetation Satellite

PROBA-V (Project for On-Board Autonomy-Vegetation) is a global vegetation monitoring satellite. The spectral quality of the data and the coverage of PROBA-V over coastal waters provide opportunities to expand its use to other applications. This study tests PROBA-V data for the retrieval of turbidity in the North Sea region. In the first step, clouds were masked and an atmospheric correction, using an adapted version of iCOR, was performed. The resulted water leaving radiance reflectance was validated against AERONET-OC stations, yielding a coefficient of determination of 0.884 in the RED band. Next, turbidity values were retrieved using the RED band. The PROBA-V retrieved turbidity data was compared with turbidity data from CEFAS Smartbuoys and ad-hoc measurement campaigns. This resulted in a coefficient of determination of 0.69. Finally, a time series of 1.5 year of PROBA-V derived turbidity data was plotted over MODIS data to check consistencies in both datasets. Seasonal dynamics were noted with high turbidity in autumn and winter and low values in spring and summer. For low values, PROBA-V and MODIS yielded similar results, but while MODIS seems to saturate around 50 FNU, PROBA-V can reach values up till almost 80 FNU.

Evaluation of NDVI Estimation Considering Atmospheric and BRDF Correction through Himawari-8/AHI

Satellite-based vegetation indices are an essential element in understanding the Earth’s surface. In this study, we estimated the normalized difference vegetation index (NDVI) using Himawari-8/Advanced Himawari Imager (AHI) data and analyzed the sensitivity of products to atmospheric and surface correction. We used the Second Simulation of a Satellite Signal in the Solar Spectrum (6S) radiative transfer model for atmospheric correction, and kernel-based semi-empirical bidirectional reflectance distribution function (BRDF) model to remove surface anisotropic effects. From this, top-of-atmosphere, top-of-canopy, and normalized NDVIs were produced. A sensitivity analysis showed that the normalized NDVI had the lowest number of missing values compared with the others and almost no low peaks during the study period. These results were validated by Terra and Aqua/Moderate Resolution Imaging Spectroradiometer (MODIS) and Project for On-Board Autonomy/Vegetation (PROBA) NDVI product, showing the root mean square error (RMSE) and bias of 0.09 and + 0.04 (MODIS) and 0.09 and − 0.04 (PROBA), respectively. These results also satisfied the FP7 Geoland2/BioPar project-defined user requirements (threshold: 0.15; target: 0.10).

MODIS and PROBA-V NDVI Products Differ when Compared with Observations from Phenological Towers at Four Tropical Dry Forests in the Americas

The Normalized Difference Vegetation Index (NDVI) is widely used to monitor vegetation phenology and productivity around the world. Over the last few decades, phenology monitoring at large scales has been possible due to the information and metrics derived from satellite sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS) or the Project for On-Board Autonomy–Vegetation (PROBA-V). However, due to their temporal and spatial resolution, adequate ground comparison is lacking. In this paper, we analyze how NDVI products from MODIS (Aqua and Terra) and PROBA-V predict vegetation phenology when compared with near-surface observations. We conduct this comparison at four tropical dry forests (TDFs) in the Americas. We undertake this study by comparing the following: (i) Dissimilarities of the standardized NDVI (NDVIS) using dynamic time warping, (ii) the differences of daily NDVIS between seasons and ENSO months using generalized linear models, and (iii) phenometrics derived from NDVI time series. Overall, our results suggest that NDVIS from satellite observations present DTW distances (dissimilarities) between 2.98 and 46.57 (18.91 ± 12.31) when compared with near-surface observations. Furthermore, NDVIS comparisons reveal that overall differences between satellite and near-surface observations are close to zero, but this tends to differ between seasons or when El Nino Southern Oscillation (ENSO) is present. Phenometrics comparisons show that metrics derived from satellite observations such as green-up, maturity, and start and end of the wet season strongly correlate with those from near-surface observations. In contrast, phenometrics that describe the day of the highest or lowest NDVI tend to be inconsistent with those from near-surface observations. All findings were observed independently of the NDVI source. Our results suggest that satellite-based NDVI products tend to be inconsistent descriptors of vegetation events on tropical deciduous forests in comparison with near-surface observations. These results reinforce the idea that satellite-based NDVI products should be used and interpreted with great caution and only in ecosystems with well-established knowledge of their vegetation phenology.

A Combination of PROBA-V/MODIS-Based Products with Sentinel-1 SAR Data for Detecting Wet and Dry Snow Cover in Mountainous Areas

In the present study, we explore the value of employing both vegetation indexes as well as land surface temperature derived from Project for On-Board Autonomy—Vegetation (PROBA-V) and Moderate Resolution Imaging Spectroradiometer (MODIS) sensors, respectively, to support the detection of total (wet + dry) snow cover extent (SCE) based on a simple tuning machine learning approach and provide reliability maps for further analysis. We utilize Sentinel-1-based synthetic aperture radar (SAR) observations, including backscatter coefficient, interferometric coherence, and polarimetric parameters, and four topographical factors as well as vegetation and temperature information to detect the total SCE with a land cover-dependent random forest-based approach. Our results show that the overall accuracy and F-measure are over 90% with an ’Area Under the receiver operating characteristic Curve (ROC)’ (AUC) score of approximately 80% over five study areas located in different mountain ranges, continents, and hemispheres. These accuracies are also confirmed by a comprehensive validation approach with different data sources, attesting the robustness and global transferability. Additionally, based on the reliability maps, we find an inversely proportional relationship between classification reliability and vegetation density. In conclusion, comparing to a previous study only utilizing SAR-based observations, the method proposed in the present study provides a complementary approach to achieve a higher total SCE mapping accuracy while maintaining global applicability with reliable accuracy and corresponding uncertainty information.

Satellite data integration for soil clay content modelling at a national scale

Soil clay content is a key parameter that influences many other soil properties and processes. The potential of adding new and contemporary satellite data for soil property mapping in France is assessed in this study. The soil property maps used for this analysis were produced within the framework of GlobalSoilMap, which was created to deliver global fine grids of soil properties and associated uncertainties using existing soil information and ancillary data to predict these properties based on digital soil mapping techniques. In this study, we evaluate the added value of Moderate Resolution Imaging Spectroradiometer (MODIS), Project for On-Board Autonomy-Vegetation (PROBA-V), and Sentinel-2 (S2) data for predicting the soil clay content at 90 m resolution for mainland France. The rationale behind adding these data is that satellite images and derived products may enable the biogeochemical characteristics of the earth’s surface to be captured more effectively, which in turn enables more precise predictions of the soil clay content. For this methodology, we i) create composite bare soil mosaics and derive the spectral indices from S2 data acquired during sowing periods from 2016 to 2017, ii) extract the first three principal components of harmonized MODIS and PROBA-V normalized difference vegetation index (NDVI) time series acquired in 2003 and 2016 to represent vegetation changes, and iii) test whether the complementary datasets are able to improve the soil clay information compared to a benchmark value. The soil clay content is obtained by using quantile regression forest (QRF) for each GlobalSoilMap depth interval of 0–5 cm, 5–15 cm, 15–30 cm, 30–60 cm, 60–100 cm, and 100–200 cm along with a 10-fold cross-validation having 10 replicates. The results show that the complementary satellite data improve the clay content estimation on bare soil for the topsoil layers (e.g., 0–30 cm) by increasing the R² and decreasing the bias at averages of 0.05 and 1 g kg−1, respectively. Moreover, the first principal component of the harmonized NDVI data is shown to be the second most important variable for estimating the clay content, as indicated by the QRF models. However, the use of only the satellite data and products as input for the QRF does not yield a satisfactory estimate of the clay content. Finally, this work provides a reference for embedding new remote sensing data in existing national soil inventories and national soil information systems. Further research should incorporate new techniques for considering the spatial–temporal variability of the earth’s surface parameters such as soil moisture and roughness.

Evaluation of PROBA-V Collection 1: Refined Radiometry, Geometry, and Cloud Screening

PROBA-V (PRoject for On-Board Autonomy–Vegetation) was launched in May-2013 as an operational continuation to the vegetation (VGT) instruments on-board the Système Pour l’Observation de la Terre (SPOT)-4 and -5 satellites. The first reprocessing campaign of the PROBA-V archive from Collection 0 (C0) to Collection 1 (C1) aims at harmonizing the time series, thanks to improved radiometric and geometric calibration and cloud detection. The evaluation of PROBA-V C1 focuses on (i) qualitative and quantitative assessment of the new cloud detection scheme; (ii) quantification of the effect of the reprocessing by comparing C1 to C0; and (iii) evaluation of the spatio-temporal stability of the combined SPOT/VGT and PROBA-V archive through comparison to METOP/advanced very high resolution radiometer (AVHRR). The PROBA-V C1 cloud detection algorithm yields an overall accuracy of 89.0%. Clouds are detected with very few omission errors, but there is an overdetection of clouds over bright surfaces. Stepwise updates to the visible and near infrared (VNIR) absolute calibration in C0 and the application of degradation models to the SWIR calibration in C1 result in sudden changes between C0 and C1 Blue, Red, and NIR TOC reflectance in the first year, and more gradual differences for short-wave infrared (SWIR). Other changes result in some bias between C0 and C1, although the root mean squared difference (RMSD) remains well below 1% for top-of-canopy (TOC) reflectance and below 0.02 for the normalized difference vegetation index (NDVI). Comparison to METOP/AVHRR shows that the recent reprocessing campaigns on SPOT/VGT and PROBA-V have resulted in a more stable combined time series.

Topography of the intertidal zone along the shoreline of Chittagong (Bangladesh) using PROBA-V imagery

ABSTRACTAcross the oceans shorelines, monitoring the topography of the intertidal zone is generally challenging. The present study is motivated by the recognized role of the intertidal topography in the near-shore hydrodynamics. We consider the region of Chittagong (northern Bay of Bengal) because of its propensity to powerful cyclone surges and associated inundation hazard. So as to curb the lack of in situ knowledge of intertidal topography, we present an original procedure relying on spaceborne optical imagery. Our method essentially amounts to a water line detection performed at various tidal levels. We apply our procedure to the recent PROBA-V (Project for On-Board Autonomy-Vegetation) multi-spectral imagery mission. The first step of our procedure concerns the shoreline extraction. PROBA-V imagery consists of four bands (Red, Blue, near-infrared – NIR, short-wave infrared – SWIR), which are then combined to generate an artificial red-green-blue (RGB) image. This RGB image is then converted into the hue-saturation-value (HSV) colour space. A simple thresholding is applied to hue and value channels to separate water masses from land masses. This process is applied to several images taken at different water levels (i.e. different parts of the tidal cycle) and the corresponding water lines are inferred. To estimate the altitude level of the water lines, we rely on tidal observations from two gauges located at Chittagong and Cox’s Bazar. We operate an ad-hoc extrapolation of the point-wise gauge data to generate a synthetic tidal water level record all along the shoreline. These synthetic tidal heights are then combined with the shorelines to generate the final digital elevation model (DEM). The DEM we generated covers a 40-km long stretch of shoreline around Chittagong city. We assessed this DEM by comparison with two independent data sets based on in situ surveys as well as on Pléiades spaceborne stereoscopy. We conclude that our DEM is accurate within 1 m to 2 m, which is within the error bar of these validation data sets. Our procedure being essentially objective, it is easy to automate, for processing of other imagery satellite, including at high resolution and/or in real time.

Mapping of peanut crops in Queensland, Australia, using time-series PROBA-V 100-m normalized difference vegetation index imagery

Mapping of peanut crops is essential in supporting peanut production, yield prediction, and commodity forecasting. While ground-based surveys can be used over small areas, the development of remote-sensing technologies could provide rapid and inexpensive crop area estimates with high accuracy over large regions. Some of these recent earth observation satellite systems, such as the Project for On-Board Autonomy Vegetation (PROBA-V), have the advantage of increased spatial and temporal resolution. With a study area located in the South Burnett region, Queensland, Australia, the primary aim of this study was to assess the ability of time-series PROBA-V 100-m normalized difference vegetation index (NDVI) for peanut crop mapping. Two datasets, i.e., PROBA-V NDVI time-series imagery and the corresponding phenological parameters generated from TIMESAT data analysis technique, were classified using maximum likelihood classification, spectral angle mapper, and minimum distance classification algorithms. The results show that among all methods used, the application of MLC in PROBA-V NDVI time series produced very good overall accuracy, i.e., 92.75%, with producer and user accuracy of each class ≥78.79 % . For all algorithms tested, the mapping of peanut cropping areas produced satisfactory classification results, i.e., 75.95% to 100%. Our study confirmed that the use of finer resolution 100 m of PROBA-V imagery (i.e., relative to MODIS 250-m data) has contributed to the success of mapping peanut and other crops in the study area.

Burned Area Mapping in the Brazilian Savanna Using a One-Class Support Vector Machine Trained by Active Fires

We used the Visible Infrared Imaging Radiometer Suite (VIIRS) active fire data (375 m spatial resolution) to automatically extract multispectral samples and train a One-Class Support Vector Machine for burned area mapping, and applied the resulting classification algorithm to 300-m spatial resolution imagery from the Project for On-Board Autonomy-Vegetation (PROBA-V). The active fire data were screened to prevent extraction of unrepresentative burned area samples and combined with surface reflectance bi-weekly composites to produce burned area maps. The procedure was applied over the Brazilian Cerrado savanna, validated with reference maps obtained from Landsat images and compared with the Collection 6 Moderate Resolution Imaging Spectrometer (MODIS) Burned Area product (MCD64A1) Results show that the algorithm developed improved the detection of small-sized scars and displayed results more similar to the reference data than MCD64A1. Unlike active fire-based region growing algorithms, the proposed approach allows for the detection and mapping of burn scars without active fires, thus eliminating a potential source of omission error. The burned area mapping approach presented here should facilitate the development of operational-automated burned area algorithms, and is very straightforward for implementation with other sensors.

Mapping Winter Wheat Biomass and Yield Using Time Series Data Blended from PROBA-V 100- and 300-m S1 Products

Monitoring crop areas and yields is crucial for food security and agriculture management across the world. In this paper, we mapped the biomass and yield of winter wheat using the new Project for On-Board Autonomy-Vegetation (PROBA-V) products in the North China Plain (NCP). First, the daily 100-m land surface reflectance was generated by fusing the PROBA-V 100-m and 300-m S1 products. Our results show that the blended data exhibited high correlations with the referenced data (0.71 ≤ R2 ≤ 0.94 for the red band, 0.50 ≤ R2 ≤ 0.95 for the near-infrared band, and 0.88 ≤ R2 ≤ 0.97 for the shortwave infrared band). The time-series Normalized Difference Vegetation Index (NDVI) derived from the synthetic reflectance was then clustered for winter wheat identification. The overall classification accuracy was between 78% and 87%, with a kappa coefficient above 0.57, which was 10%–20% higher than the classification accuracy using the 300-m data. Finally, a light use efficiency model was employed to estimate the biomass and yield. The estimation results were closely related to the field-measured biomass and yield, with high R2 and low root mean square errors (RMSE) (0.864 ≤ R2 ≤ 0.871 and 168 ≤ RMSE ≤ 191 g/m2 for biomass; and 0.631 ≤ R2 ≤ 0.663 and 41.8 ≤ RMSE ≤ 62.8 g/m2 for yield). This paper shows the strong potential of using PROBA-V 100-m data to enhance the spatial resolution of PROBA-V 300-m data and because the proposed framework in this study was based only on the relatively high spatio-temporal resolution PROBA-V data and achieved favorable results, it provides a novel approach for crop areas and yields estimation utilizing the relatively new data set.

Assessment of Automated Snow Cover Detection at High Solar Zenith Angles with PROBA-V

Changes in the snow cover extent are both a cause and a consequence of climate change. Optical remote sensing with heliosynchronous satellites currently provides snow cover data at high spatial resolution with daily revisiting time. However, high latitude image acquisition is limited because reflective sensors of many satellites are switched off at high solar zenith angles (SZA) due to lower signal quality. In this study, the relevance and reliability of high SZA acquisition are objectively quantified in the purpose of high latitude snow cover detection, thanks to the PROBA-V (Project for On-Board Autonomy-Vegetation) satellite. A snow cover extent classification based on Normalized Difference Snow Index (NDSI) and Normalized Difference Vegetation Index (NDVI) has been performed for the northern hemisphere on latitudes between 55°N and 75°N during the 2015–2016 winter season. A stratified probabilistic sampling was used to estimate the classification accuracy. The latter has been evaluated among eight SZA intervals to determine the maximum usable angle. The global overall snow classification accuracy with PROBA-V, 82% ± 4%, was significantly larger than the MODIS (Moderate-resolution Imaging Spectroradiometer) snow cover extent product (75% ± 4%). User and producer accuracy of snow are above standards and overall accuracy is stable until 88.5° SZA. These results demonstrate that optical remote sensing data can still be used with large SZA. Considering the relevance of snow cover mapping for ecology and climatology, the data acquisition at high solar zenith angles should be continued by PROBA-V.

Data Assimilation of PROBA-V 100 and 300 m

The project for on-board autonomy-vegetation (PROBA-V) satellite can produce global daily images at 300-m spatial resolution. Three sensors are mounted on the same platform. Two off-nadir-viewing sensors acquire imagery at 300-m spatial resolution, whereas a nadir-viewing sensor acquires imagery at 100-m spatial resolution. The swath of the nadir-viewing sensor is only half of the swath of a single off-nadir-viewing sensor. Using this sensor only, the revisit time is five days. Here, we present a data assimilation method to increase the temporal resolution of the 100-m product. The method implements a Kalman filter recursive algorithm that integrates the images at 100 and 300-m resolution to generate the assimilated imagery at the fine spatial detail (100 m). The proposed method can be applied for global products. In this study, it has been applied to a region in western Europe (Flanders) during the growing season. This region is particularly challenging due to frequent cloud cover (45% cloud cover on average). The assimilated product is a cloud-free time series at the temporal resolution of the 300-m data, while preserving the spatial detail of the 100-m data. Quantitative results show the potential of the method compared to a simple data assimilation and the Savitzky–Golay (SG) filter. The added value of the improved spatial resolution from 300 to 100 m has also been illustrated for monitoring agriculture via remote sensing in this area.

Evaluating NDVI Data Continuity Between SPOT-VEGETATION and PROBA-V Missions for Operational Yield Forecasting in North African Countries

After 15 years, the Systeme Pour l'Observation de la Terre (SPOT)-VEGETATION (VGT) program reached the end of its life in May 2014 and was replaced by the Project for On-Board Autonomy-Vegetation (PROBA-V) mission. Exploiting the period of overlap between instruments, this study compares the normalized difference vegetation index (NDVI) of two instruments from the point of view of the user interested in operational crop monitoring. The comparison is performed for Morocco, Algeria, and Tunisia, where NDVI is used to derive anomaly maps, temporal profiles, and cereal yield forecasts. A relevant scatter due to unexplained unsystematic variability exists between anomaly values. A mismatch between anomaly classes is observed for 20%–30% of the crop area. However, when the NDVI is averaged over cropland and administrative units to derive temporal profiles, the two data sources show a high agreement. Results for yield estimation comparison indicate an overall high agreement, and both the (null) hypotheses that the model predictions and the root mean square error (RMSE) in yield estimation are not different, when using PROBA-V instead of SPOT-VGT, cannot be rejected in all cases for Morocco and Algeria. On the contrary, in Tunisia, where RMSE is lower using PROBA-V, the hypothesis of no difference in RMSE is rejected. These findings therefore indicate that yield estimation performances are not affected (Morocco and Algeria) or improved (Tunisia) by the source transition. Finally, despite the same nominal spatial resolution, the different spatial quality of the sensors was found to have an effect on yield estimation in areas characterized by sharp transitions between cropland and desert.

Obtaining crop-specific time profiles of NDVI: the use of unmixing approaches for serving the continuity between SPOT-VGT and PROBA-V time series

The study examined the potential of two unmixing approaches for deriving crop-specific normalized difference vegetation index (NDVI) profiles so that upon availability of Project for On-Board Autonomy – Vegetation (PROBA-V) imagery in winter 2013, this new data set can be combined with existing Satellite Pour l’Observation de la Terre – VEGETATION (SPOT-VGT) data despite the differences in spatial resolution (300 m of PROBA-V versus 1 km of SPOT-VGT). To study the problem, two data sets were analysed: (1) a set of 10 temporal NDVI images, with 300 and 1000 m spatial resolution, from the state of São Paulo (Brazil) synthesized from 30 m Landsat Thematic Mapper (TM) images, and (2) a corresponding set of 10 observed Moderate Resolution Imaging Spectroradiometer (MODIS) images (250 m spatial resolution). To mimic the influence of noise on the retrieval accuracy, different sensor/atmospheric noise levels were applied to the first data set. For the unmixing analysis, a high-resolution land-cover (LC) map was used. The LC map was derived beforehand using a different set of Landsat TM images. The map distinguishes nine classes, with four different sugarcane stages, two agricultural sub-classes, plus forest, pasture, and urban/water. Unmixing aiming at the retrieval of crop-specific NDVI profiles was done at administrative level. For the synthesized data set it was demonstrated that the ‘true’ NDVI temporal profiles of different land-cover classes (from 30 m TM data) can generally be retrieved with high accuracy. The two simulated sensors (PROBA-V and SPOT-VGT) and the two unmixing algorithms gave similar results. Analysing the MODIS data set, we also found a good correspondence between the modelled NDVI profiles (both approaches) and the (true) Landsat temporal endmembers.