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In Mali's cotton belt, spatial variability in management practices, soil fertility and rainfall strongly impact crop productivity and the livelihoods of smallholder farmers. To identify crop growth conditions and hence improve food security, accurate assessment of local crop production is key. However, production estimates in heterogeneous smallholder farming systems often rely on labor-intensive surveys that are not easily scalable, nor exhaustive. Recent advances in high-resolution earth observation (EO) open up new possibilities to work in heterogeneous smallholder systems. This paper develops a method to estimate individual crop production at farm-to-community scales using high-resolution Sentinel-2 time series and ground data in the commune of Koningue, Mali. Our estimation of agricultural production relies on (i) a supervised, pixel-based crop type classification inside an existing cropland mask, (ii) a comparison of yield estimators based on spectral indices and derived leaf area index (LAI), and (iii) a Monte Carlo approach combining the resulting unbiased crop area estimate and the uncertainty on the associated yield estimate. Results show that crop types can be mapped from Sentinel-2 data with 80% overall accuracy (OA), with best performances observed for cotton (Fscore 94%), maize (88%) and millet (83%), while peanut (71%) and sorghum (46%) achieve less. Incorporation of parcel limits extracted from very high-resolution imagery is shown to increase OA to 85%. Obtained through inverse radiative transfer modeling, Sen2-Agri estimates of LAI achieve better prediction of final grain yield than various vegetation indices, reaching R2 of 0.68, 0.62, 0.8 and 0.48 for cotton, maize, millet and sorghum respectively. The uncertainty of Monte Carlo production estimates does not exceed 0.3% of the total production for each crop type.

Agricultural intensification and efficient use and targeting of fertilizer inputs on smallholder farms is key to sustainably improve food security. The objective of this paper is to demonstrate how high-resolution satellite and unmanned aerial vehicle (UAV) images can be used to assess the spatial variability of yield, and yield response to fertilizer. The study included 48 and 50 smallholder fields monitored during the 2014 and 2015 cropping seasons south-east of Koutiala (Mali), cropped with the five major crops grown in the area (cotton, maize, sorghum, millet and peanuts). Each field included up to five plots with different fertilizer applications and one plot with farmer practice. Fortnightly, in-situ in each field data were collected synchronous with UAV imaging using a Canon S110 NIR camera. A concurrent series of very high-resolution satellite images was procured and these images were used to mask out trees. For each plot, we calculated vegetation index means, medians and coefficients of variation. Cross-validated general linear models were used to assess the predictability of relative differences in crop yield and yield response to fertilizer, explicitly accounting for the effects of fertility treatments, between-field and within-field variabilities. Differences between fields accounted for a much larger component of variation than differences between fertilization treatments. Vegetation indices from UAV images strongly related to ground cover (R2 = 0.85), light interception (R2 = 0.79) and vegetation indices derived from satellite images (R2 values of about 0.8). Within-plot distributions of UAV-derived vegetation index values were negatively skewed, and within-plot variability of vegetation index values was negatively correlated with yield. Plots on shallow soils with poor growing conditions showed the largest within-plot variability. GLM models including UAV derived estimates of light interception explained up to 78% of the variation in crop yield and 74% of the variation in fertilizer response within a single field. These numbers dropped to about 45% of the variation in yield and about 48% of the variation in fertilizer response when lumping all fields of a given crop, with Q2 values of respectively 22 and 40% respectively when tested with a leave-field-out procedure. This indicates that remotely sensed imagery doesn’t fully capture the influence of crop stress and management. Assessment of crop fertilizer responses with vegetation indices therefore needs a reference under similar management. Spatial variability in UAV-derived vegetation index values at the plot scale was significantly related to differences in yields and fertilizer responses. The strong relationships between light interception and ground cover indicate that combining vertical photographs or high-resolution remotely sensed vegetation indices with crop growth models allows to explicitly account for the spatial variability and will improve the accuracy of yield and crop production assessments, especially in heterogeneous smallholder conditions.

In Southern Mali, high yield variability caused by rainfall distribution, local soil fertility and farmer management impacts the vulnerability of smallholder farmers. To better understand yield variation on a yearly basis and in a food security context, it is necessary to adequately estimate the crop yield. As yield assessment requires accurate crop type maps, the first step of the present paper is to classify crop types with the 10–20 m resolution time series of Sentinel-2 satellite. A random forest classifier applied under the cropland area produces a crop type map with an overall accuracy of 85%. The best performances are met for cotton (Fscore 93.86%), maize (Fscore 87.52%) and millet (82.5%) while sorghum (Fscore 45.7%) and peanut (Fscore 71%) show lower results. In a second phase, the yield estimation is based on linear regressions with vegetation indices (VI) or Leaf Area Index (LAI). The best performance is met with the maximum LAI (obtained from Sentinel-2) accross the growing season and reaches an average R2 of 0.61 for all crop types. This very simple proxy is ambitious for food security as crop yield map can be produced with high accuracy. Thus yield variation can easily be assessed and could help to better target agricultural interventions at the farm or village scale.

A general framework for processing high and very-high resolution imagery in support of a Global Human Settlement Layer (GHSL) is presented together with a discussion on the results of the first operational test of the production workflow. The test involved the mapping of 24.3 million km2 of the Earth surface spread in four continents, corresponding to an estimated population of 1.3 billion people in 2010. The resolution of the input image data ranges from 0.5 to 10 meters, collected by a heterogeneous set of platforms including satellite SPOT (2 and 5), CBERS 2B, RapidEye (2 and 4), WorldView (1 and 2), GeoEye 1, QuickBird 2, Ikonos 2, and airborne sensors. Several imaging modes were tested including panchromatic, multispectral and pan-sharpened images. A new fully automatic image information extraction, generalization and mosaic workflow is presented that is based on multiscale textural and morphological image features extraction. New image feature compression and optimization are introduced, together with new learning and classification techniques allowing for the processing of HR/VHR image data using low-resolution thematic layers as reference. A new systematic approach for quality control and validation allowing global spatial and thematic consistency checking is proposed and applied. The quality of the results are discussed by sensor, band, resolution, and eco-regions. Critical points, lessons learned and next steps are highlighted.

The exploitation of resources, if not properly managed, can lead to spoiling natural habitats as well as to threatening people’s health, livelihoods and security. The paper discusses a multi-scale Earth observation-based approach to provide independent information related to exploitation activities of natural resources for countries which are experiencing armed conflict. The analyses are based on medium to very high spatial resolution optical satellite data. Object-based image analysis is used for information extraction at these different scales. On a subnational level, conflict-related land cover changes as an indication of potential hot spots for exploitation activities are classified. The regional assessment provides information about potential activity areas of resource exploitation, whereas on a local scale, a site-specific assessment of exploitation areas is performed. The study demonstrates the potential of remote sensing for supporting the monitoring and documentation of natural resource e...

The paper focuses on automatic extraction building stock information for quantifying physical exposure and its vulnerability from High and Very High Resolution (VHR) optical satellite imagery. We use two case studies. In Sana’a (Yemen) we use automatic techniques to extract the building stock as well as building height that is used to characterize its vulnerability. In Port-au-Prince (Haiti) – in the area affected by the 2010 earthquake -we map the building stock based on a pre-disaster imagery – and we show the added value of area based information when added to point based damage assessment from visual change interpretation of post disaster aerial images. The paper shows that VHR imagery can be used to locate and quantify the building stock and its height. This paper also shows that damages measured from changes detected from pre-and post-disaster imagery can in principle map and provide vulnerability information related to the structural fragility of the building stock., JRC.G.2-Global security and crisis management

The current paper investigates the potential contribution of ENVISAT wide swath (WS) images for discrimination and monitoring of crops at a regional scale. The study was based on synthetic aperture radar (SAR) images acquired throughout an entire growing season. Advanced synthetic aperture radar sensor (ASAR) images in both narrow swath (NS) and WS modes were simulated based on 15 European Remote Sensing (ERS) satellite images recorded over Belgium. Unlike 'real' ASAR imagery, this exercise provided a consistent data set (i.e. same incidence angle, same acquisition date, same acquisition hour) to study the impact of spatial resolution on the SAR signal information content. A quantitative approach using 787 parcels of medium field size and various data combinations assessed monitoring and discrimination capabilities for six crop types: wheat, barley, grasses, sugar beet, maize and potato. The spatial resolution impact of the ASAR sensor was discussed with respect to the field size by comparing the results obtained from NS (30 m) and WS (150 m) mode images. WS temporal profiles were able to discriminate the various crops of interest and were representative of the crop development observed in the region. Furthermore, parcel-based unsupervised classifications successfully discriminated between grass, wheat, barley and other crops of large parcels (success rate of 83%). Dedicated interpretation schemes were developed in order to discriminate between cereal crops.

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.

This paper assessed the use of optical and SAR imagery for crop identification in an operational context with a particular emphasis on actual crop diversity and information delivery time. Fifteen ERS and Radarsat and 3 optical images were used to discriminate agricultural crop types based on dedicated per-parcel classification and photo interpretation schemes. For crop area control, the efficiency concept was introduced as a complementary indicator of classification performance. A set of 6571 parcels were classified into 39 crop types from various combinations of images. The efficiency computed from an independent set of 899 parcels peaked based on a combination of optical images and 3 to 5 SAR images. Moreover, the delivery time of the relevant information was improved when SAR data was included. The hierarchical classification strategy based on nested classifications also improved the operational crop control system for all image combinations. Finally, this research documented the respective contributions of optical and SAR time series for any control system of agricultural land.

One-day interval coherence images derived from pairs of ERS SAR tandem acquisitions are suitable for crop monitoring. Coherence images were analyzed and compared to field measurements of four crops, i.e., winter wheat, sugar beet, potato and maize, taken during the satellite overpass. First, the sensitivity of the coherence to the plant height and the canopy cover was statistically investigated. Regression analyses were computed and the coefficients of determination (R-2) ranged from 0.64 to 0.92. The shape of these relationships varied according to the geometric factors which are crop-type dependent. A prediction model of the wheat height was calculated and was able to estimate the plant height with a mean absolute error of approximately 7 cm. While this high performance obtained on the average matched the observed range of the field height for a given date, it was not sufficient for monitoring at the field level. However, such a performance level may meet the information requirements for an operational crop monitoring system at the regional level, which includes a much larger diversity of growing conditions. Moreover, the soil roughness change associated with the sowing practices that occurred between the two tandem acquisitions strongly decreased the coherence signal. This dataset also indicated that variation in the soil moisture influenced the backscattering coefficient more than it influenced the coherence signal. This result enhanced the InSAR coherence potentialities to estimate the crop parameters during the growing season. (C) 2003 Elsevier Inc. All rights reserved.

This paper reports about the feasibility to automatically map the settlements of Brazil with the panchromatic, 2.5 m data of the HRC camera of the CBERS-2B satellite. Using a combined textural and morphological image analysis in a fully automated workflow an equal error rate of 0.18 ± 0.09 and a minimal error rate of 0.06 ± 0.06 could be obtained for an area of 1.4 million km2 of the most densely inhabited parts of Brazil. The method provides in addition to the presence of buildings information about the building density and average size., JRC.G.2-Global security and crisis management

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 paper 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 paper 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