Multi-time scale analysis of sugarcane within-field variability: Improved crop diagnosis from satellite time series ?

International audienceWithin-field spatial variability is related to multiple factors that can be time independent or time-dependent. In this study, our working hypothesis is that a multi-time scale analysis of the dynamics of spatial patterns can help establish a diagnosis of crop condition. To test this hypothesis, we analyzed the within-field variability of a sugarcane crop at seasonal and annual time scales, and tried to link this variability to environmental (climate, topography, and soil depth) and cropping (harvest date) factors. The analysis was based on a sugarcane field vegetation index (NDVI) time series of fifteen SPOT images acquired in the French West Indies (Guadeloupe) in 2002 and 2003, and on an original classification method that enabled us to focus on crop spatial variability independently of crop growth stages. We showed that at the seasonal scale, the within-field growth pattern depended on the phenological stage of the crop and on cropping operations. At the annual scale, NDVI maps revealed a stable pattern for the two consecutive years at peak vegetation, despite very different rainfall amounts, but with inverse NDVI values. This inversion is linked with the topography and consequently to the plant water status. We conclude that (1) it is necessary to know the crop growing cycle to correctly interpret the spatial pattern, (2) single-date images may be insufficient for the diagnosis of crop condition or for prediction, and (3) the pattern of vigour occurrence within fields can help diagnose growth anomalies

Improving harvest and planting monitoring for smallholders with geospatial technology: the Reunion Island experience

8 pagesInternational audienceWith the recent development of geospatial technologies, remote sensing data are more and more integrated in the information systems of crop industries. The satellite's ability to collect "snapshots" over large cropped areas at once makes it a unique tool to able it to acquire localized and objective data in real-time.In this paper, we present how this technology can provide reliable information for sugarcane planting and harvest monitoring by updating information on field/blocks boundaries and cane status using time series of satellite images. Through the experience conducted on Reunion Island (Indian Ocean) where the sugar industry has difficulty collecting updated localized information on smallholders fields, we show how SPOT satellite images can be interpreted and processed in order to produce thematic maps and statistics. These maps can then be integrated in a Geographic Information System (GIS) designed for decision makers. This on-the-shelf GIS permits one to visualize maps and edit monthly statistics of the management practices in each production area

Radiative transfer in shrub savanna sites in Niger: Preliminary results from HAPEX-Sahel. Part 1: Modelling surface reflectance using a geometric-optical approach

To use optical remote sensing to monitor land surface-climate interactions over large areas, algorithms must be developed to relate multispectral measurements to key variables controlling the exchange of matter (water, carbon dioxide) and energy between the land surface and the atmosphere. The proportion of the ground covered by vegetation and the interception of photosynthetically active radiation (PAR) by vegetation are examples of two variables related to evapotranspiration and primary production, respectively. An areal-proportion model of the multispectral reflectance of shrub savanna, composed of scattered shrubs with a grass, forb or soil understory, predicted the reflectance of two 0.5 km(exp 2) sites as the area-weighted average of the shrub and understory or 'background' reflectances. Although the shaded crown and shaded background have darker reflectances, ignoring them in the area-weighted model is not serious when shrub cover is low and solar zenith angle is small. A submodel predicted the reflectance of the shrub crown as a function of the foliage reflectance and amount of plant material within the crown, and the background reflectance scattered or transmitted through canopy gaps (referred to as a soil-plant 'spectral interaction' term). One may be able to combine these two models to estimate both the fraction of vegetation cover and interception of PAR by green vegetation in a shrub savanna

: Géoprospective territoriale à l'île de La Réunion

16 p.National audienceThe objective of this paper is to present an approach for experimenting territorial prospective analysis based on spatial modelling. This approach is carried out in the framework of the DESCARTES project which aims at developing a spatial simulation tool to support the design and analysis of different scenarios for land-use allocation in Reunion Island in terms of environmental services. The spatial modelling tool is composed of two complementary applications: (i) the Ocelet modelling language and its land dynamics simulation environment, and (ii) the Margouill@ platform. The first demonstrator, a model of farm land consumption by urbanization, was developed and presented during workshops in order to test the role of the spatial simulation tool in support of a collaborative innovation process among stakeholders, and to foster new research on social learning, spatial simulation of environmental services, and scale change issues.L'objectif de cet article est de présenter une démarche de construction d'un exercice de prospective territoriale basé sur un outil de modélisation spatiale. Cette démarche est mise en œuvre dans le cadre du projet ANR DESCARTES dont l'objectif est de construire un outil de simulation cartographique pour analyser différents scénarios d'affectation de l'usage des sols à l'Ile de La Réunion, en termes de services environnementaux. La plateforme de simulation cartographique est composée de deux applications complémentaires (i) le langage de modélisation Ocelet et son environnement de simulation de paysages dynamiques, et (ii) la plateforme Margouill@. Le développement puis la présentation, en atelier, d'un premier démonstrateur sur la consommation des terres agricoles par l'urbanisation a permis de tester l'outil cartographique comme support d'un processus d'innovation collective entre les parties prenantes, et d'ouvrir de nouveaux champs de recherche sur l'analyse de la démarche par les apprentissages, la spatialisation et la simulation prospective des services écosystémiques, et la prise en compte du changement d'échelle

Estimation de la production primaire en zone sahelienne a partir de donnees radiometriques. Cas d'un couvert discontinu: le mil

SIGLEAvailable from INIST (FR), Document Supply Service, under shelf-number : T 78681 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc

Monitoramento da fenologia de culturas através de Sensoriamento Remoto

The monitoring of agriculture practices (e.g. sowing dates or double cropping systems) are considered a relevant research issue in Remote Sensing. The objective of this paper is to test the potential of MODIS satellite data to detect vegetation dynamics over agricultural land, focusing on the estimate of the sowing dates of soybean crops. First, the MODIS MCD12Q2 product, composed of phenology transition dates, was tested, although it had good results by the group in Africa (Mali), it turned out to be unusable due to a large number of missing data and inconsistencies in our study area, Mato Grosso (Brasil). An alternative method, based on the MOD13Q1 Enhanced Vegetation Indices (EVI) time series was developed. We applied a 3x3 window Savitzky-Golay filter to the EVI time series, and extracted the 2006-2007 growing period. We then calculated the dates at which different EVI values were reached (from 0.1 to 0.9, step 0.1), and correlated these dates to a set of sowing dates observed in the fields over the same period. Further studies based on this method can be used to come up with a sowing mapping for agriculture planning, monitoring the date of plantation, correlating it with yield and also be used as an input for crop modeling.Pages: 453-45

Linking heterogeneous data for food security prediction

International audienceIdentifying food insecurity situations timely and accurately is a complex challenge. To prevent food crisis and design appropriate interventions, several food security warning and monitoring systems are very active in food-insecure countries. However, the limited types of data selected and the limitations of data processing methods used make it difficult to apprehend food security in all its complexity. In this work, we propose models that aim to predict two key indicators of food security: the food consumption score and the household dietary diversity score. These indicators are time consuming and costly to obtain. We propose using heterogeneous data as explanatory variables that are more convenient to collect. These indicators are calculated using data from the permanent agricultural survey conducted by the Burkinabe government and available since 2009. The proposed models use deep and machine learning methods to obtain an approximation of food security indicators from heterogeneous explanatory data. The explanatory data are rasters (population densities, rainfall estimates, land use, etc.), GPS points (of hospitals, schools, violent events), quantitative economic variables (maize prices, World Bank variables), meteorological and demographic variables. A basic research issue is to perform pre-processing adapted to each type of data and then to find the right methods and spatio-temporal scale to combine them. This work may also be useful in an operational approach, as the methods discussed could be used by food security warning and monitoring systems to complement their methods to obtain estimates of key indicators a few weeks in advance and to react more quickly in case of famine

Estimation des rendements à partir d'indices de végétation et d'indices de température : etude de cas des céréales pluviales en Afrique de l'Ouest semi-aride

International audienceFor the semi-arid Sahelian region, climate variability is one of the most important risks of food insecurity. Field experimentations as well as crop modelling are helpful tools for the monitoring and the understanding of yields at local scale. However, extrapolation of these methods at a regional scale remains a demanding task. Remote sensing observations appear as a good alternative or addition to existing crop monitoring systems. In this study, a new approach based on the combination of vegetation and thermal indices for rainfed cereal yield assessment in the Sahelian region was investigated. Empirical statistical models were developed between MODIS NDVI and LST variables and the crop model SARRA-H simulated aboveground biomass and harvest index in order to assess each component of the yield equation. The resulting model was successfully applied at the Niamey Square Degree (NSD) site scale with yield estimations close to the official agricultural statistics of Niger for a period of 11 years (2000-21 2011) (r=0.82, pvalue<0.05). The combined NDVI and LST indices based model was found to clearly outperform the model based on NDVI alone (r=0.59, pvalue<0.10). In areas where access to ground measurements is difficult, a simple, robust and timely satellite-based model combining vegetation and 2 thermal indices from MODIS and calibrated using crop model outputs, can be pertinent. In particular, such a model can provide an assessment of the year-to-year yield variability shortly after harvest for regions with agronomic and climate characteristics close to those of the NSD study area.En région sahélienne semi-aride, la variabilité climatique constitue l'un des risques les plus importants d'insécurité alimentaire. Les expérimentations sur le terrain ainsi que la modélisation des cultures sont des outils utiles pour la surveillance et la compréhension des rendements à l'échelle locale. Cependant, l'extrapolation de ces méthodes à l'échelle régionale reste une tâche difficile. Les observations de télédétection apparaissent comme une bonne alternative ou un complément aux systèmes de surveillance des cultures existants. Dans cette étude, une nouvelle approche basée sur la combinaison d'indices de végétation et thermiques pour l'évaluation du rendement en céréales en culture pluviale dans la région sahélienne a été étudiée. Des modèles statistiques empiriques ont été développés entre les variables MODIS NDVI et LST et le modèle de culture SARRA-H simulé de la biomasse aérienne et de l'indice de récolte afin d'évaluer chaque composante de l'équation de rendement. Le modèle résultant a été appliqué avec succès à l'échelle du site du degré carré de Niamey (DCN) avec des estimations de rendement proches des statistiques agricoles officielles du Niger pour une période de 11 ans (2000-21 2011) (r = 0,82, pvalue <0,05). Le modèle combiné basé sur les indices NDVI et LST s'est révélé nettement supérieur au modèle fondé sur le NDVI seul (r = 0,59, pvalue <0,10). Dans les zones où l'accès aux mesures au sol est difficile, un modèle satellite simple, robuste et opportun combinant végétation et 2 indices thermiques de MODIS et calibré à l'aide des résultats du modèle de culture peut s'avérer pertinent. En particulier, un tel modèle peut fournir une évaluation de la variabilité du rendement d'une année à l'autre peu après la récolte pour les régions présentant des caractéristiques agronomiques et climatiques proches de celles de la zone d'étude du DCN

Remote sensing products and services in support of agricultural public policies in Africa: overview and challenges

International audienceIn the last decade, governments, international institutions, donors, the private sector, etc. have shown a renewed interest in agricultural issues in Sub-Saharan Africa (SSA). This interest came with a strong need for information in countries where the lack of reliable and timely basic information can be a problem. Thanks to its capacity to observe the Earth at local, regional, and global scales and from various vantage points, satellite remote sensing is a powerful tool to streamline the monitoring and improvement of the existing systems, and thus to support decision-making. However, the path from satellite images to public policy decisions is not straightforward, and today, only few operational information services are available in SSA countries (e.g., early warning systems for food security and desert locust plagues prevention, rangeland production forecasting). This paper aims to analyze the gap between the technical aspects of the remote sensing sciences and the pragmatic need for relevant data to address agricultural policies in Africa and produce operational recommendations. To achieve this goal, the authors (1) determine the information, and in particular the geoinformation, needed to develop, implement and evaluate agricultural public policies (2) illustrate the role of remote sensing as a public policy tool in SSA through an overview of the current off-the-shelf products and services derived from Earth Observation systems, and (3) propose an analysis of the existing gap between the remote sensing research community and the policy makers. Based on this review, the authors conclude that to benefit from this technological advancement and bridge the gap between technical analysts and policy makers, some key points are fundamental: capacity building, political will and institutional commitment, public-private partnership, and proofs of concept

Leaf area index estimation with MODIS reflectance time series and model inversion during full rotations of Eucalyptus plantations

The leaf area index (LAI) of fast-growing Eucalyptus plantations is highly dynamic both seasonally and interannually, and is spatially variable depending on pedo-climatic conditions. LAI is very important in determining the carbon and water balance of a stand, but is difficult to measure during a complete stand rotation and at large scales. Remote-sensing methods allowing the retrieval of LAI time series with accuracy and precision are therefore necessary. Here, we tested two methods for LAI estimation from MODIS 250m resolution red and near-infrared (NIR) reflectance time series. The first method involved the inversion of a coupled model of leaf reflectance and transmittance (PROSPECT4), soil reflectance (SOILSPECT) and canopy radiative transfer (4SAIL2). Model parameters other than the LAI were either fixed to measured constant values, or allowed to vary seasonally and/or with stand age according to trends observed in field measurements. The LAI was assumed to vary throughout the rotation following a series of alternately increasing and decreasing sigmoid curves. The parameters of each sigmoid curve that allowed the best fit of simulated canopy reflectance to MODIS red and NIR reflectance data were obtained by minimization techniques. The second method was based on a linear relationship between the LAI and values of the GEneralized Soil Adjusted Vegetation Index (GESAVI), which was calibrated using destructive LAI measurements made at two seasons, on Eucalyptus stands of different ages and productivity levels. The ability of each approach to reproduce field-measured LAI values was assessed, and uncertainty on results and parameter sensitivities were examined. Both methods offered a good fit between measured and estimated LAI (R(2) = 0.80 and R(2) = 0.62 for model inversion and GESAVI-based methods, respectively), but the GESAVI-based method overestimated the LAI at young ages. (C) 2010 Elsevier Inc. All rights reserved.European Integrated Project Ultra Low CO2 Steelmaking (ULCOS)[515960]CNPq306561/2007EucFlux projectFrench Ministry of Foreign Affair