Estimation of the dynamics and yields of cereals in a semi-arid area using remote sensing and the SAFY growth model

International audienceIn semi-arid areas, a strongly variable climate represents a major risk for food safety. An operational grain yield forecasting system, which could help decision-makers to make early assessments and plan annual imports, is thus needed. It can be challenging to monitor the crop canopy and production capacity of plants, especially cereals. In this context, the aim of the present study is to analyse the characteristics of two types of irrigated and non-irrigated cereals: barley and wheat. Through the use of a rich database, acquired over a period of two years for more than 30 test fields, and from 20 optical satellite SPOT/HRV images, two research approaches are considered. First, statistical analysis is used to characterize the vegetation's dynamics and grain yield, based on remotely sensed (satellite) normalized difference vegetation index (NDVI) measurements. A relationship is established between the NDVI and LAI (leaf area index). Different robust relationships (exponential or linear) are established between the satellite NDVI index acquired from SPOT/HRV images, just before the time of maximum growth (April), and grain and straw, for barley and wheat vegetation covers. Following validation of the proposed empirical approaches, yield maps are produced for the studied site. The second approach is based on the application of a Simple Algorithm for Yield Estimation (SAFY) growth model, developed to simulate the dynamics of the LAI and the grain yield. An inter-comparison between ground yield measurements and SAFY model simulations reveals that yields are underestimated by this model. Finally, the combination of multi-temporal satellite measurements with the SAFY model estimations is also proposed for the purposes of yield mapping. Although the results produced by the SAFY model are found to be reasonably well correlated with those determined by satellite measurements (NDVI), the grain yields are nevertheless underestimated

Irrigated grassland monitoring using a time series of terraSAR-X and COSMO-skyMed X-Band SAR Data

[Departement_IRSTEA]Territoires [TR1_IRSTEA]SYNERGIE [Axe_IRSTEA]TETIS-ATTOSInternational audienceThe objective of this study was to analyze the sensitivity of radar signals in the X-band in irrigated grassland conditions. The backscattered radar signals were analyzed according to soil moisture and vegetation parameters using linear regression models. A time series of radar (TerraSAR-X and COSMO-SkyMed) and optical (SPOT and LANDSAT) images was acquired at a high temporal frequency in 2013 over a small agricultural region in southeastern France. Ground measurements were conducted simultaneously with the satellite data acquisitions during several grassland growing cycles to monitor the evolution of the soil and vegetation characteristics. The comparison between the Normalized Difference Vegetation Index (NDVI) computed from optical images and the in situ Leaf Area Index (LAI) showed a logarithmic relationship with a greater scattering for the dates corresponding to vegetation well developed before the harvest. The correlation between the NDVI and the vegetation parameters (LAI, vegetation height, biomass, and vegetation water content) was high at the beginning of the growth cycle. This correlation became insensitive at a certain threshold corresponding to high vegetation (LAI ~2.5 m2/m2). Results showed that the radar signal depends on variations in soil moisture, with a higher sensitivity to soil moisture for biomass lower than 1 kg/m². HH and HV polarizations had approximately similar sensitivities to soil moisture. The penetration depth of the radar wave in the X-band was high, even for dense and high vegetation; flooded areas were visible in the images with higher detection potential in HH polarization than in HV polarization, even for vegetation heights reaching 1 m. Lower sensitivity was observed at the X-band between the radar signal and the vegetation parameters with very limited potential of the X-band to monitor grassland growth. These results showed that it is possible to track gravity irrigation and soil moisture variations from SAR X-band images acquired at high spatial resolution (an incidence angle near 30°)

Evaluation of Multiorbital SAR and Multisensor Optical Data for Empirical Estimation of Rapeseed Biophysical Parameters

This article aims to evaluate the potential of multitemporal and multiorbital remote sensing data acquired both in the microwave and optical domain to derive rapeseed biophysical parameters (crop height, dry mass, fresh mass, and plant water content). Dense temporal series of 98 Landsat-8 and Sentinel-2 images were used to derive normalized difference vegetation index (NDVI), green fraction cover (fCover), and green area index (GAI), while backscattering coefficients and radar vegetation index (RVI) were obtained from 231 mages acquired by synthetic aperture radar (SAR) onboard Sentinel-1 platform. Temporal signatures of these remote sensing indicators (RSI) were physically interpreted, compared with each other to ground measurements of biophysical parameters acquired over 14 winter rapeseed fields throughout the 2017–2018 crop season. We introduced new indicators based on the cumulative sum of each RSI that showed a significant improvement in their predictive power. Results particularly reveal the complementarity of SAR and optical data for rapeseed crop monitoring throughout its phenological cycle. They highlight the potential of the newly introduced indicator based on the VH polarized backscatter coefficient to estimate height (R2 = 0.87), plant water content (R2 = 0.77, from flowering to harvest), and fresh mass (R2 = 0.73) and RVI to estimate dry mass (R2 = 0.82). Results also demonstrate that multiorbital SAR data can be merged without significantly degrading the performance of SAR-based relationships while strongly increasing the temporal sampling of the monitoring. These results are promising in view of assimilating optical and SAR data into crop models for finer rapeseed monitoring

A Global Systematic Review of Improving Crop Model Estimations by Assimilating Remote Sensing Data: Implications for Small-Scale Agricultural Systems

There is a growing effort to use access to remote sensing data (RS) in conjunction with crop model simulation capability to improve the accuracy of crop growth and yield estimates. This is critical for sustainable agricultural management and food security, especially in farming communities with limited resources and data. Therefore, the objective of this study was to provide a systematic review of research on data assimilation and summarize how its application varies by country, crop, and farming systems. In addition, we highlight the implications of using process-based crop models (PBCMs) and data assimilation in small-scale farming systems. Using a strict search term, we searched the Scopus and Web of Science databases and found 497 potential publications. After screening for relevance using predefined inclusion and exclusion criteria, 123 publications were included in the final review. Our results show increasing global interest in RS data assimilation approaches; however, 81% of the studies were from countries with relatively high levels of agricultural production, technology, and innovation. There is increasing development of crop models, availability of RS data sources, and characterization of crop parameters assimilated into PBCMs. Most studies used recalibration or updating methods to mainly incorporate remotely sensed leaf area index from MODIS or Landsat into the WOrld FOod STudies (WOFOST) model to improve yield estimates for staple crops in large-scale and irrigated farming systems. However, these methods cannot compensate for the uncertainties in RS data and crop models. We concluded that further research on data assimilation using newly available high-resolution RS datasets, such as Sentinel-2, should be conducted to significantly improve simulations of rare crops and small-scale rainfed farming systems. This is critical for informing local crop management decisions to improve policy and food security assessments

Modélisation spatialisée de la production des flux et des bilans de carbone et d'eau des cultures de blé à l'aide de données de télédétection : application au sud-ouest de la France

Les terres agricoles, qui occupent plus d'un tiers de la surface continentale de la Terre, contribuent au changement climatique et sont aussi affectées par ces changements puisque leur production est contrainte par les conditions climatiques et les ressources en eau. L'objectif principal de cette thèse est donc de quantifier et d'analyser la production et aussi les principales composantes des cycles biogéochimiques du carbone et de l'eau des agrosystèmes, pour des années climatiques contrastées, afin d'identifier les meilleures stratégies pour maintenir la production et réduire les impacts environnementaux. Ce travail a été focalisé sur les cultures de blé du sud-ouest de la France. Pour répondre à cet objectif nous proposons une approche de modélisation spatialisée qui combine : i) des données de télédétection optique à hautes résolutions spatiale et temporelle, ii) des modèles de culture semi-empiriques et iii) un ample dispositif de mesures in-situ pour la calibration et la validation des modèles. L'utilisation combinée de ces trois outils offre de nouvelles perspectives pour la modélisation et le suivi des agrosystèmes à l'échelle régionale et globale.The agricultural lands that occupy more than one third of Earth's terrestrial surface contribute to climate change and are also impacted by those changes, since their production is conditioned by climatic conditions and water resources. The main objective of this thesis is therefore to quantify and analyze the production and also the main components of the carbon and water biogeochemical cycles for crop ecosystems in contrasted climatic years, focusing specifically on the winter wheat crop, in order to identify the best strategies for maintaining crop production and reducing environmental impacts. The study area is located in southwest France. We propose a regional modeling approach that combines: i) high spatial and temporal resolutions optical remote sensing data, ii) simple crop models and iii) an extensive set of in-situ measurements for models' calibration and validation. The combined use of these three 'tools' opens new perspectives for advanced agro-ecosystems modeling and monitoring at regional or global scales

ESA - RESGROW: Epansion of the Market for EO Based Information Services in Renewable Energy - Biomass Energy sector

Biomass energy is of growing importance as it is widely recognised, both scientifically and politically, that the increase of atmospheric CO2 has led to an enhanced efficiency of the greenhouse effect and, as such, warrants concern for climate change. It is accepted (IPCC 2011 and just recently in the draft version of the IPCC 2013 report) that climate change is partly induced by humans notably by using fossil fuels. For reducing the use of oil or coal, biomass energy is receiving more and more attention as an additional energy source available regionally in large parts of the world. Effective management of renewable energy resources is critical for the European and the global energy supply system. The future contribution of bioenergy to the energy supply strongly depends on its availability, in other words the biomass potential. Biomass potentials are currently mainly assessed on a national to regional or on a global level, with the bulk biomass potential allocated to the whole country. With certain biomass fractions being of low energy density, transport distances and thus their spatial distribution are crucial economic and ecological factors. For other biomass fractions a super-regional or global market is envisaged. Thus spatial information on biomass potentials is vital for the further expansion of bioenergy use. This study, which is an updated version of a study carried out in 2007 in frame of the ENVISOLAR project, analyses the potential use of Earth Observation data as input for biomass models in order to assessment and manage of the biomass energy resources especially biomass potentials of agricultural and forest areas with high spatial resolution (typical 1km x 1km). In addition to a sorrow review of recent developments in data availability and approaches in comparison to its 2007’ version, this study also includes a review on approaches to directly correlate remote sensing data with biomass estimations. An overview of existing biomass models is given covering models using remote sensing data as input as well as models using only meteorological and/or management data as input. It covers the full life cycle from the planning stage to plant management and operations (Figure 1). Several groups of stakeholders were identified

Apport des données satellitaires Sentinel-1 et Sentinel-2 pour la détection des surfaces irriguées et l'estimation des besoins et des consommations en eau des cultures d'été dans les zones tempérées

L'eau est une ressource naturelle, qui depuis des millions d'années participe au cycle de la vie. Mais depuis peu, le changement climatique et les activités humaines remettent en cause l'équilibre du cycle de l'eau. Pour préserver cette ressource, il est nécessaire d'améliorer la connaissance sur les surfaces irriguées ainsi que les besoins et consommations en eau des cultures sur de grandes surfaces, mais elle n'est pas simple à appréhender à cause de la forte variabilité spatiale des sols, du climat et des pratiques agricoles. La télédétection a un rôle fondamental à jouer et plus particulièrement les données Sentinel. Ces travaux de thèse ont vocation à fournir des outils de diagnostics pour assurer une gestion optimale de la ressource en eau à l'échelle des bassins versants. Pour cela, une approche de cartographie des surfaces irriguées en zones tempérées à partir d'images Sentinel-1 & 2 a été développée. Elle a permis de cartographier les cultures d'été irriguées et pluviales sur les bassins versants Adour amont et Tarn aval en fin de saison. Nous nous sommes intéressés à la modélisation des besoins et des consommations en eau du maïs irrigué à partir du modèle agro-météorologique SAMIR utilisant des images d'indice de végétation (NDVI et FCover). Il a été appliqué à différentes échelles spatiales et sur différents jeux de données de validation. Les résultats montrent que le modèle est capable de reproduire de façon satisfaisante les consommations en eau des parcelles des partenaires. Nous avons également évalué l'impact de différentes données pédologiques pour estimer la réserve utile (RU). Les résultats illustrent la nécessité d'une bonne estimation de la RU et cela à une échelle compatible avec une modélisation à la parcelle pour pouvoir estimer correctement les irrigations saisonnières, ainsi que les volumes.Water is a natural resource that has been part of the life cycle for millions of years. But recently, climate change and human activities have challenged the balance of the water cycle. To preserve this resource, it is necessary to improve knowledge of irrigated areas and the water needs and consumption of crops over large areas, but this is not easy to understand because of the high spatial variability of soils, climate and cultivation practices. Remote sensing has a fundamental role to play and more particularly Sentinel data. The aim of this thesis is to provide diagnostic tools to ensure optimal management of water resources at the catchment scale. To this end, an approach for mapping irrigated areas in temperate zones based on monthly Sentinel-1 & 2 images was developed. It was used to map irrigated and rain-fed summer crops in the Adour amont and Tarn aval catchments during and at the end of the season. In parallel with these results, the method was developed for operational purposes. We focused on modelling the water requirements and consumption of irrigated maize using the SAMIR agro-meteorological model using vegetation index images (NDVI and FCover). It was applied at different spatial scales and on different validation data sets. The results show that the model is able to reproduce satisfactorily the water consumption of the partners plots. We also evaluated the impact of different soil data to estimate the maximum useful reserve. The results illustrate the need for a good estimation of the AWC at a scale compatible with plot modelling in order to be able to correctly estimate seasonal irrigations and volumes