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

Survey of Impact of Technology on Effective Implementation of Precision Farming in India

The advancements in technology have made its impact on almost every field. India being an agricultural country, proper use of technology can greatly help in improving the standard of living of the farmers. With varying weather conditions, illiteracy of farmers and non-availability of timely assistance, the farmers of this country could not get the best out of their efforts. Precision farming focuses mainly on the aspects that can improve the efficiency based on the data collected from various sources viz. meteorology, sensors, GIS, GPS, etc. The information pertaining to farmland (e.g., soil moisture, soil pH, soil nitrogen) and agro-meteorology (e.g., temperature & humidity, solar radiation, wind speed, atmospheric CO2 concentration, rainfall, climate change and global warming) are used as input parameters to decide the varying requirements of the crop cultivation. Historical farm land data are used as a means to decide on the kind of actions to be taken under a specific scenario. This paper surveys the existing methods of precision farming and highlights the impact of technology in farming. An overview of different technologies used in precision farming around the world and their implications on the yield are discussed. The methods adopted towards managing different types of crops, the varying environmental conditions and the use of realtime data being collected through sensors are also analyzed. Also, the need for dynamic approaches to assist the farmers in taking context specific decisions has been highlighted

Crop Phenology Modelling Using Proximal and Satellite Sensor Data

peer reviewedUnderstanding crop phenology is crucial for predicting crop yields and identifying potential risks to food security. The objective was to investigate the effectiveness of satellite sensor data, compared to field observations and proximal sensing, in detecting crop phenological stages. Time series data from 122 winter wheat, 99 silage maize, and 77 late potato fields were analyzed during 2015–2017. The spectral signals derived from Digital Hemispherical Photographs (DHP), Disaster Monitoring Constellation (DMC), and Sentinel-2 (S2) were crop-specific and sensor-independent. Models fitted to sensor-derived fAPAR (fraction of absorbed photosynthetically active radiation) demonstrated a higher goodness of fit as compared to fCover (fraction of vegetation cover), with the best model fits obtained for maize, followed by wheat and potato. S2-derived fAPAR showed decreasing variability as the growing season progressed. The use of a double sigmoid model fit allowed defining inflection points corresponding to stem elongation (upward sigmoid) and senescence (downward sigmoid), while the upward endpoint corresponded to canopy closure and the maximum values to flowering and fruit development. Furthermore, increasing the frequency of sensor revisits is beneficial for detecting short-duration crop phenological stages. The results have implications for data assimilation to improve crop yield forecasting and agri-environmental modeling

Fusing optical and SAR time series for LAI gap filling with multioutput Gaussian processes

The availability of satellite optical information is often hampered by the natural presence of clouds, which can be problematic for many applications. Persistent clouds over agricultural fields can mask key stages of crop growth, leading to unreliable yield predictions. Synthetic Aperture Radar (SAR) provides all-weather imagery which can potentially overcome this limitation, but given its high and distinct sensitivity to different surface properties, the fusion of SAR and optical data still remains an open challenge. In this work, we propose the use of Multi-Output Gaussian Process (MOGP) regression, a machine learning technique that learns automatically the statistical relationships among multisensor time series, to detect vegetated areas over which the synergy between SAR-optical imageries is profitable. For this purpose, we use the Sentinel-1 Radar Vegetation Index (RVI) and Sentinel-2 Leaf Area Index (LAI) time series over a study area in north west of the Iberian peninsula. Through a physical interpretation of MOGP trained models, we show its ability to provide estimations of LAI even over cloudy periods using the information shared with RVI, which guarantees the solution keeps always tied to real measurements. Results demonstrate the advantage of MOGP especially for long data gaps, where optical-based methods notoriously fail. The leave-one-image-out assessment technique applied to the whole vegetation cover shows MOGP predictions improve standard GP estimations over short-time gaps (R 2 of 74% vs 68%, RMSE of 0.4 vs 0.44 [m 2 m −2 ]) and especially over long-time gaps (R 2 of 33% vs 12%, RMSE of 0.5 vs 1.09 [m 2 m −2 ])

Modélisation de l’évolution hydroclimatique des flux et stocks d’eau verte et d’eau bleue du bassin versant de la Garonne

La gestion intégrée de la ressource en eau implique de distinguer les parcours de l’eau qui sont accessibles aux sociétés de ceux qui ne le sont pas. Les cheminements de l’eau sont nombreux et fortement variables d’un lieu à l’autre. Il est possible de simplifier cette question en s’attardant plutôt aux deux destinations de l’eau. L’eau bleue forme les réserves et les flux dans l’hydrosystème : cours d’eau, nappes et écoulements souterrains. L’eau verte est le flux invisible de vapeur d’eau qui rejoint l’atmosphère. Elle inclut l’eau consommée par les plantes et l’eau dans les sols. Or, un grand nombre d’études ne portent que sur un seul type d’eau bleue, en ne s’intéressant généralement qu’au devenir des débits ou, plus rarement, à la recharge des nappes. Le portrait global est alors manquant. Dans un même temps, les changements climatiques viennent impacter ce cheminement de l’eau en faisant varier de manière distincte les différents composants de cycle hydrologique. L’étude réalisée ici utilise l’outil de modélisation SWAT afin de réaliser le suivi de toutes les composantes du cycle hydrologique et de quantifier l’impact des changements climatiques sur l’hydrosystème du bassin versant de la Garonne. Une première partie du travail a permis d’affiner la mise en place du modèle pour répondre au mieux à la problématique posée. Un soin particulier a été apporté à l’utilisation de données météorologiques sur grille (SAFRAN) ainsi qu’à la prise en compte de la neige sur les reliefs. Le calage des paramètres du modèle a été testé dans un contexte differential split sampling, en calant puis validant sur des années contrastées en terme climatique afin d’appréhender la robustesse de la simulation dans un contexte de changements climatiques. Cette étape a permis une amélioration substantielle des performances sur la période de calage (2000-2010) ainsi que la mise en évidence de la stabilité du modèle face aux changements climatiques. Par suite, des simulations sur une période d’un siècle (1960-2050) ont été produites puis analysées en deux phases : i) La période passée (1960-2000), basée sur les observations climatiques, a servi de période de validation à long terme du modèle sur la simulation des débits, avec de très bonnes performances. L’analyse des différents composants hydrologiques met en évidence un impact fort sur les flux et stocks d’eau verte, avec une diminution de la teneur en eau des sols et une augmentation importante de l’évapotranspiration. Les composantes de l’eau bleue sont principalement perturbées au niveau du stock de neige et des débits qui présentent tous les deux une baisse substantielle. ii) Des projections hydrologiques ont été réalisées (2010-2050) en sélectionnant une gamme de scénarios et de modèles climatiques issus d’une mise à l’échelle dynamique. L’analyse de simulation vient en bonne part confirmer les conclusions tirées de la période passée : un impact important sur l’eau verte, avec toujours une baisse de la teneur en eau des sols et une augmentation de l’évapotranspiration potentielle. Les simulations montrent que la teneur en eau des sols pendant la période estivale est telle qu’elle en vient à réduire les flux d’évapotranspiration réelle, mettant en évidence le possible déficit futur des stocks d’eau verte. En outre, si l’analyse des composantes de l’eau bleue montre toujours une diminution significative du stock de neige, les débits semblent cette fois en hausse pendant l’automne et l’hiver. Ces résultats sont un signe de l’«accélération» des composantes d’eau bleue de surface, probablement en relation avec l’augmentation des évènements extrêmes de précipitation. Ce travail a permis de réaliser une analyse des variations de la plupart des composantes du cycle hydrologique à l’échelle d’un bassin versant, confirmant l’importance de prendre en compte toutes ces composantes pour évaluer l’impact des changements climatiques et plus largement des changements environnementaux sur la ressource en eau

Satellite remote sensing priorities for better assimilation in crop growth models : winter wheat LAI and grassland mowing dates case studies

In a context of markets globalization, early, reliable and timely estimations of crop yields at regional to global scale are clearly needed for managing large agricultural lands, determining food pricing and trading policies but also for early warning of harvest shortfalls. Crop growth models are often used to estimate yields within the growing season. The uncertainties associated with these models contribute to the uncertainty of crop yield estimations and forecasts. Satellite remote sensing, through its ability to provide synoptic information on growth conditions over large geographic extents and in near real-time, is a key data source used to reduce uncertainties in biophysical models through data assimilation methods. This thesis aims at assessing possible improvements for the assimilation of remotely-sensed biophysical variables in crop growth models and to estimate their related errors reduction on modelled yield estimates. Assimilated observations are winter wheat leaf area index (LAI) and grassland mowing dates derived respectively from optical (MODIS) and microwave (ERS-2) data. These observations have been assimilated in WOFOST and LINGRA growth models. Observing System Simulation Experiments (OSSE) allowed assessing errors reduction on yield estimates after assimilation for different situations of accuracy and frequency of remotely-sensed estimates and for different assimilation strategies, indicating expected improvements with the currently available and forthcoming sensors; it supports also guidelines for future satellite missions. A main finding is the fact that yield estimate improvements are only possible for assimilation strategies able to correct the possible phenological discrepancies between the remotely-sensed and the simulated data. These phenological shifts arise mainly from uncertainties on the parameters and initial states driving the phenological stages in the models but are also due, in some situations, to lack of pixel purity because of the medium resolution of sensors such as MODIS. This thesis also identifies some of the main sources of uncertainties and assesses their impact on assimilation performances. The impact of water content and biomass on SAR backscattering of grasslands is specifically assessed. The backscattering of grasslands increases with the increases of water content and decreases with the biomass in dry conditions. Based on these results, methodologies to classify grasslands according to land use (mowing or grazing) and to detect mowings are designed and demonstrated. A good classification accuracy is observed (overall accuracy around 80%). Results for mowings detection are a bit lower as half of the mowings are correctly identified but the methodology can be considered as promising in particular in the perspective of very dense SAR time series as currently acquired operationally by Sentinel-1.(AGRO - Sciences agronomiques et ingénierie biologique) -- UCL, 201

Implementing an Agro-Environmental Information System (AEIS) Based on GIS, Remote Sensing, and Modelling -- A case study for rice in the Sanjiang Plain, NE-China

Information on agro-ecosystems is crucial for understanding the agricultural production and its impacts on the environment, especially over large agricultural areas. The Sanjiang Plain (SJP), covering an area of 108 829 km², is a critical food base located in NE-China. Rice, soya bean and maize are the major crops in the SJP which are sold as commercial grain throughout China. The aim of this study is to set up an Agro-Environmental Information System (AEIS) for the SJP by employing the technologies of geographic information systems (GIS), remote sensing (RS), and agro-ecosystem modelling. As the starting step, data carrying interdisciplinary information from multiple sources are organized and processed. For an AEIS, geospatial data have to be acquired, organized, operated, and even regenerated with good positioning conditions. Georeferencing of the multi-source data is mandatory. In this thesis, high spatial accuracy TerraSAR-X imagery was used as a reference for georeferencing raster satellite data and vector GIS topographic data. For the second step, the georeferenced multi-source data with high spatial accuracy were integrated and categorized using a knowledge-based classifier. Rice was analysed as an example crop. A rice area map was delineated based on a time series of three high resolution FORMOSAT-2 (FS-2) images and field observed GIS topographic data. Information on rice characteristics (i.e., biomass, leaf area index, plant nitrogen concentration and plant nitrogen uptake) was derived from the multi-temporal FS-2 images. Spatial variability of rice growing status on a within-field level was well detected. As the core part of the AEIS, an agro-ecosystem modelling was then applied and subsequently crops and the environmental factors (e.g., climate, soil, field management) are linked together through a series of biochemical functions inherent in the modelling. Consequently, the interactions between agriculture and the environment are better interpreted. In the AEIS for the SJP, the site-specific mode of the DeNitrification-DeComposition (DNDC) model was adapted on regional scales by a technical improvement for the source code. By running for each pixel of the model input raster files, the regional model assimilates raster data as model inputs automatically. In this study, detailed soil data, as well as the accurate field management data in terms of crop cultivation area (i.e. rice) were used as model inputs to drive the regional model. Based on the scenario optimized from field observation, rice yields over the Qixing Farm were estimated and the spatial variability was well detected. For comparison, rice yields were derived from multi-temporal FS-2 images and the spatial patterns were analysed. As representative environmental effects, greenhouse gas of nitrous oxide (N2O) and carbon dioxide (CO2) emitted from the paddy rice fields were estimated by the regional model. This research demonstrated that the AEIS is effective in providing information about (i) agriculture on the region, (ii) the impacts of agricultural practices on the environment, and (iii) simulation scenarios for sustainable strategies, especially for the regional areas (e.g. the SJP) that is lacking of geospatial data

Constrained Distance Based Clustering for Satellite Image Time-Series

International audienceThe advent of high-resolution instruments for time-series sampling poses added complexity for the formal definition of thematic classes in the remote sensing domain-required by supervised methods-while unsupervised methods ignore expert knowledge and intuition. Constrained clustering is becoming an increasingly popular approach in data mining because it offers a solution to these problems, however, its application in remote sensing is relatively unknown. This article addresses this divide by adapting publicly available constrained clustering implementations to use the dynamic time warping (DTW) dissimilarity measure, which is sometimes used for time-series analysis. A comparative study is presented, in which their performance is evaluated (using both DTW and Euclidean distances). It is found that adding constraints to the clustering problem results in an increase in accuracy when compared to unconstrained clustering. The output of such algorithms are homogeneous in spatially defined regions. Declarative approaches and k-Means based algorithms are simple to apply, requiring little or no choice of parameter values. Spectral methods, however, require careful tuning, which is unrealistic in a semi-supervised setting, although they offer the highest accuracy. These conclusions were drawn from two applications: crop clustering using 11 multi-spectral Landsat images non-uniformly sampled over a period of eight months in 2007; and tree-cut detection using 10 NDVI Sentinel-2 images non-uniformly sampled between 2016 and 2018

Remote Sensing for Precision Nitrogen Management

This book focuses on the fundamental and applied research of the non-destructive estimation and diagnosis of crop leaf and plant nitrogen status and in-season nitrogen management strategies based on leaf sensors, proximal canopy sensors, unmanned aerial vehicle remote sensing, manned aerial remote sensing and satellite remote sensing technologies. Statistical and machine learning methods are used to predict plant-nitrogen-related parameters with sensor data or sensor data together with soil, landscape, weather and/or management information. Different sensing technologies or different modelling approaches are compared and evaluated. Strategies are developed to use crop sensing data for in-season nitrogen recommendations to improve nitrogen use efficiency and protect the environment