Monitoring crops water needs at high spatio-temporal resolution by synergy of optical/thermal and radar observations

L'optimisation de la gestion de l'eau en agriculture est essentielle dans les zones semi-arides afin de préserver les ressources en eau qui sont déjà faibles et erratiques dues à des actions humaines et au changement climatique. Cette thèse vise à utiliser la synergie des observations de télédétection multispectrales (données radar, optiques et thermiques) pour un suivi à haute résolution spatio-temporelle des besoins en eau des cultures. Dans ce contexte, différentes approches utilisant divers capteurs (Landsat-7/8, Sentinel-1 et MODIS) ont été developpées pour apporter une information sur l'humidité du sol (SM) et le stress hydrique des cultures à une échelle spatio-temporelle pertinente pour la gestion de l'irrigation. Ce travail va parfaitement dans le sens des objectifs du projet REC "Root zone soil moisture Estimates at the daily and agricultural parcel scales for Crop irrigation management and water use impact: a multi-sensor remote sensing approach" (http://rec.isardsat.com/) qui visent à estimer l'humidité du sol dans la zone racinaire (RZSM) afin d'optimiser la gestion de l'eau d'irrigation. Des approches innovantes et prometteuses sont mises en place pour estimer l'évapotranspiration (ET), RZSM, la température de surface du sol (LST) et le stress hydrique de la végétation à travers des indices de SM dérivés des observations multispectrales à haute résolution spatio-temporelle. Les méthodologies proposées reposent sur des méthodes basées sur l'imagerie, la modélisation du transfert radiatif et la modélisation du bilan hydrique et d'énergie et sont appliquées dans une région à climat semi-aride (centre du Maroc). Dans le cadre de ma thèse, trois axes ont été explorés. Dans le premier axe, un indice de RZSM dérivé de LST-Landsat est utilisé pour estimer l'ET sur des parcelles de blé et des sols nus. L'estimation par modélisation de ET a été explorée en utilisant l'équation de Penman-monteith modifiée obtenue en introduisant une relation empirique simple entre la résistance de surface (rc) et l'indice de RZSM. Ce dernier est estimé à partir de la température de surface (LST) dérivée de Landsat, combinée avec les températures extrêmes (en conditions humides et sèches) simulée par un modèle de bilan d'énergie de surface piloté par le forçage météorologique et la fraction de couverture végétale dérivée de Landsat. La méthode utilisée est calibrée et validée sur deux parcelles de blé situées dans la même zone près de Marrakech au Maroc. Dans l'axe suivant, une méthode permettant de récupérer la SM de la surface (0-5 cm) à une résolution spatiale et temporelle élevée est développée à partir d'une synergie entre données radar (Sentinel-1) et thermique (Landsat) et en utilisant un modèle de bilan d'énergie du sol. L'approche développée a été validée sur des parcelles agricoles en sol nu et elle donne une estimation précise de la SM avec une différence quadratique moyenne en comparant à la SM in situ, égale à 0,03 m3 m-3. Dans le dernier axe, une nouvelle méthode est développée pour désagréger la MODIS LST de 1 km à 100 m de résolution en intégrant le SM proche de la surface dérivée des données radar Sentinel-1 et l'indice de végétation optique dérivé des observations Landsat. Le nouvel algorithme, qui inclut la rétrodiffusion S-1 en tant qu'entrée dans la désagrégation, produit des résultats plus stables et robustes au cours de l'année sélectionnée. Dont, 3,35 °C était le RMSE le plus bas et 0,75 le coefficient de corrélation le plus élevé évalués en utilisant le nouvel algorithme.Optimizing water management in agriculture is essential over semi-arid areas in order to preserve water resources which are already low and erratic due to human actions and climate change. This thesis aims to use the synergy of multispectral remote sensing observations (radar, optical and thermal data) for high spatio-temporal resolution monitoring of crops water needs. In this context, different approaches using various sensors (Landsat-7/8, Sentinel-1 and MODIS) have been developed to provide information on the crop Soil Moisture (SM) and water stress at a spatio-temporal scale relevant to irrigation management. This work fits well the REC "Root zone soil moisture Estimates at the daily and agricultural parcel scales for Crop irrigation management and water use impact: a multi-sensor remote sensing approach" (http://rec.isardsat.com/) project objectives, which aim to estimate the Root Zone Soil Moisture (RZSM) for optimizing the management of irrigation water. Innovative and promising approaches are set up to estimate evapotranspiration (ET), RZSM, land surface temperature (LST) and vegetation water stress through SM indices derived from multispectral observations with high spatio-temporal resolution. The proposed methodologies rely on image-based methods, radiative transfer modelling and water and energy balance modelling and are applied in a semi-arid climate region (central Morocco). In the frame of my PhD thesis, three axes have been investigated. In the first axis, a Landsat LST-derived RZSM index is used to estimate the ET over wheat parcels and bare soil. The ET modelling estimation is explored using a modified Penman-Monteith equation obtained by introducing a simple empirical relationship between surface resistance (rc) and a RZSM index. The later is estimated from Landsat-derived land surface temperature (LST) combined with the LST endmembers (in wet and dry conditions) simulated by a surface energy balance model driven by meteorological forcing and Landsat-derived fractional vegetation cover. The investigated method is calibrated and validated over two wheat parcels located in the same area near Marrakech City in Morocco. In the next axis, a method to retrieve near surface (0-5 cm) SM at high spatial and temporal resolution is developed from a synergy between radar (Sentinel-1) and thermal (Landsat) data and by using a soil energy balance model. The developed approach is validated over bare soil agricultural fields and gives an accurate estimates of near surface SM with a root mean square difference compared to in situ SM equal to 0.03 m3 m-3. In the final axis a new method is developed to disaggregate the 1 km resolution MODIS LST at 100 m resolution by integrating the near surface SM derived from Sentinel-1 radar data and the optical-vegetation index derived from Landsat observations. The new algorithm including the S-1 backscatter as input to the disaggregation, produces more stable and robust results during the selected year. Where, 3.35 °C and 0.75 were the lowest RMSE and the highest correlation coefficient assessed using the new algorithm

Вопросы валидации результатов при наземно-бортовом спектральном определении влажности почвы

Contact point field measurements of soil moisture are characterized by low productivity.  High efficiency of determining and monitoring soil moisture can be achieved remote sensing. The known microwave methods for remote determination of soil moisture have low spatial resolution, are expensive and hence unsuitable for the use in agriculture. For estimating soil moisture content, the article proposes the ground-based on-board complex, which includes an on-board passive meter of the optical signal reflected from the soil and a ground contact meter. Operation of the latter can be performed either by an automatic measuring network or by an operator conducting contact measurements. The authors formulated and solved the problem related to assessing the accuracy of validation of the on-board measurement of soil moisture content. As an on-board meter, a passive radiation spectroradiometer of the soil illuminated by the Sun is used, and as a ground meter, a contact meter is used for estimating soil moisture at different spatial increments. The accuracy indicators of ground measurements and their relationship with the estimated values were analyzed. Comparison of the results obtained by the ground and on-board measurements allowed to determine the relationship between the moisture content measured from the board and the error of ground measurements as a validation error.Контактные точечные полевые измерения почвенной влажности отличаются невысокой производительностью; их реальной альтернативой, обеспечивающей высокую эффективность мониторинга, являются дистанционные методы определения. Известные микроволновые методы дистанционного определения влажности почвы имеют низкое пространственное разрешение, являются дорогостоящими и непригодными для использования в целях сельского хозяйства. В статье для определения влажности почвы предлагается наземно-бортовой комплекс, включающий в себя бортовой пассивный измеритель отраженного от почвы оптического сигнала и наземный контактный измеритель, функцию которого может выполнять либо автоматическая измерительная сеть, либо оператор, проводящий контактные измерения. Авторами сформулирована и решена задача оценки точности валидации результатов бортового измерения влажности почвы в системе наземно-бортовых измерений. В качестве бортового измерителя используется спектрорадиометр пассивного излучения почвы, освещенной Солнцем, а в качестве наземного – контактный измеритель, с помощью которого оценивается влажность почвы при разных пространственных шагах. Проанализированы валидационные показатели наземных измерений и их взаимосвязь с величиной влажности почвы. Сопоставление результатов наземных и бортовых измерений позволило определить взаимосвязь между влажностью почвы, измеренной с борта, и погрешностью наземных измерений в виде погрешности валидации

Validation et désagrégation de l’humidité du sol estimée par le satellite SMOS en zones agricoles et forestières des Prairies canadiennes

Résumé : Le satellite Soil Moisture and Ocean Salinity (SMOS), lancé en novembre 2009, est le premier satellite en mode passif opérant en bande-L. Cette fréquence est considérée comme optimale pour estimer l’humidité du sol. SMOS est destiné à cartographier l’humidité de la couche 0-5 cm du sol à l’échelle globale, avec une précision attendue inférieure à 0,04 m3/m3, une répétitivité temporelle inférieure à 3 jours et une résolution spatiale d’environ 40 km. L’objectif de cette thèse est de valider l’humidité du sol de SMOS sur des sites agricoles et forestiers situés au Canada, et de contribuer au développement de méthodes de désagrégation de l’humidité du sol estimée par SMOS dans le but d’exploiter ces données dans les études à l’échelle locale telle qu’en agriculture. Les données de la campagne de terrain CanEx-SM10, effectuée sur un site agricole (Kenaston) et un site forestier (BERMS) situés à Saskatchewan, et celles de la campagne SMAPVEX12, effectuée sur un site majoritairement agricole (Winnipeg) situé au Manitoba, sont utilisées. Les données d’humidité du sol de SMOS ont montré une amélioration de la version v.309 à la version v.551. La version 551 des données d’humidité du sol de SMOS se compare mieux aux mesures in situ que les autres versions, aussi bien sur les sites agricoles que sur le site forestier. Sur les sites agricoles, l’humidité du sol de SMOS a montré une bonne corrélation avec les mesures au sol, particulièrement avec la version 551 (R ≥ 0,58, en modes ascendant et descendant), ainsi qu’une certaine sensibilité à la pluviométrie. Néanmoins, SMOS sous-estime l’humidité du sol en général. Cette sous-estimation est moins marquée sur le site de Kenaston en mode descendant (|biais| ≈ 0,03 m3/m3, avec la version v.551). Sur le site forestier, en raison de la densité de la végétation, les algorithmes d’estimation de l’humidité du sol à partir des mesures SMOS ne sont pas encore efficaces, malgré les améliorations apportées dans la version v.551. Par ailleurs, sur le site agricole de Kenaston et le site forestier de BERMS, les données d’humidité du sol de SMOS ont montré, généralement, de meilleures performances par rapport aux produits d’humidité du sol d’AMSR-E/NSIDC, AMSR-E/VUA et ASCAT/SSM. DISaggregation based on Physical And Theoretical scale Change (DISPATCH), un algorithme de désagrégation à base physique, est utilisé pour désagréger à 1 km de résolution spatiale l’humidité du sol de SMOS (40 km de résolution) sur les deux sites agricoles situés à Kenaston et à Winnipeg. DISPATCH est basé sur l’efficacité d’évaporation du sol (SEE) estimée à partir des données optique/ thermique de MODIS, et un modèle linéaire/non-linéaire liant l’efficacité d’évaporation et l’humidité du sol à l’échelle locale. Sur un site présentant une bonne dynamique spatiale et temporelle de l’humidité du sol (le site de Winnipeg au cours de la campagne de terrain SMAPVEX12), les résultats de DISPATCH obtenus avec le modèle linéaire sont légèrement meilleurs (R = 0,81 ; RMSE = 0.05 m3/m3 et pente = 0,52, par rapport aux mesures in situ) comparés aux résultats obtenus avec le modèle non-linéaire (R = 0,72 ; RMSE = 0.06 m3/m3 et pente = 0,61, par rapport aux mesures in situ). La précision de l’humidité du sol dérivée de DISPATCH, en se basant sur les deux modèles linéaire et non linéaire, décroit quand l’humidité du sol à grande échelle croît. Cette étude a montré, également, que DISPATCH peut être généralisé sur des sites particulièrement humides (le site de Kenaston au cours de la campagne de terrain CanEx-SM10). Cependant, en conditions humides, les résultats dérivés avec le modèle non-linéaire (R > 0,70, RMSE = 0,04 m3/m3 et pente ≈ 0,80, par rapport aux valeurs d’humidité du sol dérivées des mesures aéroportées de la température de brillance en bande L) ont montré de meilleures performances comparées à ceux dérivés avec le modèle linéaire (R > 0,73, RMSE = 0,08 m3/m3 et pente > 1.5, par rapport aux valeurs d’humidité du sol dérivées des mesures aéroportées de la température de brillance en bande L). Ceci est dû à une sous-estimation systématique de la limite sèche Tsmax. Par ailleurs, l’humidité du sol désagrégée présente une forte sensibilité à〖 Ts〗_max, particulièrement avec le modèle linéaire. Une approche simple a été proposée pour améliorer l’estimation de〖 Ts〗_max, dans des zones particulièrement humides. Elle a permis de réduire l’impact de l’incertitude sur〖 Ts〗_max dans le processus de désagrégation. Avec 〖 Ts〗_max améliorée, le modèle linaire aboutit à de meilleurs résultats (R > 0,72, RMSE = 0,04 m3/m3 et pente ≈ 0,80, par rapport aux valeurs d’humidité du sol estimées à partir des mesures aéroportées de la température de brillance en bande-L) que le modèle non-linéaire (R > 0,64, RMSE = 0,05 m3/m3 et pente ≈ 0,3, par rapport aux valeurs d’humidité du sol estimées à partir des mesures aéroportées de la température de brillance en bande-L). Basé sur des données optiques/ thermiques de MODIS, DISPATCH n’est pas applicable pour les journées nuageuses. Pour surmonter cette limitation, une nouvelle méthode a été proposée. Elle consiste à combiner DISPATCH avec le schéma de surface Canadian Land Surface Scheme (CLASS). Les données d’humidité du sol à 1 km de résolution dérivées de DISPATCH pour les journées non nuageuses sont utilisées pour calibrer les simulations de CLASS disponibles continuellement aux heures de passage de SMOS. Une approche de calibration basée sur la correction de la pente entre les valeurs d’humidité du sol dérivées de CLASS et les valeurs d’humidité du sol dérivées de DISPATCH (données de référence) a été mise au point. Les résultats montrent que les données d’humidité du sol à 1 km de résolution dérivées de cette nouvelle approche pour les journées nuageuses se comparent bien aux mesures in situ (R = 0,80 ; biais = -0,01 m3/m3 et pente = 0,74). Pour les journées non nuageuses, les valeurs d’humidité du sol dérivées de DISPATCH seul se comparent mieux aux mesures in situ que les valeurs dérivées en combinant DISPATCH à CLASS.Abstract : The Soil Moisture and Ocean Salinity (SMOS), launched in November 2009, is the first passive microwave satellite operating in L band which is considered as optimal for soil moisture estimation. It is designed to provide global soil moisture maps at 0 – 5 cm layer from soil surface with a targeted accuracy of 0.04 m3 / m3, revisit time of less than 3 days anda spatial resolution of about 40 km. The objective of this thesis is to validate SMOS soil moisture data over agricultural and forested sites located in Canada, and to contribute to the development of SMOS downscaling methods in order to exploit these data in local scale studies such as agriculture. The data used are collected during the CanEX-SM10 field campaign, conducted over an agricultural site (Kenaston) and a forested site (BERMS) located in Saskatchewan, and during SMAPVEX12 field campaign conducted over a mostly agricultural area (Winnipeg) located in Manitoba. SMOS soil moisture data showed an improvement from the processor versions 309 to 551. Version 551 was found to be closer and more correlated to ground measurements over both agricultural and forested sites. For the agricultural sites, SMOS soil moisture showed high correlation coefficient with ground data especially with version 551(R ≥ 0.58, for ascending and descending overpasses), as well as a certain sensitivity to rainfall events. However, the SMOS soil moisture values were underestimated compared with ground measurements. This underestimation is less pronounced for the descending overpass over the Kenaston site (|bias| viii ≈ 0.03 m3/m3, for version v.551). For the forested site, due to the vegetation density, the SMOS soil moisture estimation algorithms were not very efficient despite the improvements brought to version 551. Moreover, over the agricultural site of Kenaston and the forested site of BERMS, SMOS soil moisture data showed, in general, good performances compared to AMSR-E/NSIDC, AMSR-E/VUA and ASCAT/SSM soil moisture products. DISaggregation based on Physical And Theoretical scale Change (DISPATCH), a physically-based downscaling algorithm, was used to downscale at 1-km spatial resolution the SMOS soil moisture estimates (40-km resolution) over the agricultural sites located in Kenaston and Winnipeg. DISPATCH is based on the Soil Evaporative Efficiency (SEE) derived from optical/thermal MODIS data, and a linear/non-linear model linking the Soil Evaporative Efficiency to the near-surface soil moisture at local scale. Over a site with a good spatial and temporal dynamics of soil moisture (such as Winnipeg’s site during the SMAPVEX12 field campaign), slightly better results were obtained with DISPATCH based on the linear model (R = 0.81, RMSE = 0.05 m3 /m3 and slope = 0.52, with respect to ground data) compared to results obtained from the non-linear model (R = 0.72, RMSE = 0.06 m3 /m3 and slope = 0.61, with respect to ground data). The accuracy of the DISPATCH-derived soil moisture, using both linear and non-linear models, decreases when the large-scale soil moisture increases. This study also showed, also, that DISPATCH can be generalized for very wet soil conditions (Kenaston’s site during the CanEX-SM10 field campaign). However, under wet soil conditions, better results were obtained with DISTACH based on the nonlinear (R > 0.70, RMSE = 0.04 m3/m3 and slope ≈ 0.80, with respect to the estimated soil moisture form L-band airborne brightness temperature) compared to results obtained with ix DISPATCH based on the linear model (R > 0.73, RMSE = 0.08 m3/m3 and slope > 1.5, with respect to the estimated soil moisture form L-band airborne brightness temperature). This is due to a systematic underestimation of the dry edge Tsmax. Furthermore, the downscaling results were found to be very sensitive to , particularly with the linear model. A simple approach was proposed to improve the estimation of Tsmax under very wet soil conditions. It allowed reducing the impact of uncertainty in the disaggregation process. Using the improved Tsmax value, better results were obtained with the linear model (R > 0.72, RMSE = 0.04 m3/m3 and slope ≈ 0.80, with respect to the estimated soil moisture form L-band airborne brightness temperature) compared to the non-linear model (R > 0.64, RMSE = 0.05m3/m3 and slope ≈ 0.3, with respect to the estimated soil moisture form L-band airborne brightness temperature). Based on optical/thermal MODIS data, DISPATCH is not applicable for cloudy days. To overcome this limitation, a new method was proposed. It involves the combination of DISPATCH with the Canadian Land Surface Scheme (CLASS). DISPATCH-derived soil moisture data for cloud-free days are used to calibrate CLASS soil moisture simulations which are continually available at SMOS overpasses times. A calibration approach based on slope correction between the CLASS-derived and DISPATCH-derived (reference data) soil moisture datasets is considered. Results showed that soil moisture values derived from this newly developed method during cloudy days compare well with in situ data (R = 0.80, RMSE = 0.07 m3/m3 and slope = 0.73). For no-cloudy days, DISTATCH-derived soil moisture data are closer to in situ data than those derived when combining DISPATCH with CLASS

The value of satellite remote sensing soil moisture data and the DISPATCH algorithm in irrigation fields

Soil moisture measurements are needed in a large number of applications such as hydro-climate approaches, watershed water balance management and irrigation scheduling. Nowadays, different kinds of methodologies exist for measuring soil moisture. Direct methods based on gravimetric sampling or time domain reflectometry (TDR) techniques measure soil moisture in a small volume of soil at few particular locations. This typically gives a poor description of the spatial distribution of soil moisture in relatively large agriculture fields. Remote sensing of soil moisture provides widespread coverage and can overcome this problem but suffers from other problems stemming from its low spatial resolution. In this context, the DISaggregation based on Physical And Theoretical scale CHange (DISPATCH) algorithm has been proposed in the literature to downscale soil moisture satellite data from 40 to 1¿km resolution by combining the low-resolution Soil Moisture Ocean Salinity (SMOS) satellite soil moisture data with the high-resolution Normalized Difference Vegetation Index (NDVI) and land surface temperature (LST) datasets obtained from a Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. In this work, DISPATCH estimations are compared with soil moisture sensors and gravimetric measurements to validate the DISPATCH algorithm in an agricultural field during two different hydrologic scenarios: wet conditions driven by rainfall events and wet conditions driven by local sprinkler irrigation. Results show that the DISPATCH algorithm provides appropriate soil moisture estimates during general rainfall events but not when sprinkler irrigation generates occasional heterogeneity. In order to explain these differences, we have examined the spatial variability scales of NDVI and LST data, which are the input variables involved in the downscaling process. Sample variograms show that the spatial scales associated with the NDVI and LST properties are too large to represent the variations of the average soil moisture at the site, and this could be a reason why the DISPATCH algorithm does not work properly in this field site.Peer ReviewedPostprint (published version

The value of satellite remote sensing soil moisture data and the DISPATCH algorithm in irrigation fields

Soil moisture measurements are needed in a large number of applications such as hydro-climate approaches, watershed water balance management and irrigation scheduling. Nowadays, different kinds of methodologies exist for measuring soil moisture. Direct methods based on gravimetric sampling or time domain reflectometry (TDR) techniques measure soil moisture in a small volume of soil at few particular locations. This typically gives a poor description of the spatial distribution of soil moisture in relatively large agriculture fields. Remote sensing of soil moisture provides widespread coverage and can overcome this problem but suffers from other problems stemming from its low spatial resolution. In this context, the DISaggregation based on Physical And Theoretical scale CHange (DISPATCH) algorithm has been proposed in the literature to downscale soil moisture satellite data from 40 to 1&thinsp;km resolution by combining the low-resolution Soil Moisture Ocean Salinity (SMOS) satellite soil moisture data with the high-resolution Normalized Difference Vegetation Index (NDVI) and land surface temperature (LST) datasets obtained from a Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. In this work, DISPATCH estimations are compared with soil moisture sensors and gravimetric measurements to validate the DISPATCH algorithm in an agricultural field during two different hydrologic scenarios: wet conditions driven by rainfall events and wet conditions driven by local sprinkler irrigation. Results show that the DISPATCH algorithm provides appropriate soil moisture estimates during general rainfall events but not when sprinkler irrigation generates occasional heterogeneity. In order to explain these differences, we have examined the spatial variability scales of NDVI and LST data, which are the input variables involved in the downscaling process. Sample variograms show that the spatial scales associated with the NDVI and LST properties are too large to represent the variations of the average soil moisture at the site, and this could be a reason why the DISPATCH algorithm does not work properly in this field site.</p

Applications of remote sensing in sugarcane agriculture at Umfolozi, South Africa.

Thesis (M.Sc.)-University of KwaZulu-Natal, Pietermaritzburg, 2004.The aim of this study was to evaluate potential applications of remote sensing technology in sugarcane agriculture, using the Umfolozi Mill Supply Area as a case study. Several objectives included the evaluation of remotely sensed satellite information for the following applications: mapping of sugarcane areas, identifying sugarcane characteristics including phenology, cultivar and yield, monitoring the sugarcane inventory throughout the milling season and yield prediction. Four Landsat 7 ETM+ (Enhanced Thematic Mapper Plus) images were obtained for the 2001-2002 season. Mapping of sugarcane areas was conducted by .means of unsupervised hierarchical classifications, on three relatively cloud free, Tasseled Cap transformed images. The Brightness, Greenness and Wetness bands for each Tasseled Cap transformed image were combined into a single image for this classification. The investigation into relationships between satellite spectral reflectances and phenology, cultivar and yield involved the cosine of the solar zenith angle (COST) method for atmospheric correction of all four Landsat 7 ETM+ images. Detailed agronomic records and field boundary information, for a selection of sugarcane fields, were used to extract the at-satellite reflectances on a pixel basis . These values were stored in a relational database for analysis. Monitoring of the sugarcane inventory throughout the milling season was conducted by means of unsupervised classifications on the Brightness, Greenness and Wetness bands for each of the four time-step Tasseled Cap transformed images. Accurate field boundary information for all sugarcane fields was used to mask out non-sugarcane areas. The remaining sugarcane areas in each time-step image were then classified by means of unsupervised classification techniques to ascertain the relative proportions of the different land covers, namely: harvested immature and mature sugarcane by visual interpretation of the classification results. The yield forecasting approach utilized a time-step approach in which Vegetation Indices (VIs) were accumulated over different periods or time frames and compared with annual production. VIs were derived from both the National Oceanic and Atmospheric Administration (NOAA) and Landsat 7 ETM+ sensors. Different periods or times were used for each sensor. The results for the mapping of sugarcane areas showed that the mapping accuracies for the large scale grower fields was higher than for the small-scale growers. In both instances, the level of accuracy was below that of the recommended sugar industry mapping standard, namely 1% of the true area. Despite the low mapping accuracies, much benefit could be realized from the map product in terms of identifying new areas of sugarcane expansion. These would require detailed accurate mapping. The results for monitoring of the sugarcane inventory throughout showed that remote sensing, in conjunction with detailed field information, was able to accurately measure the areas harvested in each time-step image. These results may have highly beneficial applications in sugarcane supply management and monitoring. The results for time-step approach to yield forecasting yielded poor results in general. The Landsat derived VIs showed limited potential; however, the data were only available for one season, making it difficult to quantify the impact of climatic conditions on these results. All results for the time-step approach using NOAA data yielded negative results. The results for the investigation into relationships between satellite spectral reflectances and phenology, cultivar and yield showed that that different phenological stages of sugarcane growth were identifiable from Landsat 7 ETM+ at-satellite reflectances. The sugarcane yields and cultivar types were not correlated with the at-satellite reflectances. These results combined with the sugarcane area monitoring may provide valuable information in the management and monitoring of sugarcane supply

Désagrégation spatiale de températures Météosat par une méthode d'assimilation de données (lisseur particulaire) dans un modèle de surface continentale

Land surface temperature (LST) is one of the most important meteorologicalvariables giving access to water and energy budgets governing the Biosphere-Atmosphere continuum. To better monitor vegetation and energy states, we need hightemporal and spatial resolution measures of LST because its high variability in spaceand time.Despite the growing availability of Thermal Infra-Red (TIR) remote sensing LSTproducts, at different spatial and temporal resolutions, both high spatial resolution(HSR) and high temporal resolution (HTR) TIR data is still not possible because ofsatellite resolutions trade-off : the most frequent LST products being low spatial resolution(LSR) ones.It is therefore necessary to develop methods to estimate HSR/HTR LST from availableTIR LSR/HTR ones. This solution is known as "downscaling" and the presentthesis proposes a new approach for downscaling LST based on Data Assimilation (DA)methods. The basic idea is to constrain HSR/HTR LST dynamics, simulated by a dynamicalmodel, through the minimization of their respective aggregated LSTs discrepancytoward LSR observations, assuming that LST is homogeneous at the land cover typescale inside the LSR pixel.Our method uses a particle smoother DA method implemented in a land surfacemodel : SETHYS model (Suivie de l’Etat Hydrique de Sol). The proposed approach hasbeen firstly evaluated in a synthetic framework then validated using actual TIR LSTover a small area in South-East of France. Meteosat LST time series were downscaledfrom 5km to 90m and validated with ASTER HSR LST over one day. The encouragingresults conducted us to expand the study area and consider a larger assimilation periodof seven months. The downscaled Meteosat LSTs were quantitatively validated at1km of spatial resolution (SR) with MODIS data and qualitatively at 30m of SR withLandsat7 data. The results demonstrated good performances with downscaling errorsless than 2.5K at MODIS scale (1km of SR).La température des surfaces continentales (LST) est une variable météorologiquetrès importante car elle permet l’accès aux bilans d’énergie et d’eau ducontinuum Biosphère-Atmosphère. Sa haute variabilité spatio-temporelle nécessite desmesures à haute résolution spatiale (HRS) et temporelle (HRT) pour suivre au mieuxles états hydriques du sol et des végétations.La télédétection infrarouge thermique (IRT) permet d’estimer la LST à différentesrésolutions spatio-temporelles. Toutefois, les mesures les plus fréquentes sont souventà basse résolution spatiale (BRS). Il faut donc développer des méthodes pour estimerla LST à HRS à partir des mesures IRT à BRS/HRT. Cette solution est connue sous lenom de désagrégation et fait l’objet de cette thèse.Ainsi, une nouvelle approche de désagrégation basée sur l’assimilation de données(AD) est proposée. Il s’agit de contraindre la dynamique des LSTs HRS/HRT simuléespar un modèle en minimisant l’écart entre les LST agrégées et les données IRT àBRS/HRT, sous l’hypothèse d’homogénéité de la LST par type d’occupation des sols àl’échelle du pixel BRS. La méthode d’AD choisie est un lisseur particulaire qui a étéimplémenté dans le modèle de surface SETHYS (Suivi de l’Etat Hydrique du Sol).L’approche a été évaluée dans une première étape sur des données synthétiques etvalidée ensuite sur des données réelles de télédétection sur une petite région au Sud-Est de la France. Des séries de températures Météosat à 5 km de résolution spatialeont été désagrégées à 90m et validées sur une journée à l’aide de données ASTER.Les résultats encourageants nous ont conduit à élargir la région d’étude et la périoded’assimilation à sept mois. La désagrégation des produits Météosat a été validée quantitativementà 1km à l’aide de données MODIS et qualitativement à 30m à l’aide dedonnées Landsat7. Les résultats montrent de bonnes performances avec des erreursinférieures à 2.5K sur les températures désagrégées à 1km

Third Annual Earth Resources Program Review. Volume 2: Agriculture, forestry, and sensor studies

Remote sensing and data reduction techniques for Earth Resources Program applied to agriculture and forestry - conferenc

Estimation of regional evaporation under different weather conditions from satellite and meteorological data: a case study in the Naivasha Basin, Kenya

Existing remote sensing algorithms used to estimate evaporation from remotely sensed data differ in the way they describe the spatial variations of input parameters. An evaluation of the impact of spatially varying input parameters on distributed surface fluxes showed that the vertical near surface air temperature difference and frictional velocity were the most critical parameters. Most remote sensing algorithms treat air temperature as spatially constant indicating that they are less suitable for the calculation of distributed evaporation in heterogeneous catchments.The temporal variability of the evaporative fractionΛat the daily and seasonal time frames was investigated with field data obtained at two experimental sites. For general weather conditions the values of the midday (12.00 to 13.00 hrs) evaporative fractionΛ mid compared well with the averaged day time evaporative fractionΛ day . A good relationship was obtained between daytime evaporation estimated fromΛ mid and evaporation measured by the Bowen ratio surface energy balance method. Less satisfactory evaporation results were obtained using morning (9.00 to 10.00 hrs) evaporation fractionΛ mor . The seasonal evolution ofΛ day was observed to be gradual. To capture the seasonal evolution ofΛ day it would be sufficient to measureΛ day approximately every 10 days. Moreover, it was shown that the inter-annual variability of the 10-day averageΛcould be reliably estimated from standard weather data.To monitor the temporal evolution of daily evaporation over a season, evaporation has to be estimated between consecutive clear days with satellite images being available. Two methods to predict daily evaporation on days without satellite images due to cloud cover are presented. Field data acquired at two sites were used to test these methods. The first method consists of the application of the Penman-Monteith equation and Jarvis-Stewart model with standard weather data and the assumption of gradual soil moisture changes between consecutive clear days. With this method evaporation could be accurately predicted for up-to 5 continuous days with no satellite images. The second method is a simplified approach involving the use of a constantΛbetween cloud free days with measured evaporation. This approach did not give satisfactory results in predicting evaporation on individual days. However, the total evaporation of a 7-day time span was equally good for both methods.Five NOAA AVHRR satellite images were used to produce daily evaporation maps of the Naivasha basin for 15 continuous days with intermittent cloud cover by using the Penman-Monteith equation coupled with the Jarvis-Stewart model as well as the evaporative fraction method. The evaporation maps were validated with field data and overall good agreement was obtained. This demonstrated that remote sensing methods can be extended for practical use under all weather conditions to map both the spatial patterns as well as the temporal evolution of evaporation in catchments and river basins. The methods of predicting evaporation can be applied at different time scales. Users can select the appropriate time scales depending on their needs. The implementation of the Penman-Monteith equation and Jarvis-Stewart model requires a land cover classification of the catchment to assign land cover dependent coefficients in the Jarvis-Stewart model. At each land cover type standard weather data has to be measured.</p

Spatio-temporal modelling of bluetongue virus distribution in Northern Australia based on remotely sensed bioclimatic variables

The presence of Bluetongue virus (BTV) in Northern Australia poses an ongoing threat for animal health and although clinical disease has not been detected in livestock, it limits export of livestock from the infected areas. BTV presence is governed by variable environmental conditions, which influence vector and host habitats. The National Arbovirus Monitoring Program (NAMP) was established to determine the extent of virus activity and control the risk of infection spread. Groups of young cattle, previously unexposed to infection, are regularly tested to detect evidence of transmission. This approach is labour and cost intensive and difficult to operate in the remote areas of Northern Australia. The resulting data are therefore characterised by spatial and temporal gaps. The aim of this research is to assess the use of remotely sensed environmental and climatic data as a means of predicting the distribution of BTV seroprevalence throughout Northern Australia to complement conventional surveillance.Environmental factors relating to the viruses’ host and vector habitats and the transmission cycle of BTV have been identified based on the extensive review of virus ecology. Different data sources have been assessed to provide sufficient spatial and temporal coverage for the definition of spatio-temporal environmental variables that can be used to explain and predict the distribution of BTV. Following this assessment, satellite data products from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Tropical Rainfall Measuring Mission (TRMM) were acquired for the Pilbara in Western Australia, and the Northern Territory. These were reprojected and processed into spatio-temporal variables for the period between the years 2000 and 2009. Due to uncertainty in the precision of the geographic location and timing of animals tested for seropositivity, summary statistics of bioclimatic variables were generated at the station (i.e. property) level for each year. Different combinations of these variables, including vegetation greenness and phenology, land surface temperature and precipitation were screened for correlation with BTV presence using a Generalised Additive Model approach. A final model was developed to predict the presence or absence of BTV seropositivity on the basis of statistical significance of the remotely sensed predictor variables, and informed by knowledge of virus ecological principles.The model, based on the maximum seasonal Normalised Difference Vegetation Index (NDVI), and mean and maximum land surface temperature variables provided excellent discriminatory ability and the basis for the generation of prediction maps of BTV seropositivity for the first eight years. Besides internal assessment, the model’s predictive capabilities were validated using monitoring data from the season 2008/09.It has been demonstrated that the predictions are useful in complementing complement NAMP surveillance by identifying areas at higher risk for seropositivity in cattle, which aids planning of livestock movement and further monitoring activities. Uncertainty in the model was attributed to the spatio-temporal inconsistency in the precision of the available serosurveillance data. The discriminatory ability of models of this type could be further improved by ensuring that exact location details and date of NAMP BTV test events are consistently recorded