Soil is one of the most precious resources on Earth because of its role in storing and recycling water and nutrients essential for life, providing a variety of ecosystem services. This vulnerable resource is at risk from degradation by erosion, salinity, contamination and other effects of mismanagement. Information from soil is therefore crucial for its sustainable management. While the demand for soil information is growing, the quantity of data collected in the field is reducing due to financial constraints. Digital Soil Mapping (DSM) supports the creation of geographically referenced soil databases generated by using field observations or legacy data coupled, through quantitative relationships, with environmental covariates. This enables the creation of soil maps at unexplored locations at reduced costs. The selection of an optimal scale for environmental covariates is still an unsolved issue affecting the accuracy of DSM. The overall aim of this research was to explore the effect of spatial scale alterations of environmental covariates in DSM. Three main targets were identified: assessing the impact of spatial scale alterations on classifying soil taxonomic units; investigating existing approaches from related scientific fields for the detection of scale patterns and finally enabling practitioners to find a suitable scale for environmental covariates by developing a new methodology for spatial scale analysis in DSM. Three study areas, covered by detailed reconnaissance soil survey, were identified in the Republic of Ireland. Their different pedological and geomorphological characteristics allowed to test scale behaviours across the spectrum of conditions present in the Irish landscape. The investigation started by examining the effects of scale alteration of the finest resolution environmental covariate, the Digital Elevation Model (DEM), on the classification of soil taxonomic units. Empirical approaches from related scientific fields were subsequently selected from the literature, applied to the study areas and compared with the experimental methodology. Wavelet analysis was also employed to decompose the DEMs into a series of independent components at varying scales and then used in DSM analysis of soil taxonomic units. Finally, a new multiscale methodology was developed and evaluated against the previously presented experimental results. The results obtained by the experimental methodology have proved the significant role of scale alterations in the classification accuracy of soil taxonomic units, challenging the common practice of using the finest available resolution of DEM in DSM analysis. The set of eight empirical approaches selected in the literature have been proved to have a detrimental effect on the selection of an optimal DEM scale for DSM applications. Wavelet analysis was shown effective in removing DEM sources of variation, increasing DSM model performance by spatially decomposing the DEM. Finally, my main contribution to knowledge has been developing a new multiscale methodology for DSM applications by combining a DEM segmentation technique performed by k-means clustering of local variograms parameters calculated in a moving window with an experimental methodology altering DEM scales. The newly developed multiscale methodology offers a way to significantly improve classification accuracy of soil taxonomic units in DSM. In conclusion, this research has shown that spatial scale analysis of environmental covariates significantly enhances the practice of DSM, improving overall classification accuracy of soil taxonomic units. The newly developed multiscale methodology can be successfully integrated in current DSM analysis of soil taxonomic units performed with data mining techniques, so advancing the practice of soil mapping. The future of DSM, as it successfully progresses from the early pioneering years into an established discipline, will have to include scale and in particular multiscale investigations in its methodology. DSM will have to move from a methodology of spatial data with scale to a spatial scale methodology. It is now time to consider scale as a key soil and modelling attribute in DSM.

A escassez de informações do solo que permitam o seu uso adequado, seja para fins agrícola, ambiental ou de projeto urbanos, pode ser minimizada com soluções provenientes do desenvolvimento de novas tecnologias. Nesse sentido, o presente estudo teve como objetivo aplicar duas estratégias digitais para obtenção de mapas de solos preliminares em áreas onde não foram realizados levantamentos pedológicos convencionais. As estratégias foram executadas com base em variáveis ambientais que estabelecem relações entre ocorrência de solos e suas posições na paisagem. A área de estudo compreendeu o município de Barra Bonita-SP, totalizando 11.072 ha. Para uso na predição dos solos pela técnica de Redes Neurais Artificiais (RNA) foram utilizadas as variáveis: declividade, elevação, perfil de curvatura, plano de curvatura e índice de convergência derivados de um Modelo Digital de Elevação (MDE), além das informações de geologia e das superfícies geomórficas identificadas na região. Na primeira estratégia, por meio de uma análise de agrupamento (Fuzzy k-médias) das variáveis, foram escolhidas cinco áreas chaves distribuídas na área de estudo, nas quais foi realizado levantamento de solos de nível semidetalhado para reconhecimento das unidades de mapeamento. Na estratégia 2, elaborou-se um mapa de solos de nível detalhado a partir de dados pré-existentes de apenas uma área chave, localizada no centro da região. Com a identificação das unidades de mapeamento foram gerados arquivos de treinamento e testes das redes neurais. Utilizou-se o simulador JavaNNS e o algoritmo de aprendizado backpropagation. Conjuntos de variáveis ambientais foram testados, avaliando a importância de cada variável na discriminação dos solos. A rede que exibiu melhor desempenho do índice Kappa foi utilizada para generalização de suas informações, obtendo os mapas digitais de solos. Pela aplicação de tabulação cruzada foram analisadas as correspondências espaciais entre os mapas digitais e um mapa convencional nível semidetalhado da região. Foram coletados pontos de referência para validar o desempenho dos mapas digitais. De acordo com a posição na paisagem e material de origem subjacente, notou-se tendência na ocorrência das classes de solos nas áreas chaves mapeadas. A mesma disposição dos solos foi observada nas classificações digitais. Os atributos do terreno elevação e declividade exibiram maior influência na distinção entre os solos pelas redes neurais em ambas as estratégias. A comparação com pontos de referência mostrou que o mapa digital produzido com base em unidades de mapeamento provenientes de abordagem convencional detalhada teve um desempenho superior (81,8% de concordância) ao mapa baseado em levantamento pedológico de nível semidetalhado (72,7%). Este estudo mostrou que a obtenção de mapas digitais de solos, com uso de variáveis ambientais que expressem a relação solo-paisagem, pode contribuir para a geração de informações preliminares do solo em locais não mapeados, a partir de unidades de mapeamento obtidas em áreas adjacentes. / The scarcity of land information to enable its proper use, whether for agricultural, environmental and urban design, can be minimized by solutions from the development of new technologies. Accordingly, this study aimed to apply two strategies to obtain digital maps of soil in areas where no preliminary surveys were carried out conventional pedological. The strategies were implemented based on environmental variables that establish relations between the occurrence of soils and their positions in the landscape. The study area comprised the municipality of Barra Bonita, SP, totaling 11,072 ha. For use in the prediction of soil by the technique of Artificial Neural Networks (ANN) were used variables: slope, elevation, profile curvature, plan curvature and convergence index derived from a Digital Elevation Model (DEM), in addition to information geology and geomorphic surfaces identified in the region. In the first strategy, through a cluster analysis (Fuzzy k-means) of variables, we selected five key areas distributed in the study area, soil survey being conducted semi-detailed level at these sites for recognition of the map units. In strategy 2, a map was drawn up detailed level of soil from pre-existing data of only one key area, located in the center of the region. Identifying the map units were generated files for training and testing of neural networks. Was used the simulator JavaNNS and learning algorithm \"backpropagation. Sets environmental variables were tested by assessing the importance of each variable to predict soil. The network showed better performance for the Kappa index was used to generalize their information, obtaining the digital soil maps. By applying cross tabulation analyzed the spatial correspondence between the digital maps and a conventional map of the region. Reference points were collected to validate the performance of digital maps. According to the position in the landscape and the underlying source material, was noticed a tendency of occurrence of soil classes in key areas mapped. The same arrangement was observed in the soil classifications digital. The attributes of the terrain elevation and slope exhibited a greater influence on the distinction between the soil by the neural networks in both strategies. The comparison with reference points showed that the digital map produced based on mapping units from the conventional approach detailed outperformed (81.8% agreement) to the map based on pedological survey of semi-detailed level (72.7 %). This study showed that to obtain digital maps of soils, use of environmental variables that express the soillandscape relationship, may contribute to the generation of information preeliminares soil in areas not mapped from map units obtained from adjacent areas.

Para a avaliação das características do solo relacionadas com o potencial uso dos solos, assim como para a avaliação da fertilidade, as análises químicas e físicas de rotina são os métodos convencionalmente usados. Estes são bastante custosos e demorados o que tem representado no Brasil uma dificuldade no seu uso por parte de pequenos agricultores, além da aplicabilidade da agricultura de precisão no manejo de solos. Atualmente a pedometria está fornecendo a possibilidade de incorporar em ciência do solo técnicas bastante sofisticadas que podem ajudar a diminuir o custo na obtenção da informação e compreender melhor o funcionamento dos processos do solo. Entre os tópicos mais recentes que estão incluídos na pesquisa relacionada com pedometria está a espectroscopia de reflectância. Embora se tenha demonstrado que uma grande quantidade de atributos podem ser estimados a partir da resposta espectral do solo via sensoriamento, ainda não se têm atingido níveis de acurácia ótimos em relação às metodologias convencionais, sobretudo no referente a atributos químicos. Para tanto, o presente trabalho foi desenvolvido com a finalidade de responder basicamente os seguintes questionamentos: a. Existem faixas espectrais específicas das bases trocáveis ou se estas podem mudar em função do argilomineral fornecedor da capacidade de troca de cátions?; b. A calibração de modelos usando unicamente algumas faixas espectrais específicas pode melhorar o desempenho destes?; c. Qual é a influência dos níveis de acurácia dos modelos espectrais sobre mapas construídos com atributos estimados a partir destes?; d. Como os tamanhos dos grupos de amostras de calibração influenciam a acurácia dos modelos?; e. Como a calibração de atributos relacionados com o intemperismo podem auxiliar no mapeamento de classes de solo? / The routine soil analysis is traditionally used on the evaluation of soil attributes related to land use potential, and the assessment of fertility. It is costly and time consuming, making it inaccessible for small farmers, and hampering the applicability of precision agriculture on soil management in Brazil. Currently, pedometrics is providing the possibility of incorporating in soil science sophisticated techniques that can help to reduce the cost of obtaining information and improve the understanding about how several soil processes works. One of the more recent topics on pedometrics is the soil reflectance spectroscopy. Through the soil reflected energy is possible to infer several soil properties, although optimum accuracy levels in the spectral estimation of soil attributes have not yet reached. In this sense, the aim of this study was basically answer the following questions: a. The exchangeable bases have specific spectral bands or the spectral responses of theses depends on the clay mineral?; b. the calibration of models by using only some specific spectral bands may improve the prediction performance?; c. What is the influence of the accuracy of prediction models on maps constructed with predicted soil attributes?; D. How calibration set size affect the accuracy of the models?; e. How the calibration of models for prediction of soil attributes related to soil weathering may assist the digital soil mapping?

Para a avaliação das características do solo relacionadas com o potencial uso dos solos, assim como para a avaliação da fertilidade, as análises químicas e físicas de rotina são os métodos convencionalmente usados. Estes são bastante custosos e demorados o que tem representado no Brasil uma dificuldade no seu uso por parte de pequenos agricultores, além da aplicabilidade da agricultura de precisão no manejo de solos. Atualmente a pedometria está fornecendo a possibilidade de incorporar em ciência do solo técnicas bastante sofisticadas que podem ajudar a diminuir o custo na obtenção da informação e compreender melhor o funcionamento dos processos do solo. Entre os tópicos mais recentes que estão incluídos na pesquisa relacionada com pedometria está a espectroscopia de reflectância. Embora se tenha demonstrado que uma grande quantidade de atributos podem ser estimados a partir da resposta espectral do solo via sensoriamento, ainda não se têm atingido níveis de acurácia ótimos em relação às metodologias convencionais, sobretudo no referente a atributos químicos. Para tanto, o presente trabalho foi desenvolvido com a finalidade de responder basicamente os seguintes questionamentos: a. Existem faixas espectrais específicas das bases trocáveis ou se estas podem mudar em função do argilomineral fornecedor da capacidade de troca de cátions?; b. A calibração de modelos usando unicamente algumas faixas espectrais específicas pode melhorar o desempenho destes?; c. Qual é a influência dos níveis de acurácia dos modelos espectrais sobre mapas construídos com atributos estimados a partir destes?; d. Como os tamanhos dos grupos de amostras de calibração influenciam a acurácia dos modelos?; e. Como a calibração de atributos relacionados com o intemperismo podem auxiliar no mapeamento de classes de solo? / The routine soil analysis is traditionally used on the evaluation of soil attributes related to land use potential, and the assessment of fertility. It is costly and time consuming, making it inaccessible for small farmers, and hampering the applicability of precision agriculture on soil management in Brazil. Currently, pedometrics is providing the possibility of incorporating in soil science sophisticated techniques that can help to reduce the cost of obtaining information and improve the understanding about how several soil processes works. One of the more recent topics on pedometrics is the soil reflectance spectroscopy. Through the soil reflected energy is possible to infer several soil properties, although optimum accuracy levels in the spectral estimation of soil attributes have not yet reached. In this sense, the aim of this study was basically answer the following questions: a. The exchangeable bases have specific spectral bands or the spectral responses of theses depends on the clay mineral?; b. the calibration of models by using only some specific spectral bands may improve the prediction performance?; c. What is the influence of the accuracy of prediction models on maps constructed with predicted soil attributes?; D. How calibration set size affect the accuracy of the models?; e. How the calibration of models for prediction of soil attributes related to soil weathering may assist the digital soil mapping?

No description available.

Orientadores: Mara de Andrade M. Weill, Alvaro Pires da Silva / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Agricola / Made available in DSpace on 2018-08-10T18:27:12Z (GMT). No. of bitstreams: 1 Silva_LauraFernandaSimoesda_M.pdf: 1032516 bytes, checksum: 40f9d7e8fc5ec74a07d3d13eacd211a4 (MD5) Previous issue date: 2007 / Resumo: O manejo agrícola, visando a produção de culturas, afeta a qualidade física do solo. O monitorament o da qualidade estrutural do solo pode ser realizado empregando atributos físicos selecionados e tomando por base um indicador de qualidade. O intervalo hídrico ótimo (IHO) é um indicador que integra os atributos físicos do solo que influenciam diretamente sua qualidade física, em especial no que concerne ao crescimento das culturas. Neste trabalho, foi desenvolvida uma aplicação do conceito do IHO, como uma ferramenta de planejamento do uso agrícola, de maneira a permitir comparações entre sistemas agrícolas. O objetivo geral do trabalho foi o de quantificar o IHO para o Latossolo Vermelho Distroférrico típico da região de Campinas-SP (Unidade de Mapeamento Barão Geraldo), aplicando-o na avaliação de diferentes sistemas de manejo agrícola. A pesquisa foi conduzida no Campo Experimental da Faculdade de Engenharia Agrícola da UNICAMP, em Campinas (SP), em parcelas experimentais cultivadas com milho e feijão sob Sistema Plantio Direto (SPD) e Sistema Convencional com Grade aradora (SC). Para determinação do IHO do solo, a curva de retenção de água (CRA), a curva de resistência do solo à penetração (RP) e a densidade do solo (Ds) foram determinadas para duas camadas, 0-0,20 m e 0,20-0,40 m. O monitoramento da qualidade física do solo foi efetuado com base em dados de densidade do solo, produtividade da cultura e indicadores biométricos do milho de três safras agrícolas consecutivas (2003/04; 2004/05; 2005/06). Na safra 2004/2005, também foram consideradas a densidade do solo, a umidade volumétrica e a produtividade da cultura de feijão, irrigado e não irrigado. Os resultados evidenciam que na camada 0,0-0,20 m, a umidade na capacidade de campo(ØCC) determinou o limite superior do IHO e a umidade no ponto de murcha permanente (ØPMP) o limite inferior até a Ds de 1,2 kg dm-3, quando então a umidade onde a resistência do solo à penetração atinge 2 Mpa (ØRP) é que passou a definir o limite inferior. Na camada 0,20-0,40 m, o limite inferior do IHO foi determinado pela ØRP em toda faixa de variação da Ds, sendo que a umidade a partir da qual a porosidade de aeração é inferior a 0,10 m3m-3 (ØPA) e a ØPMP não limitaram o valor do IHO. Os resultados indicam pior qualidade estrutural do solo em subsuperfície (0,20-0,40m). O monitoramento da cultura do milho indicou melhoria da qualidade física do solo sob SPD, a partir do segundo ano agrícola em relação ao solo sob SC, nas duas camadas estudadas. Os indicadores biométricos da cultura permitiram diferenciar os tratamentos desde o primeiro ano do ensaio, mostrando-se mais sensíveis às variações da qualidade física do solo. Para o feijoeiro o período em que o solo permaneceu fora das condições ideais de umidade estabelecidas pelo IHO foi suficiente para afetar diferencialmente a produtividade da cultura nos tratamentos não irrigados, com vantagem para o SPD. Conclui-se que o IHO, determinado para uma dada classe taxonômica de solo, constitui-se um indicador aplicável na avaliação da qualidade estrutural desse solo quando submetido a diferentes sistemas de manejo. O IHO pode ser utilizado no monitoramento da qualidade física do solo em sistemas agrícolas auxiliando na definição da necessidade de intervenção. A determinação do IHO para duas camadas permitiu maior compreensão dos efeitos do manejo agrícola na qualidade física do solo e na produtividade dos sistemas / Abstract: Agricultural management intended for field crop production affects physical soil quality. The monitoring of soil structural quality can be achieved using selected physical attributes and a quality indicator. The least limiting water range (LLWR) is an indicator that connects the physical soil attributes, which have influence on its physical quality especially for crop growth. In this work, it was developed an application of the LLWR concept as a tool for agricultural use planning, in order to making comparisons between agricultural systems. The central goal was to quantify the LLWR of the Red Latosol (Typic Hapludult) (Barão Geraldo Soil Mapping Unit) and to apply it on evaluation of different agricultural management systems. The research was conducted at the Experimental Station of the Agricultural Engineering College of UNICAMP in Campinas (SP), in experimental plots cultivated with maize under direct-drilling system (DDS) and conventional system with heavy harrow (CS) systems. For the LLWR determination, the soil water retention curve (WRC), the soil strength curve (SSC), and bulk density (Dd), were determined in two layers, 0-0,2m and 0,2-0,4m. The monitoring of soil physical quality was made using: bulk density data, maize productivities, and biometric indicators of maize from three consecutive production cycles (2003/04; 2004/05; 2005/06). The production cycle 2004/05, t they were also considered the bulk density data, volumetric soil water content and bean productivity data, with and without irrigation. At the layer 0,0-0,2m, the results show that soil water content at field capacity (ØCC) and soil water content at the wilting point (ØPMP) have determined respectively the superior and inferior limits of LLWR until the Ds=1,2 kg m-3, when soil water content at which the penetration resistance is 2 MPa (ØRP) has determined the inferior limit of LLWR. At the layer 0,2-0,4m, the inferior limit of LLWR was defined by the ØRP along the entire range of variation of bulk density, the water content when air-filled porosity 10% (ØPA) and ØPMP didn¿t reduce the LLWR. The results indicate that soil structural quality is worse at deeper layer (0,2-0,4m). The monitoring of crop maize shows improvement of soil physical quality under DDS, since the second production cycle in comparison with the CS in both layers. The biometric indicators of maize made possible the distinction between treatments since the first cycle of production; this suggests that they are more susceptible to the variations of physical soil quality than the productivity. The results acquired from the monitoring of winter crop (bean) indicate that the time, which the soil remained outside of the ideal conditions of humidity of the LLWR, was sufficient to affect adversely the productivity in the no-irrigated treatments, with advantage for the DDS. In conclusion, the LLWR determined for a given soil taxonomic class is an indicator to structural quality evaluation of the soil under distinct management systems. And, the LLWR can be used in the monitoring of soil physical quality to define the need for intervention. The LLWR determination for two soil layers provided more comprehension about the effects of agricultural management on soil physical quality and systems productivities / Mestrado / Agua e Solo / Doutor em Engenharia Agrícola

A escassez de informações do solo que permitam o seu uso adequado, seja para fins agrícola, ambiental ou de projeto urbanos, pode ser minimizada com soluções provenientes do desenvolvimento de novas tecnologias. Nesse sentido, o presente estudo teve como objetivo aplicar duas estratégias digitais para obtenção de mapas de solos preliminares em áreas onde não foram realizados levantamentos pedológicos convencionais. As estratégias foram executadas com base em variáveis ambientais que estabelecem relações entre ocorrência de solos e suas posições na paisagem. A área de estudo compreendeu o município de Barra Bonita-SP, totalizando 11.072 ha. Para uso na predição dos solos pela técnica de Redes Neurais Artificiais (RNA) foram utilizadas as variáveis: declividade, elevação, perfil de curvatura, plano de curvatura e índice de convergência derivados de um Modelo Digital de Elevação (MDE), além das informações de geologia e das superfícies geomórficas identificadas na região. Na primeira estratégia, por meio de uma análise de agrupamento (Fuzzy k-médias) das variáveis, foram escolhidas cinco áreas chaves distribuídas na área de estudo, nas quais foi realizado levantamento de solos de nível semidetalhado para reconhecimento das unidades de mapeamento. Na estratégia 2, elaborou-se um mapa de solos de nível detalhado a partir de dados pré-existentes de apenas uma área chave, localizada no centro da região. Com a identificação das unidades de mapeamento foram gerados arquivos de treinamento e testes das redes neurais. Utilizou-se o simulador JavaNNS e o algoritmo de aprendizado backpropagation. Conjuntos de variáveis ambientais foram testados, avaliando a importância de cada variável na discriminação dos solos. A rede que exibiu melhor desempenho do índice Kappa foi utilizada para generalização de suas informações, obtendo os mapas digitais de solos. Pela aplicação de tabulação cruzada foram analisadas as correspondências espaciais entre os mapas digitais e um mapa convencional nível semidetalhado da região. Foram coletados pontos de referência para validar o desempenho dos mapas digitais. De acordo com a posição na paisagem e material de origem subjacente, notou-se tendência na ocorrência das classes de solos nas áreas chaves mapeadas. A mesma disposição dos solos foi observada nas classificações digitais. Os atributos do terreno elevação e declividade exibiram maior influência na distinção entre os solos pelas redes neurais em ambas as estratégias. A comparação com pontos de referência mostrou que o mapa digital produzido com base em unidades de mapeamento provenientes de abordagem convencional detalhada teve um desempenho superior (81,8% de concordância) ao mapa baseado em levantamento pedológico de nível semidetalhado (72,7%). Este estudo mostrou que a obtenção de mapas digitais de solos, com uso de variáveis ambientais que expressem a relação solo-paisagem, pode contribuir para a geração de informações preliminares do solo em locais não mapeados, a partir de unidades de mapeamento obtidas em áreas adjacentes. / The scarcity of land information to enable its proper use, whether for agricultural, environmental and urban design, can be minimized by solutions from the development of new technologies. Accordingly, this study aimed to apply two strategies to obtain digital maps of soil in areas where no preliminary surveys were carried out conventional pedological. The strategies were implemented based on environmental variables that establish relations between the occurrence of soils and their positions in the landscape. The study area comprised the municipality of Barra Bonita, SP, totaling 11,072 ha. For use in the prediction of soil by the technique of Artificial Neural Networks (ANN) were used variables: slope, elevation, profile curvature, plan curvature and convergence index derived from a Digital Elevation Model (DEM), in addition to information geology and geomorphic surfaces identified in the region. In the first strategy, through a cluster analysis (Fuzzy k-means) of variables, we selected five key areas distributed in the study area, soil survey being conducted semi-detailed level at these sites for recognition of the map units. In strategy 2, a map was drawn up detailed level of soil from pre-existing data of only one key area, located in the center of the region. Identifying the map units were generated files for training and testing of neural networks. Was used the simulator JavaNNS and learning algorithm \"backpropagation. Sets environmental variables were tested by assessing the importance of each variable to predict soil. The network showed better performance for the Kappa index was used to generalize their information, obtaining the digital soil maps. By applying cross tabulation analyzed the spatial correspondence between the digital maps and a conventional map of the region. Reference points were collected to validate the performance of digital maps. According to the position in the landscape and the underlying source material, was noticed a tendency of occurrence of soil classes in key areas mapped. The same arrangement was observed in the soil classifications digital. The attributes of the terrain elevation and slope exhibited a greater influence on the distinction between the soil by the neural networks in both strategies. The comparison with reference points showed that the digital map produced based on mapping units from the conventional approach detailed outperformed (81.8% agreement) to the map based on pedological survey of semi-detailed level (72.7 %). This study showed that to obtain digital maps of soils, use of environmental variables that express the soillandscape relationship, may contribute to the generation of information preeliminares soil in areas not mapped from map units obtained from adjacent areas.

La compréhension de la répartition spatiale des sols et leur cartographie est un enjeu important tant les services écosystémiques rendus par les sols ont un rôle fondamental dans les enjeux agro-environnementaux actuels. A l’échelle nationale, les données pédologiques sont fournies via des cartographies au 1 :250 000 des types de sols (Référentiel Régional Pédologique, RRP) dont la résolution est devenue insuffisante pour répondre à ces enjeux. Placés dans un contexte de cartographie numérique des propriétés des sols à l’échelle régionale (Languedoc-Roussillon) caractérisé par une grande étendue (27 236 km²) et une faible densité de données sur les sols ( 1 observation/13.5 km2), les travaux de thèse ont eu pour objectif de réaliser une nouvelle infrastructure de données pédologiques régionale satisfaisant les spécifications édictées dans le projet international GlobalSoilMap et répondant aux besoins des utilisateurs de la région.Dans un premier temps, plusieurs approches connues de cartographie numérique des sols utilisant les diverses données pédologiques issues du RRP ont été appliquées et comparées entre elles. Les meilleurs résultats ont été obtenus par des approches de régression krigeage utilisant les profils avec analyses de sol existant dans le RRP. Pour le pH, le carbone organique et les variables de texture (argile, limon, sable) les performances de prédiction se sont avérés modérées mais suffisantes pour permettre la production de cartes informatives (R2 entre 0.2 et 0.7). En revanche les propriétés de sol avec une trop faible densité de profils et/ou variant sur des distances trop courtes (Eléments grossier, Profondeur, CEC) n’ont pu être prédites .Dans un deuxième temps, des méthodologies ont été proposées et testées pour mieux estimer les incertitudes de prédictions de propriétés de sol. Concernant les incertitudes locales, des progrès par rapport à l’utilisation de la régression krigeage ont été obtenus avec l’utilisation d’arbres de régression quantile. Ces incertitudes locales ont pu d’autre part être propagées dans les calculs d’indicateurs de sol caractérisant des entités géographiques de la région (exemple : commune). Enfin une troisième étape a été consacrée à la mise en production effective de la nouvelle infrastructure de données pédologique régionale permettant une diffusion des cartes obtenues dans cette thèse vers les utilisateurs.Les résultats de la thèse permettent de démontrer la faisabilité d’une approche de cartographie numérique des propriétés de sols à l’échelle régionale qui pourra être généralisée sur le territoire français. Bien que certains verrous méthodologiques restent à lever (ex : modèles de prédiction pour données censurées, covariable « lithologie »), la faible densité des observations pédologiques stockées actuellement en bases de données représente le facteur limitant majeur qui devra être levé dans l’avenir pour obtenir des cartes numériques de propriétés de sol à des précisions acceptables et incertitudes connues. / Depicting and mapping the soil variability is an important issue since the ecosystem services provided by soils play an important role in solving the current agro-environmental challenges. At the French national scale, the pedological data are currently provided by regional soil databases (« Référentiel Régionaux Pédologiques », RRP) at 1:250,000. However they provide soil information at a spatial resolution that is too coarse for addressing these challenges. This thesis undertakes a Digital Soil Mapping approach at the regional scale in a region (Languedoc-Roussillon) characterized by a great extent (27 236 km ²) and a low density of soil observations (1 observation/13.5 km2). The goal is to produce a new regional infrastructure of pedological data that could satisfy the specifications enacted in the international project GlobalSoilMap and that meets the needs of the local end-users. In a first step, several known approaches of digital soil mapping using the various pedological data available in the RRP were applied and compared. The best results were obtained by a regression-kriging approach using the legacy measured soil profiles of the RRP. For the pH, organic carbon and the variables of texture (clay, silt, sand) the performances of prediction were of moderate quality but sufficient to allow the production of informative maps (R2 between 0.2 and 0.7). Conversely the soil properties with a too low density of profiles and/or that varied within too short distances (coarse fragment, soil Depth, CEC) could not be predicted. In a second step, methodologies were proposed and tested for better estimating uncertainties of predictions of soil properties. Concerning local uncertainties, a progress compared to the use of Regression Kriging was obtained with the use of Quantile Regression Tree. These local uncertainties could in addition be propagated in calculations of soil indicators characterizing the geographical entities of the area (example: districts). Finally a third stage was devoted to the setting in effective production of the new regional infrastructure of pedological data, which allowed the diffusion of the maps obtained in this thesis towards the users. The results of the thesis demonstrate the feasibility of a digital soil mapping approach at the regional scale that could be generalized over the French territory. Although some methodological obstacles have to be addressed (ex: models of prediction for censored data, soil covariate “lithology”), the low density of the pedological observations currently stored in regional databases represents the major limiting factor, which will have to be addressed in the future to obtain digital maps of soil properties with acceptable and known precision.

Much older soils data and soils information lies idle in libraries and archives and is largely unused, especially in developing countries like Kenya. We demonstrated the usefulness of a stepwise approach to bring legacy soils data ‘back to life’ using the 1980 <i>Reconnaissance Soil Map of the Busia Area</i> <i>(quarter degree sheet No. 101)</i> in western Kenya as an example. Three studies were conducted by using agronomic information, field observations, and laboratory data available in the published soil survey report as inputs to several digital soil mapping techniques. In the first study, the agronomic information in the survey report was interpreted to generate 10 land quality maps. The maps represented the ability of the land to perform specific agronomic functions. Nineteen crop suitability maps that were not previously available were also generated. In the second study, a dataset of 76 profile points mined from the survey report was used as input to three spatial prediction models for soil organic carbon (SOC) and texture. The three predictions models were (i) ordinary kriging, (ii) stepwise multiple linear regression, and (iii) the Soil Land Inference Model (SoLIM). Statistically, ordinary kriging performed better than SoLIM and stepwise multiple linear regression in predicting SOC (RMSE = 0.02), clay (RMSE = 0.32), and silt (RMSE = 0.10), whereas stepwise multiple linear regression performed better than SoLIM and ordinary kriging for predicting sand content (RSME = 0.11). Ordinary kriging had the narrowest 95% confidence interval while stepwise multiple linear regression had, the widest. From a pedological standpoint, SoLIM conformed better to the soil forming factors model than ordinary kriging and had a narrower confidence interval compared to stepwise multiple linear regression. In the third study, rules generated from the map legend and map unit descriptions were used to generate a soil class map. Information about soil distribution and parent material from the map unit polygon descriptions were combined with six terrain attributes, to generate a disaggregated fuzzy soil class map. The terrain attributes were multiresolution ridgetop flatness (MRRTF), multiresolution valley bottom flatness (MRVBF), topographic wetness index (TWI), topographic position index (TPI), planform curvature, and profile curvature. The final result was a soil class map with a spatial resolution of 30 m, an overall accuracy of 58% and a Kappa coefficient of 0.54. Motivated by the wealth of soil agronomic information generated by this study, we successfully tested the feasibility of delivering this information in rural western Kenya using the cell phone-based Soil Explorer app (<a href="https://soilexplorer.net/">https://soilexplorer.net/</a>). This study demonstrates that legacy soil data can play a critical role in providing sustainable solutions to some of the most pressing agronomic challenges currently facing Kenya and most African countries.<div><p></p></div>

Pollution of surface and groundwater, due to improper land management, has become a major problem worldwide. Integrated watershed modelling provides a tool for the understanding of the processes governing water and matter transport at different scales within the watershed. The Soil Water Assessment Tool (SWAT) has been successfully utilized for the combined modelling of water fluxes and quality within a large range of scales and environmental conditions across the world. For suitable assessments integrated watershed models require large data sets of measured information for both model parameterization as for model calibration and validation. Data scarcity represents a serious limitation to the use of hydrologic models for supporting decision making processes, and may lead unsupported statements, poor statistics, misrepresentations, and, ultimately, to inappropriate measures for integrated water resources management efforts. In particular, the importance of spatially distributed soil information is often overlooked. In this thesis the eco-hydrological SWAT model was been applied to assess the water balance and diffuse pollution loadings of two rivers within a rural context at the mesoscale watershed level: 1) the Western Bug River, Ukraine, 2) the Águeda River, Portugal. Both watersheds in focus serve as examples for areas where the amount and quality of the measured data hinders a strait forward hydrologic modelling assessment. The Dobrotvir watershed (Western Bug River, Ukriane) is an example of such a region. In the former Soviet Union, soil classification primarily focused on soils of agricultural importance, whereas, forested, urban, industrial, and shallow soil territories were left underrepresented in the classification systems and resulting soil maps. Similarly the forest-dominated Águeda watershed in North-Central Portugal is a second example of a region with serious soil data availability limitations. Through the use of pedotransfer functions (PTFs) and the construction of soil-landscape models the data gaps could be successfully diminished, allowing a subsequent integrated watershed modelling approach. A valuable tool for the data gap closure was the fuzzy logic Soil Land Inference Model (SoLIM) which, combined with information from several soil surveys, was used to create improved maps. In the Dobrotvir watershed the fuzzy approach was used to close the gaps of the existing soil map, while in the Águeda watershed a new soil properties map, based upon the effective soil depths of the landscape, was constructed. While the water balance simulation in both study areas was successful, a calibration parameter ensemble approach was tested for the Águeda watershed. In the common modelling practice the individual best simulation and best parameter set is considered, the tested approach involved merging individual model outputs from numerous acceptable parameter sets, tackling the problematic of parameter equifinality. This procedure was tested for both original soil map and the newly derived soil map with differentiation of soil properties. It was noticeable that a better model set-up, with a better representation of the soil spatial distribution, was reflected in tighter model output spreads and narrower parameter distances. A further challenge was the calibration of water quality parameters, namely nitrate-N in the Dobrotvir watershed and sediment loads in the Águeda watershed. The limited amount of water quality observations were handled by assessing and by process verification at the smallest modelling unit, the hydrological response unit (HRU). The ruling hydrological processes could be depicted by combining own measured data and modelling outputs. The management scenario simulations showed the anticipated response to changes in management and reflected the rational spatial variation within the watershed reasonably well. The impacts of the different intervention options were evaluated on water balance, nitrate-N export and sediment yield at the watershed, sub-watershed and, when feasible, HRU level. This thesis covers two regional case studies with particular data limitations and specific processes of water and matter fluxes. Still, data reliability is a problem across the globe. This thesis demonstrates how relevant it is to tackle shortages of spatially differentiated soil information. The considered approaches contribute toward more reliable model predictions. Furthermore, the tested methods are transferable to other regions with differing landscape and climate conditions with similar problems of data scarcity, particularly soil spatially differentiated information.