Tecnologias de sensoriamento para mapeamento do solo superficial e avaliação das funções do solo

This doctoral dissertation deals with the use of proximal and remote sensing technologies for mapping soil properties and the possibilities of using these products in the study of the functions that the soil is capable to offer. Digital soil mapping has gained strength since the 90’s, when the first concepts of pedometrics and predictive soil mapping were proposed. Currently, there is a growing demand for soil maps and sensing technologies are playing a key role in making this possible. Considering this scenario, the objective of this dissertation is to introduce examples of soil attribute mapping using bare soil images obtained from satellite time series and how to use these data in mapping examples of other characteristics related to soil functions. In Chapter 1, a general introduction to the work is presented and addresses the main problems that are intended to be resolved with this thesis are addressed. Chapter 2 explores the properties of satellite imagery at different scales and their influence on obtaining maps of soil properties at the farm level. It was observed that, in fact, maps produced with images of different pixel sizes were different. The impact of these maps at different scales was evaluated both for soil classification purposes and for soil management and it was observed that the delineation of mapping units can be affected by the quality of soil property maps. The same situation was described for the case of soil management, where there may be an inconsistency in the spatial distribution of specific soil properties which can lead to a different management strategy depending on which type of map and at which scale it is used. Chapter 3 proposes the joining of data from two satellites to obtain larger areas of exposed soil that can enable a better study of soil variations, similar to chapter 2, but at the regional level, approaching soil variation from a different point of view. In this chapter, it was observed that advances in terms of greater availability of satellite images over time provide a better understanding of soil variations. Chapter 4 presents strategies for mapping drainage classes in tropical regions. This chapter was developed to assess the ability of soil property maps to study features that are more complex and difficult to measure. Finally, Chapter 5 provides a review of the possibilities of remote and proximal sensors for measuring and studying soil functions. Here we described sensors from the laboratory to satellites that cover the entire electromagnetic spectrum and how they can be used to study soils. In the final topic, three examples of application in Brazil are presented and this thesis is concluded by discussing the main advantages, limitations and what still needs to be done to advance in the study of the soil resource using the available technologies.Esta tese de doutorado trata do uso de tecnologias de sensoriamento remoto e proximal para o mapeamento de propriedades do solo e das possibilidades de uso destes produtos no estudo das funções que ele é capaz de oferecer. O mapeamento digital tem tomado força desde os anos 90’s quando foram propostos os primeiros conceitos de pedometria e mapeamento preditivo do solo. Atualmente, existe uma demanda crescente por mapas de solos e as tecnologias de detecção estão desempenhando um papel fundamental para que isto seja possível. Considerando este cenário, o objetivo desta tese é introduzir exemplos de mapeamento de atributos do solo utilizando imagens de solo exposto obtidas a partir de séries temporais de satélites e de como usar estes dados em exemplos de mapeamento de outros atributos do solo relacionadas às suas funções. No Capítulo 1 se expõe uma introdução geral do trabalho e se abordam as principais problemáticas que se pretendem resolver com esta tese. No Capítulo 2 se exploram as propriedades de imagens de satélite em diferentes escalas e sua influência na obtenção de mapas de propriedades de solo no nível de fazenda. Foi observado que, de fato, os mapas produzidos com imagens de tamanho de pixel diferente proporcionam mapas diferentes. O impacto destes mapas em diferentes escalas foi avaliado tanto para fins de classificação como para o manejo do solo e foi observado que o delineamento de unidades de mapeamento pode ser afetado pela qualidade dos mapas. A mesma situação foi descrita para o caso do manejo do solo, onde pode haver uma inconsistência na distribuição espacial de propriedades do solo específicas o que pode levar a uma estratégia de manejo diferente dependendo de qual tipo de mapa e em qual escala é utilizado. O capítulo 3 propõe a junção de dados de dois satélites para a obtenção de maiores áreas de solo exposto que possam possibilitar o melhor estudo da variação do solo, similar ao capítulo 2, porém no nível regional abordando a variação do solo desde um ponto de vista macro. Neste capítulo, foi observado que os avanços em termos de maior disponibilidade de imagens de satélite ao longo do tempo proporcionam um melhor entendimento das variações do solo. Já no capítulo 4 se apresentam estratégias para o mapeamento de classes de drenagem em regiões tropicais. Este capítulo foi desenvolvido com o intuito de avaliar a capacidade dos mapas de propriedades do solo para estudar características que são mais complexas e difíceis de estudar. Finalmente, o capítulo 5 proporciona uma revisão das possibilidades dos sensores remotos e proximais para a medição e estudo das funções do solo. Aqui se descrevem desde sensores de laboratório até satélites que cobrem toda a região do espectro eletromagnético e de como estes podem ser utilizados para estudar os solos. No tópico final se apresentam três exemplos de aplicação no Brasil e se conclui esta tese discutindo as principais vantagens, limitações e o que ainda precisa ser feito para avançar no estudo do recurso solo fazendo uso das tecnologias disponíveis

Soil Color and Mineralogy Mapping Using Proximal and Remote Sensing in Midwest Brazil

Soil color and mineralogy are used as diagnostic criteria to distinguish different soil types. In the literature, 350–2500 nm spectra were successfully used to predict soil color and mineralogy, but these attributes currently are not mapped for most Brazilian soils. In this paper, we provided the first large-extent maps with 30 m resolution of soil color and mineralogy at three depth intervals for 850,000 km2 of Midwest Brazil. We obtained soil 350–2500 nm spectra from 1397 sites of the Brazilian Soil Spectral Library at 0–20 cm, 20–60, and 60–100 cm depths. Spectra was used to derive Munsell hue, value, and chroma, and also second derivative spectra of the Kubelka–Munk function, where key spectral bands were identified and their amplitude measured for mineral quantification. Landsat composites of topsoil and vegetation reflectance, together with relief and climate data, were used as covariates to predict Munsell color and Fe–Al oxides, and 1:1 and 2:1 clay minerals of topsoil and subsoil. We used random forest for soil modeling and 10-fold cross-validation. Soil spectra and remote sensing data accurately mapped color and mineralogy at topsoil and subsoil in Midwest Brazil. Hematite showed high prediction accuracy (R2 > 0.71), followed by Munsell value and hue. Satellite topsoil reflectance at blue spectral region was the most relevant predictor (25% global importance) for soil color and mineralogy. Our maps were consistent with pedological expert knowledge, legacy soil observations, and legacy soil class map of the study region

Fine-scale soil mapping with Earth Observation data: a multiple geographic level comparison

Multitemporal collections of satellite images and their products have recently been explored in digital soil mapping. This study aimed to produce a bare soil image (BSI) for the São Paulo State (Brazil) to perform a pedometric analysis for different geographical levels. First, we assessed the potential of the BSI for predicting the surface (0.00-0.20 m) and subsurface (0.80-1.00 m) clay, iron oxides (Fe 2 O 3 ), aluminum (m%) and bases saturation (V%) contents at the state level, which are important properties for soil classification. In this task, legacy soil samples, the BSI and terrain attributes were employed in machine learning. In a second moment, we evaluated the capacity of the BSI for clustering the landscape at the regional level, comparing the predicted patterns with a legacy semi-detailed soil map from a smaller reference site. In the final stage, the predicted soil maps from the state level were investigated at the farm level considering several sites distributed across the São Paulo state. Our results demonstrated that clay and Fe 2 O 3 reached the best prediction performance for both depths at the state level, reaching a RMSE of less than 10 %, RPIQ higher than 1.6 and R 2 of at least 0.41. Additionally, the predicted landscape clusters had a significant association with the main pedological classes, subsurface color, soil mineralogy and texture from the legacy semi-detailed soil map. Illustrative examples at the farm level indicated great capacity of BSI in detecting the variations of soils, which were linked to several soil properties, such as texture, iron content, drainage network, among others. Therefore, this study demonstrates that BSI is valuable information derived from optical Earth Observation data that can contribute to the future of soil survey and mapping in Brazil (PronaSolos)

Drivers of Organic Carbon Stocks in Different LULC History and along Soil Depth for a 30 Years Image Time Series

Soil organic carbon (SOC) stocks are a remarkable property for soil and environmental monitoring. The understanding of their dynamics in crop soils must go forward. The objective of this study was to determine the impact of temporal environmental controlling factors obtained by satellite images over the SOC stocks along soil depth, using machine learning algorithms. The work was carried out in São Paulo state (Brazil) in an area of 2577 km2. We obtained a dataset of boreholes with soil analyses from topsoil to subsoil (0–100 cm). Additionally, remote sensing covariates (30 years of land use history, vegetation indexes), soil properties (i.e., clay, sand, mineralogy), soil types (classification), geology, climate and relief information were used. All covariates were confronted with SOC stocks contents, to identify their impact. Afterwards, the abilities of the predictive models were tested by splitting soil samples into two random groups (70 for training and 30% for model testing). We observed that the mean values of SOC stocks decreased by increasing the depth in all land use and land cover (LULC) historical classes. The results indicated that the random forest with recursive features elimination (RFE) was an accurate technique for predicting SOC stocks and finding controlling factors. We also found that the soil properties (especially clay and CEC), terrain attributes, geology, bioclimatic parameters and land use history were the most critical factors in controlling the SOC stocks in all LULC history and soil depths. We concluded that random forest coupled with RFE could be a functional approach to detect, map and monitor SOC stocks using environmental and remote sensing data