Satellite Imagery to Map Topsoil Organic Carbon Content over Cultivated Areas: An Overview

There is a need to update soil maps and monitor soil organic carbon (SOC) in the upper horizons or plough layer for enabling decision support and land management, while complying with several policies, especially those favoring soil carbon storage. This review paper is dedicated to the satellite-based spectral approaches for SOC assessment that have been achieved from several satellite sensors, study scales and geographical contexts in the past decade. Most approaches relying on pure spectral models have been carried out since 2019 and have dealt with temperate croplands in Europe, China and North America at the scale of small regions, of some hundreds of km(2): dry combustion and wet oxidation were the analytical determination methods used for 50% and 35% of the satellite-derived SOC studies, for which measured topsoil SOC contents mainly referred to mineral soils, typically cambisols and luvisols and to a lesser extent, regosols, leptosols, stagnosols and chernozems, with annual cropping systems with a SOC value of similar to 15 g.kg(-1) and a range of 30 g.kg(-1) in median. Most satellite-derived SOC spectral prediction models used limited preprocessing and were based on bare soil pixel retrieval after Normalized Difference Vegetation Index (NDVI) thresholding. About one third of these models used partial least squares regression (PLSR), while another third used random forest (RF), and the remaining included machine learning methods such as support vector machine (SVM). We did not find any studies either on deep learning methods or on all-performance evaluations and uncertainty analysis of spatial model predictions. Nevertheless, the literature examined here identifies satellite-based spectral information, especially derived under bare soil conditions, as an interesting approach that deserves further investigations. Future research includes considering the simultaneous analysis of imagery acquired at several dates i.e., temporal mosaicking, testing the influence of possible disturbing factors and mitigating their effects fusing mixed models incorporating non-spectral ancillary information

Remote sensing for cropland soils at the regional scale

Uta Heiden, German Aerospace Center (DLR), addresses the challenges of observing soils from optical satellite platforms. She introduces the technique of soil composite mapping (SCMaP) that creates a reflectance signal for each bare soil pixel based on a time series of images. The advantage of such system is that the coverage of cropland area is much larger than for single images and that the signal is more stable for each pixel as a result of averaging. She addresses the challenges of defining thresholds for spectral indices allowing to create an optimal bare soil mask and demonstrates the SCMaP product suite. These products can be used for soil property algorithms but also for indicators of management practices such as bare soil frequency and seasonality

Aplicación del enfoque multi-índice con imágenes Sentinel-2 para obtener áreas urbanas en la estación seca (Zonas semiáridas en el noreste de Argelia)

[EN] The mapping of urban areas mostly presents a big difficulty, particularly, in arid and semi-arid environments. For that reason, in this research, we expect to increase built up accuracy mapping for Bordj Bou Arreridj city in semi-arid regions (North-East Algeria) by focusing on the identification of appropriate combination of the remotely sensed spectral indices. The study applies the ‘k–means’ classifier. In this regard, four spectral indexes were selected, namely normalized difference tillage index (NDTI) for built-up, and both bare soil index (BSI) and dry bare-soil index (DBSI), which are related to bare soil, as well as the normalized difference vegetation index (NDVI). All previous spectral indices mentioned were derived from Sentinel-2 data acquired during the dry season. Two combinations of them were generated using layer stack process, keeping both of NDTI and NDVI index constant in both combinations so that the multi-index NDTI/BSI/NDVI was the first single dataset combination, and the multi-index NDTI/DBSI/NDVI as the second component. The results show that BSI index works better with NDTI index compared to the use of DBSI index. Therefore, BSI index provides improvements: bare soil classes and built-up were better discriminated, where the overall accuracy increased by 5.67% and the kappa coefficient increased by 12.05%. The use of k-means as unsupervised classifier provides an automatic and a rapid urban area detection. Therefore, the multi-index dataset NDTI/ BSI / NDVI was suitable for mapping the cities in dry climate, and could provide a better urban management and future remote sensing applications in semi-arid areas particularly.[ES] La cartografía de las zonas urbanas presenta una gran dificultad, especialmente en los entornos áridos y semiáridos. Por esa razón, en esta investigación esperamos aumentar la precisión de la cartografía de la ciudad de Bordj Bou Arreridj en las regiones semiáridas (noreste de Argelia) centrándose en la identificación de la combinación adecuada de los índices espectrales obtenidos por teledetección. El estudio aplica el clasificador ‘k-means’. A este respecto, se seleccionaron cuatro índices espectrales, a saber, el índice de labranza de diferencia normalizada (NDTI) para el área construida, el índice de suelo desnudo (BSI) y el índice de suelo desnudo seco (DBSI), que están relacionados con el suelo desnudo, así como el índice de vegetación de diferencia normalizada (NDVI). Todos los índices espectrales anteriores mencionados se derivaron de datos Sentinel-2 adquiridos durante la estación seca (agosto). Se generaron dos combinaciones de ellas utilizando el proceso de superposición de capas, manteniendo constante tanto el índice NDTI como el índice NDVI en ambas combinaciones, de modo que el multi-índice NDTI/BSI/NDVI fue la primera combinación de conjuntos de datos, y el multi-índice NDTI/DBSI/NDVI fue el segundo componente. Los resultados muestran que el índice BSI funciona mejor con NDTI en comparación con el uso de DBSI. 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Soil Reflectance Composites - Improved Thresholding and Performance Evaluation

Reflectance composites that capture bare soil pixels from multispectral image data are increasingly being analysed to model soil constituents such as soil organic carbon. These temporal composites are used instead of single-date multispectral images to account for the frequent vegetation cover of soils and, thus, to get broader spatial coverage of bare soil pixels. Most soil compositing techniques require thresholds derived from spectral indices such as the Normalised Difference Vegetation Index (NDVI) and the Normalised Burn Ratio 2 (NBR2) to separate bare soils from all other land cover types. However, the threshold derivation is handled based on expert knowledge of a specific area, statistical percentile definitions or in situ data. For operational processors, such site-specific and partly manual strategies are not applicable. There is a need for a more generic solution to derive thresholds for large-scale processing without manual intervention. This study presents a novel HIstogram SEparation Threshold (HISET) methodology deriving spectral index thresholds and testing them for a Sentinel-2 temporal data stack. The technique is spectral indexindependent, data-driven and can be evaluated based on a quality score. We tested HISET for building six soil reflectance composites (SRC) using NDVI, NBR2 and a new index combining the NDVI and a short-wave infrared (SWIR) band (PV+IR2). A comprehensive analysis of the spectral and spatial performance and accuracy of the resulting SRCs proves the flexibility and validity of HISET. Disturbance effects such as spectral confusion of bare soils with non-photosynthetic-active vegetation (NPV) could be reduced by choosing grassland and crops as input LC for HISET. The NBR2-based SRC spectra showed the highest similarity with LUCAS spectra, the broadest spatial coverage of bare soil pixels and the least number of valid observations per pixel. The spatial coverage of bare soil pixels is validated against the database of the Integrated Administration and Control System (IACS) of the European Commission. Validation results show that PV+IR2-based SRCs outperform the other two indices, especially in spectrally mixed areas of bare soil, photosynthetic-active vegetation and NPV. The NDVI-based SRCs showed the lowest confidence values (95%) in all bands. In the future, HISET shall be tested in other areas with different environmental conditions and LC characteristics to evaluate if the findings of this study are also valid

Pedometric mapping of key topsoil and subsoil attributes using proximal and remote sensing in midwest Brazil

Tese (doutorado)—Universidade de Brasília, Faculdade de Comunicação, Programa de Pós-graduação em Comunicação, 2020.The Midwest region in Brazil has the largest and most recent agricultural frontier in the country, where there is no currently detailed soil information to support the land use intensification. Producing large-extent digital soil maps is resource intensive. We aimed to use pedometric techniques coupled with proximal and remote sensing data to produce digital maps with 30 m resolution of key soil attributes at topsoil and subsoil for 851,000 km2 of Midwest Brazil. For mapping key soil attributes we used multi-resolution covariates based on Earth observations: we produced composites of bare topsoil reflectance and potential natural vegetation reflectance using Landsat time series, which were coupled with terrain attributes, geologic and climate variables to capture short and long-range soil spatial patterns. We acquired soil data from observations at 0−20, 20−60 and 60−100 cm (rooting) depth intervals containing soil attributes, which are commonly used (as key criteria) for soil interpretations: clay, silt and sand, organic matter, pH, aluminum and base saturation. We also determined both the soil color in Munsell notation and the relative abundance of minerals in soil (hematite, goethite, kaolinite, gibbsite and 2:1 clay minerals) from laboratorial reflectance spectra (350−2500 nm). We fitted and validated optimal models for the spatial patterns of each soil attribute at topsoil and subsoil using Random Forest regression and 10-fold cross validation in R software. We identified the covariates that were most relevant to describe the soil spatial patterns in the study area. We mapped the spatial distribution of soil attributes at 30 m resolution for the 0−20, 20−60 and 60−100 cm depth intervals using the optimized models and Google Earth Engine. We made publicly available for download (GeoTIFF) at 250 m resolution the predicted soil maps of clay, silt and sand of the study area. We concluded that physical and chemical soil attributes, as well as soil color and mineralogy derived from spectra at multiple depth intervals, can be mapped using Earth observations data and machine learning methods with good performance.FAPDF, FAPESPA região Centro-Oeste do Brasil tem a maior e mais recente fronteira agrícola do país, onde atualmente não há informações detalhadas sobre o solo para apoiar a intensificação do uso do solo. A produção de mapas de solo digitais de grandes extensões é intensiva em recursos. O principal objetivo desta pesquisa foi usar técnicas pedométricas acopladas a dados de sensoriamento proximal e remoto para produzir mapas digitais com resolução de 30 m dos principais atributos do solo em superfície e subsuperfície para 850.000 km2 do Centro-Oeste do Brasil. Para mapear os principais atributos do solo, utilizamos covariáveis multi-resolução baseados em dados de observações da Terra: produzimos imagens compostas de refletância do solo exposto e de refletância da vegetação natural potencial usando séries temporais Landsat, que foram acoplados com atributos do terreno, variáveis geológicas e climáticas para capturar padrões espaciais do solo de curto e longo alcance. Adquirimos dados do solo a partir de observações em intervalos de profundidade (enraizamento) de 0–20, 20–60 e 60–100 cm, contendo atributos do solo que são comumente usados (como critério chave) para interpretações do solo: argila, silte e areia, matéria orgânica, pH, saturação de bases e de alumínio. Também determinamos a cor do solo em notação de Munsell e a abundância relativa de minerais no solo (hematita, goetita, caulinita, gibbsita e minerais de argila 2:1) a partir de espectros de laboratório (350–2500 nm). Foram ajustados e validados modelos ótimos para os padrões espaciais de cada atributo do solo em superfície e subsuperfície, usando regressão Random Forest e validação cruzada no software R. Identificamos as covariáveis mais relevantes que descreveram os padrões espaciais do solo na área de estudo. Mapeamos a distribuição espacial dos atributos do solo com resolução de 30 m para os intervalos de profundidade de 0-20, 20-60 e 60-100 cm usando os modelos otimizados e a plataforma Google Earth Engine. Disponibilizamos publicamente para consulta (GeoTIFF), com resolução de 250 m, os mapas de solo preditos de argila, silte e areia da área de estudo. Concluímos que atributos físicos e químicos do solo, assim como também a cor e a mineralogia do solo derivados de espectros de refletância, obtidos em múltiplos intervalos de profundidade, podem ser mapeados usando dados de observação da Terra e métodos de aprendizagem de máquinas com bom desempenho

Improving soil organic carbon predictions from a Sentinel–2 soil composite by assessing surface conditions and uncertainties

Soil organic carbon (SOC) prediction from remote sensing is often hindered by disturbing factors at the soil surface, such as photosynthetic active and non–photosynthetic active vegetation, variation in soil moisture or surface roughness. With the increasing amount of freely available satellite data, recent studies have focused on stabilizing the soil reflectance by building reflectance composites using time series of images. Although composite imagery has demonstrated its potential in SOC prediction, it is still not well established if the resulting composite spectra mirror the reflectance fingerprint of the optimal conditions to predict topsoil properties (i.e. a smooth, dry and bare soil). We have collected 303 photos of soil surfaces in the Belgian loam belt where five main classes of surface conditions were distinguished: smooth seeded soils, soil crusts, partial cover by a growing crop, moist soils and crop residue cover. Reflectance spectra were then extracted from the Sentinel–2 images coinciding with the date of the photos. After the growing crop was removed by an NDVI < 0.25, the Normalized Burn Ratio (NBR2) was calculated to characterize the soil surface, and a threshold of NBR2 < 0.05 was found to be able to separate dry bare soils from soils in unfavorable conditions i.e. wet soils and soils covered by crop residues. Additionally, we found that normalizing the spectra (i.e. dividing the reflectance of each band by the mean reflectance of all spectral bands) allows for cancelling the albedo shift between soil crusts and smooth soils in seed–bed conditions. We then built the exposed soil composite from Sentinel–2 imagery for southern Belgium and part of Noord-Holland and Flevoland in the Netherlands (covering the spring periods of 2016–2021). We used the mean spectra per pixel to predict SOC content by means of a Partial Least Squares Regression Model (PLSR) with 10–fold cross–validation. The uncertainty of the models was assessed via the prediction interval ratio (PIR). The cross validation of the model gave satisfactory results (mean of 100 bootstraps: model efficiency coefficient (MEC) = 0.48 ± 0.07, RMSE = 3.5 ± 0.3 g C kg–1, RPD = 1.4 ± 0.1 and RPIQ = 1.9 ± 0.3). The resulting SOC prediction maps show that the uncertainty of prediction decreases when the number of scenes per pixel increases, and reaches a minimum when at least six scenes per pixel are used (mean PIR of all pixels is 12.4 g C kg–1, while mean SOC predicted is 14.1 g C kg–1). The results of a validation against an independent data set showed a median difference of 0.5 g C kg–1 ± 2.8 g C kg–1 SOC between the measured (average SOC content 13.5 g C kg–1) and predicted SOC contents at field scale. Overall, this compositing method shows both realistic within field and regional SOC patterns

Applications of geographic information systems, remote-sensing, and a landscape ecology approach to biodiversity conservation in the Western Ghats

The mountains along the west coast of peninsular India, the Western Ghats, constitute one of the unique biological regions of the world. Rapidly occurring land-cover and land-use change in the Western Ghats has serious implications for the biodiversity of the region. Both landscape changes as well as the distribution of biodiversity are phenomena with strong spatial correlates. Recent developments in remote-sensing technology and Geographic Information Systems (GIS) allow the use of a landscape ecology and spatial analysis approach to the problem of deforestation and biodiversity conservation in the Western Ghats. Applications of this approach include analyses of land-cover and landuse change; estimation of deforestation rates and rates of forest fragmentation; examination of the spatial correlates of forest loss and the socioeconomic drivers of land-use change; modelling of deforestation; analysis of the consequences of land-cover and land-use change in the form of climate change and change in distribution of biodiversity; biomass estimation;gap analysis of the effectiveness of the protected area network in conserving areas of importance for biodiversity conservation; and conservation planning. We present examples from our work in the Western Ghats, in general, and in the Agastyamalai region and Biligiri Rangan Hills, in particular, as well as that of other researchers in India on various aspects of applications of GIS, remote sensing, and 'a landscape ecology approach to biodiversity conservation