Lem benchmark database for tropical agricultural remote sensing application.

Abstract: The monitoring of agricultural activities at a regular basis is crucial to assure that the food production meets the world population demands, which is increasing yearly. Such information can be derived from remote sensing data. In spite of topic?s relevance, not enough efforts have been invested to exploit modern pattern recognition and machine learning methods for agricultural land-cover mapping from multi-temporal, multi-sensor earth observation data. Furthermore, only a small proportion of the works published on this topic relates to tropical/subtropical regions, where crop dynamics is more complicated and difficult to model than in temperate regions. A major hindrance has been the lack of accurate public databases for the comparison of different classification methods. In this context, the aim of the present paper is to share a multi-temporal and multi-sensor benchmark database that can be used by the remote sensing community for agricultural land-cover mapping. Information about crops in situ was collected in Luís Eduardo Magalhães (LEM) municipality, which is an important Brazilian agricultural area, to create field reference data including information about first and second crop harvests. Moreover, a series of remote sensing images was acquired and pre-processed, from both active and passive orbital sensors (Sentinel-1, Sentinel-2/MSI, Landsat-8/OLI), correspondent to the LEM area, along the development of the main annual crops. In this paper, we describe the LEM database (crop field boundaries, land use reference data and pre-processed images) and present the results of an experiment conducted using the Sentinel-1 and Sentinel-2 data

Challenges in data-based geospatial modeling for environmental research and practice

With the rise of electronic data, particularly Earth observation data, data-based geospatial modelling using machine learning (ML) has gained popularity in environmental research. Accurate geospatial predictions are vital for domain research based on ecosystem monitoring and quality assessment and for policy-making and action planning, considering effective management of natural resources. The accuracy and computation speed of ML has generally proved efficient. However, many questions have yet to be addressed to obtain precise and reproducible results suitable for further use in both research and practice. A better understanding of the ML concepts applicable to geospatial problems enhances the development of data science tools providing transparent information crucial for making decisions on global challenges such as biosphere degradation and climate change. This survey reviews common nuances in geospatial modelling, such as imbalanced data, spatial autocorrelation, prediction errors, model generalisation, domain specificity, and uncertainty estimation. We provide an overview of techniques and popular programming tools to overcome or account for the challenges. We also discuss prospects for geospatial Artificial Intelligence in environmental applications

A review of technical factors to consider when designing neural networks for semantic segmentation of Earth Observation imagery

Semantic segmentation (classification) of Earth Observation imagery is a crucial task in remote sensing. This paper presents a comprehensive review of technical factors to consider when designing neural networks for this purpose. The review focuses on Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), and transformer models, discussing prominent design patterns for these ANN families and their implications for semantic segmentation. Common pre-processing techniques for ensuring optimal data preparation are also covered. These include methods for image normalization and chipping, as well as strategies for addressing data imbalance in training samples, and techniques for overcoming limited data, including augmentation techniques, transfer learning, and domain adaptation. By encompassing both the technical aspects of neural network design and the data-related considerations, this review provides researchers and practitioners with a comprehensive and up-to-date understanding of the factors involved in designing effective neural networks for semantic segmentation of Earth Observation imagery.Comment: 145 pages with 32 figure

Désagrégation de l'humidité du sol issue des produits satellitaires micro-ondes passives et exploration de son utilisation pour l'amélioration de la modélisation et la prévision hydrologique

De plus en plus de produits satellitaires en micro-ondes passives sont disponibles. Cependant, leur large résolution spatiale (25-50 km) n’en font pas un outil adéquat pour des applications hydrologiques à une échelle locale telles que la modélisation et la prévision hydrologiques. Dans de nombreuses études, une désagrégation d’échelle de l’humidité du sol des produits satellites micro-ondes est faite puis validée avec des mesures in-situ. Toutefois, l’utilisation de ces données issues d’une désagrégation d’échelle n’a pas encore été pleinement étudiée pour des applications en hydrologie. Ainsi, l’objectif de cette thèse est de proposer une méthode de désagrégation d’échelle de l’humidité du sol issue de données satellitaires en micro-ondes passives (Satellite Passive Microwave Active and Passive - SMAP) à différentes résolutions spatiales afin d’évaluer leur apport sur l’amélioration potentielle des modélisations et prévisions hydrologiques. À partir d’un modèle de forêt aléatoire, une désagrégation d’échelle de l’humidité du sol de SMAP l’amène de 36-km de résolution initialement à des produits finaux à 9-, 3- et 1-km de résolution. Les prédicteurs utilisés sont à haute résolution spatiale et de sources différentes telles que Sentinel-1A, MODIS et SRTM. L'humidité du sol issue de cette désagrégation d’échelle est ensuite assimilée dans un modèle hydrologique distribué à base physique pour tenter d’améliorer les sorties de débit. Ces expériences sont menées sur les bassins versants des rivières Susquehanna (de grande taille) et Upper-Susquehanna (en comparaison de petite taille), tous deux situés aux États-Unis. De plus, le modèle assimile aussi des données d’humidité du sol en profondeur issue d’une extrapolation verticale des données SMAP. Par ailleurs, les données d’humidité du sol SMAP et les mesures in-situ sont combinées par la technique de fusion conditionnelle. Ce produit de fusion SMAP/in-situ est assimilé dans le modèle hydrologique pour tenter d’améliorer la prévision hydrologique sur le bassin versant Au Saumon situé au Québec. Les résultats montrent que l'utilisation de l’humidité du sol à fine résolution spatiale issue de la désagrégation d’échelle améliore la représentation de la variabilité spatiale de l’humidité du sol. En effet, le produit à 1- km de résolution fournit plus de détails que les produits à 3- et 9-km ou que le produit SMAP de base à 36-km de résolution. De même, l’utilisation du produit de fusion SMAP/ in-situ améliore la qualité et la représentation spatiale de l’humidité du sol. Sur le bassin versant Susquehanna, la modélisation hydrologique s’améliore avec l’assimilation du produit de désagrégation d’échelle à 9-km, sans avoir recours à des résolutions plus fines. En revanche, sur le bassin versant Upper-Susquehanna, c’est le produit avec la résolution spatiale la plus fine à 1- km qui offre les meilleurs résultats de modélisation hydrologique. L’assimilation de l’humidité du sol en profondeur issue de l’extrapolation verticale des données SMAP n’améliore que peu la qualité du modèle hydrologique. Par contre, l’assimilation du produit de fusion SMAP/in-situ sur le bassin versant Au Saumon améliore la qualité de la prévision du débit, même si celle-ci n’est pas très significative.Abstract: The availability of satellite passive microwave soil moisture is increasing, yet its spatial resolution (i.e., 25-50 km) is too coarse to use for local scale hydrological applications such as streamflow simulation and forecasting. Many studies have attempted to downscale satellite passive microwave soil moisture products for their validation with in-situ soil moisture measurements. However, their use for hydrological applications has not yet been fully explored. Thus, the objective of this thesis is to downscale the satellite passive microwave soil moisture (i.e., Satellite Microwave Active and Passive - SMAP) to a range of spatial resolutions and explore its value in improving streamflow simulation and forecasting. The random forest machine learning technique was used to downscale the SMAP soil moisture from 36-km to 9-, 3- and 1-km spatial resolutions. A combination of host of high-resolution predictors derived from different sources including Sentinel-1A, MODIS and SRTM were used for downscaling. The downscaled SMAP soil moisture was then assimilated into a physically-based distributed hydrological model for improving streamflow simulation for Susquehanna (larger in size) and Upper Susquehanna (relatively smaller in size) watersheds, located in the United States. In addition, the vertically extrapolated SMAP soil moisture was assimilated into the model. On the other hand, the SMAP and in-situ soil moisture were merged using the conditional merging technique and the merged SMAP/in-situ soil moisture was then assimilated into the model to improve streamflow forecast over the au Saumon watershed. The results show that the downscaling improved the spatial variability of soil moisture. Indeed, the 1-km downscaled SMAP soil moisture presented a higher spatial detail of soil moisture than the 3-, 9- or original resolution (36-km) SMAP product. Similarly, the merging of SMAP and in-situ soil moisture improved the accuracy as well as spatial representation soil moisture. Interestingly, the assimilation of the 9-km downscaled SMAP soil moisture significantly improved the accuracy of streamflow simulation for the Susquehanna watershed without the need of going to higher spatial resolution, whereas for the Upper Susquehanna watershed the 1-km downscaled SMAP showed better results than the coarser resolutions. The assimilation of vertically extrapolated SMAP soil moisture only slightly further improved the accuracy of the streamflow simulation. On the other hand, the assimilation of merged SMAP/in-situ soil moisture for the au Saumon watershed improved the accuracy of streamflow forecast, yet the improvement was not that significant. Overall, this study demonstrated the potential of satellite passive microwave soil moisture for streamflow simulation and forecasting

A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community

In recent years, deep learning (DL), a re-branding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, natural language processing, etc. Whereas remote sensing (RS) possesses a number of unique challenges, primarily related to sensors and applications, inevitably RS draws from many of the same theories as CV; e.g., statistics, fusion, and machine learning, to name a few. This means that the RS community should be aware of, if not at the leading edge of, of advancements like DL. Herein, we provide the most comprehensive survey of state-of-the-art RS DL research. We also review recent new developments in the DL field that can be used in DL for RS. Namely, we focus on theories, tools and challenges for the RS community. Specifically, we focus on unsolved challenges and opportunities as it relates to (i) inadequate data sets, (ii) human-understandable solutions for modelling physical phenomena, (iii) Big Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and learning algorithms for spectral, spatial and temporal data, (vi) transfer learning, (vii) an improved theoretical understanding of DL systems, (viii) high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote Sensin

LEM BENCHMARK DATABASE FOR TROPICAL AGRICULTURAL REMOTE SENSING APPLICATION

The monitoring of agricultural activities at a regular basis is crucial to assure that the food production meets the world population demands, which is increasing yearly. Such information can be derived from remote sensing data. In spite of topic’s relevance, not enough efforts have been invested to exploit modern pattern recognition and machine learning methods for agricultural land-cover mapping from multi-temporal, multi-sensor earth observation data. Furthermore, only a small proportion of the works published on this topic relates to tropical/subtropical regions, where crop dynamics is more complicated and difficult to model than in temperate regions. A major hindrance has been the lack of accurate public databases for the comparison of different classification methods. In this context, the aim of the present paper is to share a multi-temporal and multi-sensor benchmark database that can be used by the remote sensing community for agricultural land-cover mapping. Information about crops in situ was collected in Luís Eduardo Magalhães (LEM) municipality, which is an important Brazilian agricultural area, to create field reference data including information about first and second crop harvests. Moreover, a series of remote sensing images was acquired and pre-processed, from both active and passive orbital sensors (Sentinel-1, Sentinel-2/MSI, Landsat-8/OLI), correspondent to the LEM area, along the development of the main annual crops. In this paper, we describe the LEM database (crop field boundaries, land use reference data and pre-processed images) and present the results of an experiment conducted using the Sentinel-1 and Sentinel-2 data