In Situ Observation-Constrained Global Surface Soil Moisture Using Random Forest Model

The inherent biases of different long-term gridded surface soil moisture (SSM) products, unconstrained by the in situ observations, implies different spatio-temporal patterns. In this study, the Random Forest (RF) model was trained to predict SSM from relevant land surface feature variables (i.e., land surface temperature, vegetation indices, soil texture, and geographical information) and precipitation, based on the in situ soil moisture data of the International Soil Moisture Network (ISMN). The results of the RF model show an RMSE of 0.05 m3 m−3 and a correlation coefficient of 0.9. The calculated impurity-based feature importance indicates that the Antecedent Precipitation Index affects most of the predicted soil moisture. The geographical coordinates also significantly influence the prediction (i.e., RMSE was reduced to 0.03 m3 m−3 after considering geographical coordinates), followed by land surface temperature, vegetation indices, and soil texture. The spatio-temporal pattern of RF predicted SSM was compared with the European Space Agency Climate Change Initiative (ESA-CCI) soil moisture product, using both time-longitude and latitude diagrams. The results indicate that the RF SSM captures the spatial distribution and the daily, seasonal, and annual variabilities globally.

How to cite: Zhang, L.; Zeng, Y.; Zhuang, R.; Szabó, B.; Manfreda, S.; Han, Q.; Su, Z. In Situ Observation-Constrained Global Surface Soil Moisture Using Random Forest Model. Remote Sens. 202113, 4893. [pdf]

Planning the future of Harmonious

The Department of Topography and Cartography of the Technical University of Madrid hosted our work group meeting of COST Action – HARMONIOUS from 27 up to the 30 of October.

During this meeting the WG1 finalized the Glossary of terms used for UAS-based applications considering the three macro categories : platform and equipment, software and outputs.


1 Category: Platforms and Equipment 

  • Global Navigation Satellite System (GNSS) is a constellation of satellites used for positioning a receiver on the ground.
  • GALILEO is the GNSS European solution used to determine the ground position of an object.  
  • GPS is the most common GNSS based on the reception of signals from about 24 orbiting satellites by the USA, used to determine the ground position of an object. This global and accurate system allows users to know their exact location, velocity, and time 24 hours per day, anywhere in the world.    
  • Light Detection and Ranging (LiDAR) is based on laser pulses to locate the acquired point cloud in a 3D remote sensing. LiDAR data products are often managed within a gridded or raster data format.
  • Multispectral imaging captures image data within specific wavelength ranges across the electromagnetic spectrum.  The used spectral regions are often at least partially outside the visible spectral range, covering parts of the infrared and ultraviolet region. For example, a multi-spectral imager may provide wavelength channels for near-UV, red, green, blue, near-infrared, mid-infrared and far-infrared light – sometimes even thermal radiation.
  • Near Infrared (NIR) is a subset of the infrared band that is just outside the range of what humans can see. Applied to cameras, NIR cameras cover the wavelength range of 900 to 1700 nm, a range that is best suited for absorption and radiation characteristics analyses.
  • Noise    is an irregular fluctuation that accompanies a transmitted electrical signal but is not part of it and tends to obscure it. The main sources of noise can be divided into two main categories: the physical noise, linked to physics constraints like the corpuscular nature of light, and the hardware noise, linked to mechanical issues in the camera.
  • Optical Camera is a photographic device aimed to form and record an image of an object. An optical camera sensor is an imager that collects visible light (400~700nm).
  • Payload is the weight a drone or unmanned aerial vehicle (UAV) can carry on board. It is usually counted outside of the weight of the drone itself and includes anything additional to the drone – such as extra cameras, sensors, or packages for delivery.
  • Pixel size of an image identifies the spatial resolution and it is dependent on the sensor capabilities. It provides a measure of the image resolution, which is higher with finer grids, where the degree of recognizable details increases.
  • RGB Camera is equipped with a standard Complementary Metal Oxide Semiconductor (CMOS) sensor through which the colourful images of persons and objects are acquired. In a CMOS sensor, the charge from the photosensitive pixel is converted to a voltage at the pixel site and the signal is multiplied by row and column to multiple on chip Digital-to-Analog Converters (DACs). In a RGB camera, the acquisition of static photos is commonly expressed in megapixels that define the amount of pixels in a singular photo. While, the acquisition of videos is usually expressed with terms such as Full HD or Ultra HD.        
  • Thermal Camera is a non-contact temperature measurement sensor. All objects (above absolute zero) emit infrared energy as a function of their temperature. The vibration of atoms and molecules generates infrared energy. The higher the temperature of an object, the faster its molecules and atoms move. This movement is emitted as infrared radiation, which our eyes cannot see but our skin can feel (as heat). Thermal imaging uses special infrared camera sensors to illuminate a spectrum of light invisible to the naked eye. Thermal energy is invisible to the naked eye and works in different ways; it can be emitted, absorbed, or reflected. Infrared cannot see through objects but can detect differences in radiated thermal energy between materials. This is known as thermal bridging or heat transfer. 
  • Unmanned Aerial System (UAS) is a remotely controlled professional system integrating several technological components (e.g., navigation system, gyroscope, and sensors) in order to perform spatial observations.
  • Unmanned Aerial Vehicle (UAV) is a remotely controlled vehicle able to perform several operations and observations.

2 Software 

  • Aero-triangulation is the method most frequently applied to the photogrammetry to determine the X, Y, and Z ground coordinates of individual points based on photo coordinate measurements. The purpose of aero-triangulation is to increase the density of a geodetic network in order to provide images with an exhaustive number of control points for topographic mapping. Deliverables from aero-triangulation may be three-dimensional or planimetric, depending on the number of point coordinates determined.
  • Checkpoints are Ground Control Points (GCPs) used to validate the relative and absolute accuracy of the geo-localization of maps. The checkpoints are not used for processing. Instead, they are used to calculate the error of the map by comparing the known measured locations of the checkpoints to the coordinates of the checkpoints shown on the map.
  • Flight Type refers to the flight mission mode (manual or autonomous). In the manual mode, a pilot manages the UAS during the flight. The autonomous mission is programmed to react to various types of events, in a preset and direct way by means of special sensors. This makes UAS flight predictable and subject to intervention by a remote pilot, only if necessary.
  • Flight Time is a measurement of the total time needed to complete a mission, from the first to the last image taken during a flight. Flight time can be used to characterize the wind impacts on flight performance of UAS.    
  • Ground Control Points (GCPs) are user defined and priorly determined tie points within the mapping polygon used in the process of indirectly georeferencing UAS images. Such tie points can be permanent or portable markers with or without georeferenced data.
  • Masking is the procedure of excluding some part of the scene from image analysis. For instance, clouds, trees, bushes and their shadows should not be considered in further processing, such as in vegetation studies for the evaluation of crop vegetation indices.        
  • Orthorectification is a process of linearly scaling the image pixel size to real-world distances. This is achieved by accounting for the impacts of camera perspective and relative height above the sensed object. The objective is the reprojection of the original image, which could be captured from oblique viewing angles looking at unlevelled terrain, into an image plane to generate a distortion-free photo. 
  • Point Cloud is a collection of data points in a three-dimensional plane. Each point contains several measurements, including its coordinates along the X, Y, and Z-axes, and sometimes additional data such as a color value, which is stored in RGB format, and luminance value, which determines how bright the point is.
  • Radiometric Calibration is a process that allows the transformation of the intensities or digital numbers (DN) of multiple images in order to describe an area and detect relative changes of the landscape, removing anomalies due to atmospheric factors or illumination conditions. 
  • Structure from Motion (SfM) is the process of reconstructing a three-dimensional model from the projections derived from a series of images taken from different viewpoints. Camera orientation and scene geometry are reconstructed simultaneously through the automatic identification of matching features in multiple images.        
  • Tie Point is a point in a digital image or aerial photograph that can be found in the same location in an adjacent image or aerial photograph. A tie point is a feature that can be clearly identified in two or more images and selected as a reference point and whose ground coordinates are not known. The ground coordinates of Tie Points are computed during block triangulation. So, Tie points represent matches between key points detected on two (or more) different images and represent the link between images to get 3D relative positioning.
  • Precision is a description of random errors in the 2D/3D representations.
  • Quality Assessment is an estimation of the statistical geometric and radiometric errors of the final products obtained using ground true data.           

UAS-based Outputs

  • 2D Model is a bidimensional representation of the earth that contains 2 coordinates X and Y.
  • 3D Model is a mathematical or virtual representation of a three dimensional object.
  • 2.5D Model (Pseudo 3D Model) is a three-dimensional representation that uses X, Y coordinates, which are associated to a single elevation value in order to relate different points.
  • Digital Elevation Model (DEM) or Digital Height Model (DHM) is a gridded image describing the altitude of the earth excluding all other objects artificial or natural.    
  • Digital Surface Model (DSM) is a gridded image describing the altitude of the earth including all other objects artificial or natural. For instance, the DSM provides information about dimensions of buildings and forests.    
  • Digital Terrain Model (DTM) is a vector or raster dataset consisting of a virtual representation of the land environment in the mapping polygon. In a DTM the height of the point belongs to the bare ground.
  • Orthophoto is an aerial or terrestrial photograph that has been geometrically corrected to make the scale of the photograph uniform and use it as a map. Since each pixel of the orthophoto has a X and Y, it can be overlapped to other orthophotos, and it can be used to measure true distances of features within the photograph.        
  • Orthomosaic    is a high resolution image made by the combination of many orthophotos. It is a single, radiometrically corrected image that offers a photorealistic representation of an area that can produce surveyor-grade measurements of topography, infrastructure, and buildings.    
  • Feature Identification is a vector information computed from images using artificial intelligence algorithms in order to identify objects (roads, buildings, bridges, etc.) automatically. 
  • Point Cloud is a set of data points in space representing a three-dimensional object. Each point position has its set of Cartesian coordinates (X, Y, Z). It can be generated from overlapping images or LiDAR sensors.
  • Point Cloud Classification is the output of an algorithm that classifies the points of a cloud by computing a set of geometric and radiometric attributes.
  • Image Segmentation is a process that detects the features of an image clearly distinguishable based on the image texture and color.
  • Triangulated Irregular Network (TIN) is a pseudo three-dimensional representation obtained from the  relations in a point cloud using triangles.   
  • Vegetation Indices (VIs) are combinations of surface reflectance at two or more wavelengths designed to highlight a particular property of vegetation. VIs are designed to maximize sensitivity to the vegetation characteristics while minimizing confounding factors such as soil background reflectance, directional, or atmospheric effects. VIs can be found in the scientific literature under different forms such as NDVI, EVI, SAVI, etc.                
  • Aerial photograph is an image taken from an air-borne (i.e., UAS) platform using a precision camera. From aerial photographs, it is possible to derive qualitative information of the depicted areas, such as land use/land cover, topographical forms, soil types, etc. 
  • Terrestrial photograph is an image taken from the earth surface using a camera with an orientation that in most cases is not Nadiral.               

Mapping Water Infiltration Rate Using Ground and UAV Hyperspectral Data: A Case Study of Alento, Italy

Water infiltration rate (WIR) into the soil profile was investigated through a comprehensive study harnessing spectral information of the soil surface. As soil spectroscopy provides invaluable information on soil attributes, and as WIR is a soil surface-dependent property, field spectroscopy may model WIR better than traditional laboratory spectral measurements. This is because sampling for the latter disrupts the soil-surface status. A field soil spectral library (FSSL), consisting of 114 samples with different textures from six different sites over the Mediterranean basin, combined with traditional laboratory spectral measurements, was created. Next, partial least squares regression analysis was conducted on the spectral and WIR data in different soil texture groups, showing better performance of the field spectral observations compared to traditional laboratory spectroscopy. Moreover, several quantitative spectral properties were lost due to the sampling procedure, and separating the samples according to texture gave higher accuracies. Although the visible near-infrared–shortwave infrared (VNIR–SWIR) spectral region provided better accuracy, we resampled the spectral data to the resolution of a Cubert hyperspectral sensor (VNIR). This hyperspectral sensor was then assembled on an unmanned aerial vehicle (UAV) to apply one selected spectral-based model to the UAV data and map the WIR in a semi-vegetated area within the Alento catchment, Italy. Comprehensive spectral and WIR ground-truth measurements were carried out simultaneously with the UAV–Cubert sensor flight. The results were satisfactorily validated on the ground using field samples, followed by a spatial uncertainty analysis, concluding that the UAV with hyperspectral remote sensing can be used to map soil surface-related soil properties.

How to cite: Francos, N.; Romano, N.; Nasta, P.; Zeng, Y.; Szabó, B.; Manfreda, S.; Ciraolo, G.; Mészáros, J.; Zhuang, R.; Su, B.; Ben-Dor, E.  Mapping Water Infiltration Rate Using Ground and UAV Hyperspectral Data: a Case Study of AlentoItalyRemote Sensing13, 2606, (doi: 10.3390/rs13132606) 2021. [pdf]

Special Issue on RS entitled Global Gridded Soil Information Based on Machine Learning

This Special Issue is dedicated to machine learning-based methods in:•proximal and digital global mapping of soil properties (e.g., basic, hydraulic, thermal, functional, ecosystem services);•computing systems/algorithms/approaches using Earth observation data to derive global gridded soil datasets;•preprocessing Earth observation data to feed into global soil mapping;•data-intensive computing methods for incorporating Earth observation data for predictive soil mapping;•optimizing temporal resolution to globally track the changes of soil properties;•uncertainty assessment of the derived gridded soil information;•other related topics.

A 40% discount can be granted to papers received from this conference/project on the basis that the manuscript is accepted for publication following the peer review process.


Exploring the use of UAVs with the simplified ‘triangle’ technique for soil water content and evaporative fraction retrievals in a Mediterranean setting

Information acquired from Unmanned Aerial Vehicles (UAVs) is frequently used nowadays in a variety of disciplines and research fields. The present study explores for the first time the combined use of UAVs with a newly proposed technique for estimating evaporative fraction (EF) and surface soil moisture (SSM). The investigation is performed in a typical Mediterranean setting, a citrus field with flat topography divided in two plots with different irrigation schemes, in Sicily, Italy, at which ground data acquired during an extensive field campaign in July 2019. Reasonable estimates of both EF and surface wetness were produced, with patterns in agreement to vegetation cover fragmentation, topography, and other site-specific characteristics. Validation shows average error of 0.053 for EF and of 0.040 cm3 cm−3 for SSM. The results are comparable or better to those reported in analogous studies performed in similar areas. This implies that the investigated approach performs well under the semi-arid conditions characterizing the experimental set up. To our knowledge, this study represents the first evaluation of the combined use of the ‘simplified triangle’ with very high-resolution UAV imagery. As such, the findings are of significance regarding the potential future use of the ‘simplified triangle’ approach particularly with very fine resolution imagery such as that provided by UAV for mapping and monitoring EF and SSM in agricultural and natural ecosystems.

Figure: Maps of EF (a) and SSM (b) computed from the ‘simplified triangle’ implementation using the data retrieved with UAV.

How to cite: Petropoulos, G.P., A. Maltese, T. N. Carlson, G. Provenzano, A. Pavlides, G. Ciraolo, D. Hristopulos, F. Capodici, C. Chalkias, G. Dardanelli, S. Manfreda, Exploring the use of UAVs with the simplified “triangle” technique for Soil Water Content and Evaporative Fraction retrievals in a Mediterranean setting, International Journal of Remote Sensing, VOL. 42, NO. 5, 1623–1642, (doi: 10.1080/01431161.2020.1841319) 2021. [pdf]

A Geostatistical Approach to Map Near-Surface Soil Moisture Through Hyperspatial Resolution Thermal Inertia

Thermal inertia has been applied to map soil water content exploiting remote sensing data in the short and long wave regions of the electromagnetic spectrum. Over the last years, optical and thermal cameras were sufficiently miniaturized to be loaded onboard of unmanned aerial systems (UASs), which provide unprecedented potentials to derive hyperspatial resolution thermal inertia for soil water content mapping. In this study, we apply a simplification of thermal inertia, the apparent thermal inertia (ATI), over pixels where underlying thermal inertia hypotheses are fulfilled (unshaded bare soil). Then, a kriging algorithm is used to spatialize the ATI to get a soil water content map. The proposed method was applied to an experimental area of the Alento River catchment, in southern Italy. Daytime radiometric optical multispectral and day and nighttime radiometric thermal images were acquired via a UAS, while in situ soil water content was measured through the thermo-gravimetric and time domain reflectometry (TDR) methods. The determination coefficient between ATI and soil water content measured over unshaded bare soil was 0.67 for the gravimetric method and 0.73 for the TDR. After interpolation, the correlation slightly decreased due to the introduction of measurements on vegetated or shadowed positions (r² = 0.59 for gravimetric method; r² = 0.65 for TDR). The proposed method shows promising results to map the soil water content even over vegetated or shadowed areas by exploiting hyperspatial resolution data and geostatistical analysis.

How to cite: Paruta, A., P. Nasta, G. Ciraolo, F. Capodici, S. Manfreda, N. Romano, E. Bendor, Y. Zeng, A. Maltese, S. F. Dal Sasso and R. Zhuang, A geostatistical approach to map near-surface soil moisture through hyper-spatial resolution thermal inertia, IEEE Transactions on Geoscience and Remote Sensing, (doi: 10.1109/TGRS.2020.3019200) 2020. [pdf]

An Integrative Information Aqueduct to Close the Gaps between Satellite Observation of Water Cycle and Local Sustainable Management of Water Resources

The past decades have seen rapid advancements in space-based monitoring of essential water cycle variables, providing products related to precipitation, evapotranspiration, and soil moisture, often at tens of kilometer scales. Whilst these data effectively characterize water cycle variability at regional to global scales, they are less suitable for sustainable management of local water resources, which needs detailed information to represent the spatial heterogeneity of soil and vegetation. The following questions are critical to effectively exploit information from remotely sensed and in situ Earth observations (EOs): How to downscale the global water cycle products to the local scale using multiple sources and scales of EO data? How to explore and apply the downscaled information at the management level for a better understanding of soil-water-vegetation-energy processes? How can such fine-scale information be used to improve the management of soil and water resources? An integrative information flow (i.e., iAqueduct theoretical framework) is developed to close the gaps between satellite water cycle products and local information necessary for sustainable management of water resources. The integrated iAqueduct framework aims to address the abovementioned scientific questions by combining medium-resolution (10 m–1 km) Copernicus satellite data with high-resolution (cm) unmanned aerial system (UAS) data, in situ observations, analytical- and physical-based models, as well as big-data analytics with machine learning algorithms. This paper provides a general overview of the iAqueduct theoretical framework and introduces some preliminary results.

Concept Diagram

How to cite: Su, Z.; Zeng, Y.; Romano, N.; Manfreda, S.; Francés, F.; Dor, E.B.; Szabó, B.; Vico, G.; Nasta, P.; Zhuang, R.; Francos, N.; Mészáros, J.; Sasso, S.F.D.; Bassiouni, M.; Zhang, L.; Rwasoka, D.T.; Retsios, B.; Yu, L.; Blatchford, M.L.; Mannaerts, C. An Integrative Information Aqueduct to Close the Gaps between Satellite Observation of Water Cycle and Local Sustainable Management of Water Resources. Water 202012, 1495. [pdf]

Quantifying Long-term Land Surface and Root Zone Soil Moisture over Tibetan Plateau

It is crucial to monitor the dynamics of soil moisture over the Tibetan Plateau, while considering its important role in understanding the land-atmosphere interactions and their influences on climate systems (e.g., Eastern Asian Summer Monsoon). However, it is very challenging to have both the surface and root zone soil moisture (SSM and RZSM) over this area, especially the study of feedbacks between soil moisture and climate systems requires long-term (e.g., decadal) datasets. In this study, the SSM data from different sources (satellites, land data assimilation, and in-situ measurements) were blended while using triple collocation and least squares method with the constraint of in-situ data climatology. A depth scaling was performed based on the blended SSM product, using Cumulative Distribution Function (CDF) matching approach and simulation with Soil Moisture Analytical Relationship (SMAR) model, to estimate the RZSM. The final product is a set of long-term (~10yr) consistent SSM and RZSM product. The inter-comparison with other existing SSM and RZSM products demonstrates the credibility of the data blending procedure used in this study and the reliability of the CDF matching method and SMAR model in deriving the RZSM.

How to cite: Zhuang, R.; Zeng, Y.; Manfreda, S.; Su, Z. Quantifying Long-term Land Surface and Root Zone Soil Moisture over Tibetan Plateau. Remote Sens. 202012, 509. [pdf]

Estimating root zone soil moisture across the Eastern United States with passive microwave satellite data and a simple hydrologic model

Root zone soil moisture (RZSM) affects many natural processes and is an important component of environmental modeling, but it is expensive and challenging to monitor for relatively small spatial extents. Satellite datasets offer ample spatial coverage of near-surface (0-2 cm) soil moisture content at up to a daily time-step, but satellite-derived data products are currently too coarse in spatial resolution to use directly for many environmental applications, such as those for small catchments. This study investigates using passive microwave satellite soil moisture data products in a simple hydrologic model to provide root zone soil moisture estimates across a small catchment over a 2 year time-scale and the Eastern U.S. (EUS) at a 1 km resolution over a decadal time-scale. The physically based Soil Moisture Analytical Relationship (SMAR) is calibrated and tested with the Advanced Microwave Scanning Radiometer (AMSRE), Soil Moisture Ocean Salinity (SMOS), and Soil Moisture Active Passive (SMAP) data products. The SMAR spatial model relies on maps of soil physical properties and is first tested at the Shale Hills experimental catchment in central Pennsylvania. The model meets a root mean square error (RMSE) benchmark of 0.06 cm3 cm-3 at 66% of the locations throughout the catchment. Then, the SMAR spatial model is calibrated at up to 68 sites (SCAN and AMERIFLUX network sites) that monitor soil moisture across the EUS region, and maps of SMAR parameters are generated for each satellite data product. The average RMSE for RZSM estimates from each satellite data product is < 0.06 cm3 cm-3. Lastly, the 1 km EUS regional RZSM maps are tested with data from Shale Hills, which was set aside for validating the regional SMAR, and the RMSE between the RZSM predictions and the catchment average is 0.042 cm3 cm-3. This study offers a promising approach for generating long time-series of regional RZSM maps with the same resolution of soil property maps.

How to cite: Baldwin, D., S. Manfreda, H. Lin, and E.A.H. Smithwick, Estimating root zone soil moisture across the Eastern United States with passive microwave satellite data and a simple hydrologic model, Remote Sensing11, 2013, 2019. [pdf]

On the Use of Unmanned Aerial Systems for Environmental Monitoring

Environmental monitoring plays a central role in diagnosing climate and management impacts on natural and agricultural systems; enhancing the understanding of hydrological processes; optimizing the allocation and distribution of water resources; and assessing, forecasting, and even preventing natural disasters. Nowadays, most monitoring and data collection systems are based upon a combination of ground-based measurements, manned airborne sensors, and satellite observations. These data are utilized in describing both small- and large-scale processes, but have spatiotemporal constraints inherent to each respective collection system. Bridging the unique spatial and temporal divides that limit current monitoring platforms is key to improving our understanding of environmental systems. In this context, Unmanned Aerial Systems (UAS) have considerable potential to radically improve environmental monitoring. UAS-mounted sensors offer an extraordinary opportunity to bridge the existing gap between field observations and traditional air- and space-borne remote sensing, by providing high spatial detail over relatively large areas in a cost-effective way and an entirely new capacity for enhanced temporal retrieval. As well as showcasing recent advances in the field, there is also a need to identify and understand the potential limitations of UAS technology. For these platforms to reach their monitoring potential, a wide spectrum of unresolved issues and application-specific challenges require focused community attention. Indeed, to leverage the full potential of UAS-based approaches, sensing technologies, measurement protocols, post-processing techniques, retrieval algorithms, and evaluation techniques need to be harmonized. The aim of this paper is to provide an overview of the existing research and applications of UAS in natural and agricultural ecosystem monitoring in order to identify future directions, applications, developments, and challenges.

How to cite: Manfreda, S., M. F. McCabe, P. E. Miller, R. Lucas, V. Pajuelo Madrigal, G. Mallinis, E. Ben-Dor, D. Helman, L. Estes, G. Ciraolo, J. Müllerová, F. Tauro, M. I. de Lima, J. L. M. P. de Lima, A. Maltese, F. Frances, K. Caylor, M. Kohv, M. Perks, G. Ruiz-Pérez, Z. Su, G. Vico, and B. Toth, On the Use of Unmanned Aerial Systems for Environmental MonitoringRemote Sensing, 10(4), 641; (doi:10.3390/rs10040641) 2018.  [pdf