Ground, Proximal, and Satellite Remote Sensing of Soil Moisture

Soil moisture (SM) is a key hydrologic state variable that is of significant importance for numerous Earth and environmental science applications that directly impact the global environment and human society. Potential applications include, but are not limited to, forecasting of weather and climate variability; prediction and monitoring of drought conditions; management and allocation of water resources; agricultural plant production and alleviation of famine; prevention of natural disasters such as wild fires, landslides, floods, and dust storms; or monitoring of ecosystem response to climate change. Because of the importance and wide‐ranging applicability of highly variable spatial and temporal SM information that links the water, energy, and carbon cycles, significant efforts and resources have been devoted in recent years to advance SM measurement and monitoring capabilities from the point to the global scales. This review encompasses recent advances and the state‐of‐the‐art of ground, proximal, and novel SM remote sensing techniques at various spatial and temporal scales and identifies critical future research needs and directions to further advance and optimize technology, analysis and retrieval methods, and the application of SM information to improve the understanding of critical zone moisture dynamics. Despite the impressive progress over the last decade, there are still many opportunities and needs to, for example, improve SM retrieval from remotely sensed optical, thermal, and microwave data and opportunities for novel applications of SM information for water resources management, sustainable environmental development, and food security

Review of the CALIMAS Team Contributions to European Space Agency's Soil Moisture and Ocean Salinity Mission Calibration and Validation

Camps, Adriano ... et al.-- 38 pages, 22 figuresThis work summarizes the activities carried out by the SMOS (Soil Moisture and Ocean Salinity) Barcelona Expert Center (SMOS-BEC) team in conjunction with the CIALE/Universidad de Salamanca team, within the framework of the European Space Agency (ESA) CALIMAS project in preparation for the SMOS mission and during its first year of operation. Under these activities several studies were performed, ranging from Level 1 (calibration and image reconstruction) to Level 4 (land pixel disaggregation techniques, by means of data fusion with higher resolution data from optical/infrared sensors). Validation of SMOS salinity products by means of surface drifters developed ad-hoc, and soil moisture products over the REMEDHUS site (Zamora, Spain) are also presented. Results of other preparatory activities carried out to improve the performance of eventual SMOS follow-on missions are presented, including GNSS-R to infer the sea state correction needed for improved ocean salinity retrievals and land surface parameters. Results from CALIMAS show a satisfactory performance of the MIRAS instrument, the accuracy and efficiency of the algorithms implemented in the ground data processors, and explore the limits of spatial resolution of soil moisture products using data fusion, as well as the feasibility of GNSS-R techniques for sea state determination and soil moisture monitoringThis work has been performed under research grants TEC2005-06863-C02-01/TCM, ESP2005-06823-C05, ESP2007-65667-C04, AYA2008-05906-C02-01/ESP and AYA2010-22062-C05 from the Spanish Ministry of Science and Innovation, and a EURYI 2004 award from the European Science FoundationPeer Reviewe

A review of current and potential applications of remote sensing to study the water status of horticultural crops

Published: 17 January 2020With increasingly advanced remote sensing systems, more accurate retrievals of crop water status are being made at the individual crop level to aid in precision irrigation. This paper summarises the use of remote sensing for the estimation of water status in horticultural crops. The remote measurements of the water potential, soil moisture, evapotranspiration, canopy 3D structure, and vigour for water status estimation are presented in this comprehensive review. These parameters directly or indirectly provide estimates of crop water status, which is critically important for irrigation management in farms. The review is organised into four main sections: (i) remote sensing platforms; (ii) the remote sensor suite; (iii) techniques adopted for horticultural applications and indicators of water status; and, (iv) case studies of the use of remote sensing in horticultural crops. Finally, the authors’ view is presented with regard to future prospects and research gaps in the estimation of the crop water status for precision irrigation.Deepak Gautam and Vinay Paga

The application of Earth Observation for mapping soil saturation and the extent and distribution of artificial drainage on Irish farms

Artificial drainage is required to make wet soils productive for farming. However, drainage may have unintended environmental consequences, for example, through increased nutrient loss to surface waters or increased flood risk. It can also have implications for greenhouse gas emissions. Accurate data on soil drainage properties could help mitigate the impact of these consequences. Unfortunately, few countries maintain detailed inventories of artificially-drained areas because of the costs involved in compiling such data. This is further confounded by often inadequate knowledge of drain location and function at farm level. Increasingly, Earth Observation (EO) data is being used map drained areas and detect buried drains. The current study is the first harmonised effort to map the location and extent of artificially-drained soils in Ireland using a suite of EO data and geocomputational techniques. To map artificially-drained areas, support vector machine (SVM) and random forest (RF) machine learning image classifications were implemented using Landsat 8 multispectral imagery and topographical data. The RF classifier achieved overall accuracy of 91% in a binary segmentation of artifically-drained and poorly-drained classes. Compared with an existing soil drainage map, the RF model indicated that ~44% of soils in the study area could be classed as “drained”. As well as spatial differences, temporal changes in drainage status where detected within a 3 hectare field, where drains installed in 2014 had an effect on grass production. Using the RF model, the area of this field identified as “drained” increased from a low of 25% in 2011 to 68% in 2016. Landsat 8 vegetation indices were also successfully applied to monitoring the recovery of pasture following extreme saturation (flooding). In conjunction with this, additional EO techniques using unmanned aerial systems (UAS) were tested to map overland flow and detect buried drains. A performance assessment of UAS structure-from-motion (SfM) photogrammetry and aerial LiDAR was undertaken for modelling surface runoff (and associated nutrient loss). Overland flow models were created using the SIMWE model in GRASS GIS. Results indicated no statistical difference between models at 1, 2 & 5 m spatial resolution (p< 0.0001). Grass height was identified as an important source of error. Thermal imagery from a UAS was used to identify the locations of artifically drained areas. Using morning and afternoon images to map thermal extrema, significant differences in the rate of heating were identified between drained and undrained locations. Locations of tiled and piped drains were identified with 59 and 64% accuracy within the study area. Together these methods could enable better management of field drainage on farms, identifying drained areas, as well as the need for maintenance or replacement. They can also assess whether treatments have worked as expected or whether the underlying saturation problems continues. Through the methods developed and described herein, better characterisation of drainage status at field level may be achievable

Statistical and Machine Learning Models for Remote Sensing Data Mining - Recent Advancements

This book is a reprint of the Special Issue entitled "Statistical and Machine Learning Models for Remote Sensing Data Mining - Recent Advancements" that was published in Remote Sensing, MDPI. It provides insights into both core technical challenges and some selected critical applications of satellite remote sensing image analytics

Contributions to land, sea, and sea ice remote sensing using GNSS-reflectometry

This PhD thesis researches the use of passive remote sensing techniques using signals transmitted from the navigation satellites (GNSS) in order to retrieve different geophysical parameters. The thesis consists of two different parts without taking into account the introduction, the state of the art and the conclusions. The first part analyzes the Interference Pattern Technique (IPT), which was previously used in another PhD thesis, and proposes some key improvements. First, the addition of horizontal polarization to the existing vertical polarization is proposed. Then, the retrieval of soil moisture is studied using the horizontal polarization only and combining both polarizations to correct for the surface roughness effects. It is also demonstrated that the phase difference between the two interference patterns is directly related to soil moisture content. A field campaign was conducted in Australia to test empirically all the theoretical developments and algorithms. Secondly, the possibility of measuring Significant Wave Height (SWH) and Mean Sea Surface Level (MSSL) using the IPT is studied. A three month field campaign over coastal sea is devoted to that study. The SWH retrieval is a new estimation algorithm based on measuring the point where the interference pattern loses coherence. The MSSL retrieval is based on the estimation of the IPT oscillation frequency, testing different spectral estimators to improve the accuracy. Since the IPT is limited in coverage due to its static requirements, the research conducted in this thesis migrated to scatterometric GNSS-R techniques. The main goal that migration was to increase coverage of the different GNSS-R instruments. Therefore, the second part of this thesis analyzes the applicability of a scatterometric technique from different platforms: ground-based (mobile and fixed), airborne, and spaceborne. The ground-based still platforms have allowed to develop a soil moisture retrieval algorithm. The ground-based moving platforms have extended the validity of that algorithm. Airborne platforms have been used to study the reflected electric field statistics when the surface reflecting surface is varying (smooth or rough land, and sea). They have also been used to develop different algorithms to measure the coherent and incoherent scattered components depending on the data structure (real-data or complex data). Coherent reflectivity measured from airborne platforms has been compared to other techniques such microwave radiometry, which is highly used in the soil moisture retrieval from spaceborne sensors, and other sensors using optical, multispectral and thermal frequency bands. These relationships between microwave radiometry and GNSS-R measurements suggests the potential synergy of both techniques. A sea ice detection algorithm is also developed using scatterometric GNSS-R data from the UK TDS-1 mission. This algorithm is based on measuring the degree of coherence of the reflected waveform. Finally, a field campaign was conducted to study the effect of vegetation on the GNSS signals that pass through it in order to take into account and correct the effect of vegetation in the GNSS-R data and in the soil moisture retrieval algorithms.Aquesta tesi doctoral aprofundeix en el coneixement de les tècniques de teledetecció passives utilitzant senyals emesos pels satèl·lits de navegació (GNSS) amb l'objectiu de recuperar diferents paràmetres geofísics del terreny. La tesi conté dues parts ben diferenciades a banda de la introducció, estat de l'art i conclusions. La primera part analitza la tècnica coneguda com a patró d'interferències, utilitzada prèviament en una altra tesi doctoral, i proposa certes millores per la seva aplicabilitat. En primer lloc es decideix afegir polarització horitzontal a la ja existent polarització vertical, i s'estudia la recuperació d'humitat del sòl utilitzant només polarització horitzontal i combinant les dues polaritzacions per corregir els efectes de la rugositat del terreny. A continuació es demostra que la mesura de desfasament entre els dos patrons d'interferència està directament relacionada amb la humitat del terreny. Es va realitzar una campanya de mesures a Austràlia per provar empíricament tots els desenvolupaments teòrics i algorismes proposats. En segon lloc s'analitza l'aplicabilitat del patró d'interferències en la mesura de l'altura de les onades (SWH) i del nivell del mar (MSSL), tots dos de forma precisa. L'estimació de l'alçada de les onades és un procés totalment nou basat en mesurar el punt on el patró d'interferències perd la coherència. L'estimació del nivell del mar es basa en l'anàlisi espectral del patró d'interferències provant diferents estimadors espectrals. Atès que la tècnica del patró d'interferència està limitada en cobertura per les seves característiques estàtiques, la investigació duta a terme en aquesta tesi doctoral va migrar cap a tècniques GNSS-R escateromètriques. El principal objectiu a assolir va ser el d'augmentar la cobertura dels diferents instruments GNSS-R de mesura. En conseqüència, la segona part d'aquesta tesi analitza l'aplicabilitat d'aquestes tècniques des de diferents plataformes terrestres (mòbils i fixes), aerotransportades i satèl·lit. Les plataformes terrestres fixes han permès derivar algoritmes de recuperació d'humitat i les mòbils estendre la validació d'aquests. Les plataformes aerotransportades s'han utilitzat per mirar l'estadística del camp elèctric reflectit quan la superfície on es reflecteixen els senyals GNSS va variant (terra plana o terra rugosa, i mar). També han servit per desenvolupar diferents algorismes amb l'objectiu de determinar les components coherent i incoherent del senyal reflectit. De la mateixa manera, dades de reflectivitat coherent mesurades des d'aquestes plataformes han estat comparades amb altres tècniques de teledetecció passiva com la radiometria de microones, altament utilitzada en la mesura d'humitat de terreny, i altres sensors òptics, multi-espectrals, i tèrmics. Aquests resultats han permès suggerir la possible sinergia de dades d'ambdues tecnologies. Un algorisme per detectar la presència de gel sobre el mar també ha estat desenvolupat mitjançant l'ús de dades GNSS-R escateromètriques satel·litals de la missió UK TDS-1. Aquest algorisme es basa en mesurar el grau de coherència de la forma d'ona reflectida. Finalment, s'ha realitzat un estudi de l'efecte de la vegetació en els senyals GNSS que la travessen, per tal de poder corregir aquest efecte en els algoritmes de recuperació d'humitat del terreny