Remote sensing technology continues to play a significant role in the understanding of our environment. It has evolved into an integral research tool for the natural sciences. Disciplines such as climatology, hydrology, and studies of the terrestrial biosphere have all developed a strong remote sensing analysis component. Moreover, remote sensing has facilitated our understanding of the environment and its many processes over a broad range of spatial and temporal scales. This is a highly important aspect of land surface research, especially in the management of land and water resources and for the detection of environmental change.

Remote sensing applications have greatly enhanced our ability to monitor and manage our natural resources, especially in the areas of agriculture, ecosystems, and water resources. However, in spite of significant progress in recent years, there are still many areas where the potential of remote sensing has not been fully realized, and these are areas of active research.

Of unique importance are those efforts that are focused on gaining a better understanding of what sensors are actually measuring as well as new applications and inverse modelling techniques. For this Conference, contributions using visible, near- and thermal infrared, microwave and other wavebands are solicited, as well as applications using laser/LiDAR or hyperspectral imaging. The conference is especially interested in papers, which emphasize the use of data from Sentinel satellites, hyperspectral sensors and satellites (such as PRISMA), nanosatellites, airborne and Unmanned Aerial Systems (UAS) platforms, describing recent research results in the hydrological, agricultural and ecosystems sciences. Contributions are sought for state-of-the-art research and operational applications, in particular related to water cycle research and climate change. Invited keynote speakers will present overviews of problems, progress and prospects in key areas. Supporting papers are requested that review the latest contributions of Earth Observations (EO) to water cycle and soil-vegetation-atmosphere sciences from global to basin to field scales (e.g., precipitation, soil water content, water levels, surface water, groundwater, land and water mass and heat exchanges). Also assessing the advances and identify the needs in physical modeling, including uncertainties and consistency quantification and data assimilation of EO-based observations to improve our knowledge of water, vegetation and ecosystems processes and our ability to assess future changes in water cycle, extreme events and hydrological hazards.

The understanding of small-scale complex environmental systems is still a challenging problem due to interface between global and regional data sets. This is driven by the lack of in situ observations and the variety of downscaling techniques used to model the regional issues. These are the pre-requisites for addressing urban to regional problems such as agriculture health, water resource management, drought and food security.

In recent years, opportunities for big data analysis in food and agricultural production are arising. Technological advancements in remote sensing coupled with advances in IT, mobile/cloud computing (smart phones, wearable devices), wide spread adoption of GPS, internet of things and all advanced digital technologies have created a unique opportunity for implementing smarter solutions for large and smallholder farmers globally, leading to increased productivity, reduced resource consumption, and improved food security.

Moreover, geomatic engineering is a rapidly developing discipline that focuses on principles of spatial information and incorporating land surveying also for hydrological and agricultural remote sensing. These techniques allow for the delivery of high-tech agricultural services and precision agriculture based on remote sensing.

In addition, distributed networks provide the opportunity for setting up integrated processing for near real-time regional or global monitoring products for hydrology; agriculture; and ecosystems: e.g., HF radar networks, ground stations, GPS networks, flux towers, etc.

Modern technique for image processing and data analysis, with promising results and large potential, include deep learning and machine learning. These classes of algorithms have been successfully applied in various ecosystems.

Papers related to the above mentioned and the following topics are solicited:

Hydrological Sciences
  • hydro-geomatics (surveying work carried out above the surface areas of water and for hydrological applications)
  • hydrological modelling
  • sensors for monitoring water resources in hydrology
  • data scaling and data assimilation in hydrology (interpolation, smoothing and filtering applications)
  • water balance applications
  • soil water content
  • satellite-based rainfall estimation and modeling (e.g., meteorological RADAR)
  • precipitation, snow and ice hydrology
  • water resource management
  • drought monitoring, analysis and prediction
  • sedimentation and erosion
  • radar applications in hydrology (interferometry for land slide detection; canopy, soil moisture and soil roughness characterization; flooding)
  • lidar applications in hydrology
  • remote sensing in depth to ground water modeling and detection (passive and active microwaves, thermal infrared, gravimetry, ground penetrating radar)
  • remote sensing in surface water topography
  • water quality
  • estuarine and coastal applications
  • flood mapping and modeling
  • dams and hydraulic infrastructures monitoring via interferometry
  • studies of ice sheets: Cryosat, ICESat, IceBridge, GRACE, IceCube.

  • Agricultural Biosphere
  • agro-geomatics (geomatics techniques application for a precise management of agriculture)
  • smarter solutions for farmers based on IT, cloud computing, mobile technology, GPS
  • reflectance properties of soils
  • soil organic carbon content
  • crop yield modelling
  • food production, energy and water nexus
  • open data for agriculture and food production
  • water securing for food
  • agriculture disease detection
  • fluorescence applications in agriculture
  • canopy and leaf optical models
  • vegetation indices applications
  • biomass monitoring
  • evapotranspiration and energy balance (EB) applications
  • eddy covariance, surface renewal, Bowen ratio systems, scintillometry etc.

  • Ecosystems and Environmental Change
  • wildfire applications
  • forestry dynamics and carbon cycle studies
  • ecosystem and ecological management
  • climate modeling, prediction and environmental change
  • forecasting techniques
  • long-term data records for water cycle and climate
  • big data for sustainable development
  • regional and global vegetation monitoring early warning techniques
  • shallow and deep learning algorithms for ecosystems mapping
  • unmanned aerial systems (UAS) applications in hydrology, agriculture and ecosystems monitoring.

  • Joint Session
    The conference will organize a joint session with the “Microwave Remote Sensing: Data Processing and Applications” conference (Conference RS106). Contributions are solicited for the following topic: “Monitoring of soil moisture and vegetation biomass by using optical and microwave data”.;
    In progress – view active session
    Conference 11856

    Remote Sensing for Agriculture, Ecosystems, and Hydrology XXIII

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    View Session ∨
    • Remote Sensing Plenary Presentation I: Monday
    • Security+Defence Plenary Presentation
    • Remote Sensing Plenary Presentation II: Wednesday
    • Agriculture: Big Data and Policy
    • Mapping with Sentinel-2
    • Nitrogen and Chlorophyll Monitoring
    • Advances in Interpretation and Mapping
    • Energy Balance and Evapotranspiration
    • Vegetation Monitoring and Mapping
    • Yield Retrieval and Water Productivity
    • Water Monitoring Applications
    • UAV and Airborne Sensing
    • Climate, Drought, and Soil Water Content
    • Poster Session
    In addition to the pre-recorded on-demand presentations available for the presentations listing below, this conference will also hold a live-stream broadcast of its presentations.

    Pose your questions and join us for this unique opportunity for some interesting networking and discussion; plan to attend the conference live broadcast.
    If you are unable to take advantage of the live session, pre-recorded on-demand presentations will remain available through the digital forum duration.

    Tuesday, 14 September: 13:00 to 15:20 hrs CEST
    Wednesday, 15 September: 13:10 to 16:00 hrs CEST
    Thursday, 16 September: 15:00 to 17:40 hrs CEST
    Times for this live event are all Central European Summer Time, CEST (UTC+2:00 hours)

    Detailed schedule is listed in each conference session below.
    Link to join this live broadcast will be available to registered participants on this website starting on Tuesday, 14 September at 12:45 hrs CEST.
    Remote Sensing Plenary Presentation I: Monday
    Livestream: 13 September 2021 • 16:30 - 17:30 CEST
    Author(s): Pierluigi Silvestrin, European Space Research and Technology Ctr. (Netherlands)
    On demand | Presented Live 13 September 2021
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    In recent years the Earth observation (EO) programmes of the European Space Agency (ESA) have been dramatically extended. They now include activities that cover the entire spectrum of the wide EO domain, encompassing both upstream and downstream developments, i.e. related to flight elements (e.g. sensors, satellites, supporting technologies) and to ground elements (e.g. operations, data exploitation, scientific applications and services for institutions, businesses and citizens). In the field of EO research missions, ESA continues the successful series of Earth Explorer (EE) missions. The last additions to this series include missions under definition, namely Harmony (the tenth EE) and four candidates for the 11th EE: CAIRT (Changing Atmosphere InfraRed Tomography Explorer), Nitrosat (reactive nitrogen at the landscape scale), SEASTAR (ocean submesoscale dynamics and atmosphere-ocean processes), WIVERN (Wind Velocity Radar Nephoscope). On the smaller programmatic scale of the Scout missions, ESA is also developing two new missions: ESP-MACCS (Earth System Processes Monitored in the Atmosphere by a Constellation of CubeSats) and HydroGNSS (hydrological climate variables from GNSS reflectometry). Another cubesat-scale mission of technological flavor is also being developed, Φ-sat-2. Furthermore, in collaboration with NASA, ESA is defining a Mass change and Geosciences International Constellation (MAGIC) for monitoring gravity variations on a spatio-temporal scale that enables applications at regional level, continuing - with vast enhancements - the successful series of gravity mapping missions flown in the last two decades. The key features of all these missions will be outlined, with emphasis on those relying on optical payloads. ESA is also developing a panoply of new missions for other European institutions, namely Eumetsat and the European Union, which will be briefly reviewed too. These operational-type missions rely on established EO techniques. Nonetheless some new technologies are applied to expand functional and performance envelopes. A brief resume’ of their main features will be provided, with emphasis on the new Sentinel missions for the EU Copernicus programme.
    Security+Defence Plenary Presentation
    Livestream: 14 September 2021 • 09:00 - 10:00 CEST
    Author(s): Patrick R. Body, Tecnobit (Spain)
    On demand | Presented Live 14 September 2021
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    Optronic systems for the defence market are available from the UV to the LWIR wavelengths but the ideal band very much depends on the particular application and their environment. This lecture will cover some of the more important features of each type of optronic sensor and using examples from the experience gained over many years of system development by Tecnobit for Airborne, Navel and Land sectors, suggests some broad recommendations.
    Remote Sensing Plenary Presentation II: Wednesday
    Livestream: 15 September 2021 • 09:00 - 10:00 CEST
    Author(s): Adriano Camps, Institut d'Estudis Espacials de Catalunya (Spain)
    On demand | Presented Live 15 September 2021
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    Today, space is experiencing a revolution: from large space agencies, multimillion dollar budgets, and big satellite missions to spin-off companies, moderate budgets, and fleets of small satellites. Some have called this the “democratization” of space, in the sense that it is now more accessible than it was just a few years ago. To a large extent, this revolution has been fostered on one side by the standardization of the platforms’ mechanical interfaces, and on the other side by the technology developments coming from mobile communications. Standard platform’s mechanical interfaces have led to standard orbital deployers, and new launching capabilities. The technology developed for cell phones has brought more computing resources, with less power consumption and volume. Small satellites are used as pure technology demonstrators, for targeted scientific missions, mostly Earth Observation, some for Astronomy, and they are starting to enter in the field of communications, as huge satellite constellations are now becoming more possible. In this lecture, the most widely used nano/microsats form factors, and its main applications will be presented. Then, the main Scientific Earth Observation and Astronomy missions suitable to be boarded in SmallSats will be discussed, also in the context of the rising Constellations of SmallSats for Communication. Finally, the nanosat program at the Universitat Politècnica de Catalunya (UPC) will be introduced, and the results of the FSSCAT mission will be presented.
    Agriculture: Big Data and Policy
    Livestream: 14 September 2021 • 13:10 - 13:50 CEST
    Session Chair: Christopher M. U. Neale, Univ. of Nebraska-Lincoln (United States)
    In addition to the pre-recorded on-demand presentations available for the presentations listing below, this conference session will also hold a live-stream broadcast of its presentations.
    Times listed are Central European Summer Time, CEST (UTC+2:00 hours)

    13:10 hrs Welcome and Opening Remarks

    13:15 hrs 11856-1: MORERA: latest Earth observation system to translate big data to agriculture (Invited Paper)

    13:30 hrs 11856-2: Supporting the common agricultural policy with Sentinel-2 data and deep recurrent networks

    13:40 hrs 11856-3: Assessing existing methods and tools for agricultural paying agencies and how they can improve their procedures by using remote sensing

    For Sessions 2-11 timing please see the respective session listings.
    Author(s): Angel Alvaro Sanchez, Thales Alenia Space (Spain); José Antonio Sobrino, Univ. de València (Spain); Concepcion Mira Rodado, TEPRO Consultores Agrícolas S.L. (Spain); Victoria Gonzalez-Dugo, Instituto de Agricultura Sostenible (Spain); Tomás Belenguer Dávila, INTA Instituto Nacional de Técnica Aeroespacial (Spain); Andrés Cifuentes, ASE Optics Europe (Spain); Javier Moreno Raso, LIDAX (Spain)
    On demand
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    The MORERA program has recently been selected as one of the “Science and Innovation Missions” from the Spanish CDTI, an innovative program targeting solutions for deep social problems through innovation. The main Spanish industry is Agriculture (11% PIB), but this sector is threatened by climate change, as 34% of the Spanish irrigated surface is considered out of balance. The complexity of providing useful and fully processed information to the end users for supporting their decisions severely affect the optimization of the resources.. Well informed decisions optimize resources and costs, maximizing productivity. To solve this problem, the development of MORERA, an Earth Observation software-defined optical system, involves in a unique project the complete value chain, from sensor to user, thanks to a solid consortium: TASE (Systems Engineering, AI, video chain), TEPRO (user interface), IAS and UV (algorithms) ASE Optics Europe (optics), LIDAX (thermomechanics) and INTA (AIT), and it is based on three pillars: - A compact (Cubesat-compatible) and highly specific freeform optical instrument will be used to estimate evapotranspiration data at farm level with required TIR bandwidth and spatial resolution. Since no present instrument fulfills these requirements, it will be developed in the framework of the project. - Final personalized irrigation requirementsthat will be directly provided to the user using a mobile device. - Artificial intelligence (Machine learning, Big Data) techniques will be used to combine all relevant data (Evapotranspiration algorithms, Copernicus, Meteorological...) to build a final watering recommendation. The MORERA concept (compact sensor plus AI processing) can be extrapolated to many remote sensing applications, and to take advantage of this, it has been conceived as a modular system, where each module may be adapted with minor impact. This first system is focused on providing precise irrigation and fertilization recommendations, as well as self-learning yield estimations.
    Author(s): Manuel Campos-Taberner, Francisco Javier García-Haro, Beatriz Martínez, Sergio Sánchez-Ruiz, María Amparo Gilabert, Univ. de València (Spain)
    On demand
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    The 2020+ Common Agricultural Policy encourages the use of Copernicus remote sensing data for the monitoring of agricultural parcels. The main goal is to apply automated supervision for the vast majority (>95%) of agricultural territory, and address doubtful cases, which cannot be completely identified by remote sensing to traditional methods (e.g., in situ verification). Subsidies are payed depending on the farmers’ declarations, which indicate the extension of the parcels, agricultural activity, and land use. In this context, and with the starting of the new CAP regulation, the interest of land use classification from Sentinel-2 data has increased since it becomes key in order to verify farmers’ declarations. Land use identification from remote sensing data is a widely covered topic over the years. However, the identification of abandoned lands is an issue that lacks of studies since still presents some challenges from a remote sensing perspective, as it is a complex phenomenon. Moreover, the identification of abandoned lands is of great interest since the CAP does not provide any subside in this case. In this work, a procedure for automatic identification of land use from remote sensing data is proposed. The approach includes the use of spectral information of Sentinel-2 time series over the Valencia province (Spain) during the agronomic year of 2018, and deep learning recurrent networks. In particular, a bi-directional Long Short Term Memory (Bi-LSTM) network was trained to classify active land uses and abandoned lands. A comparison exercise was undertaken to assess the classification power of the Bi-LSTM as compared to the random forest (RF) algorithm. The Bi-LSTM network outperformed the RF, and provided and overall accuracy of 97.5% when discriminating nine land uses including abandoned lands. The results suggest the proposed methodology could potentially be implemented in an automated procedure to supervise the CAP requirements to access subsidies. In addition, the classification process also supports the continuous update of the Land Parcel Identification System (LPIS), which allows paying agencies to uniquely identify land parcels in space, store records of land uses (and assess its evolution), and ultimately ease the declaration procedure to both farmers and paying agencies.
    Mapping with Sentinel-2
    Livestream: 14 September 2021 • 13:50 - 14:40 CEST
    Session Chair: María Patrocinio González-Dugo, Instituto de Investigación y Formación Agraria y Pesquera (Spain)
    In addition to the pre-recorded on-demand presentations available for the presentations listing below, this conference session will also hold a live-stream broadcast of its presentations.
    Times listed are Central European Summer Time, CEST (UTC+2:00 hours)

    13:50 hrs 11856-5: Land cover mapping at national scale with Sentinel-2 and LUCAS: a case study in Portugal

    14:00 hrs 11856-6: A case of study of land surface phenology for CAP management: using Sentinel-2 data to obtain phenometrics for winter cereals in Andalusia, Spain.

    14:10 hrs 11856-7: Characterising the spring and autumn land surface phenology of Macaronesian species using Sentinel-2 data: the case of Canary Island

    Break: 14:20 to14:40 hrs

    For timing of sessions 1 & 3-11 see the respective session listings.
    Author(s): Pedro José Benevides, Direção-Geral do Território (Portugal); Nuno Silva, NOVA Information Management School (Portugal); Hugo Costa, Direção-Geral do Território (Portugal), NOVA Information Management School (Portugal); Francisco D. Moreira, Daniel Moraes, Direção-Geral do Território (Portugal); Mauro Castelli, NOVA Information Management School (Portugal); Mário Caetano, Direção-Geral do Território (Portugal), NOVA Information Management School (Portugal)
    On demand
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    The Land Use and Cover Area frame Survey (LUCAS) is a free land cover land use (LCLU) based in Europe. Earth Observation information from the Sentinel-2 satellites, which are also freely accessible, provide multi-spectral images with short revisit frequency. Machine learning algorithms, such as Random Forest (RF), have been widely used in supervised classification mapping applications with success. Experiments were carried out to investigate the use of LUCAS dataset from 2018 and Sentinel-2 imagery data to produce a land cover map in Portugal through automated supervised classification. The goal was to evaluate if LUCAS can be used as a single reference dataset for land cover classification at national level. The RF algorithm is used, and oversampling techniques with training data are also tested. To use LUCAS as a reference source for classification, some processing steps were undertaken. The adopted LCLU nomenclature is composed of 12 and 6 level-2 and level-1 map classes, respectively. Filtering was performed considering the attributes information from LUCAS survey, reducing the initial number of LUCAS points from 7168 to 4910. Monthly composites of Sentinel-2 images acquired over Portugal between October 2017 and September 2018 were used. Additional derived data is also computed, gathering a total of 285 input variables for classification. To reduce the imbalance in LUCAS training points, an oversampling technique based on Synthetic Minority Over-Sampling Technique (SMOTE) is used. An independent validation dataset is produced with 600 points. RF shows an overall accuracy (OA) of 57% for level-2 and 72% for level-1 nomenclatures. When using the oversampling technique, the OA accuracy increases by 3% for level-2 and 2% for level-1. The results show that RF is suitable to handle small and imbalanced training data such as LUCAS dataset and that the use of oversampling techniques in training data can improve the classification performance.
    Author(s): Miguel A. Garcia-Perez, Lorenzo Quesada-Ruiz, Jose A. Caparros-Santiago, Esperanza Sánchez-Rodríguez, Victor Rodriguez-Galiano, Univ. de Sevilla (Spain)
    On demand
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    The launch of the Copernicus Sentinel-2 mission offered new insights for the management of the European Common Agrarian Policy (CAP). Sentinel-2 provides information at a spatial and temporal resolution of 10 m and 5 days, respectively. However, this unprecedented time series of high resolution satellite imagery requires from approaches to extract meaningful agronomical information and reduce dimensionality. This could be the case of land surface phenology, which consists in estimating key phenometrics related to agronomical events from time series of vegetation indices (VIs). Knowing the dynamics of crop phenology is essential for the correct monitoring of CAP. We used EVI2 (Enhanced Vegetation Index 2) time series of Sentinel-2 data for the period 2018-2020. EVI2 is a VI widely used as an indicator of plant vigour, that avoids saturation in regions with high biomass. Double Logistic smoothing method was used to fill the gaps caused by the lack of images due to cloud presence or sensor failures. We selected plots of durum and common wheat, sorghum, barley and triticale according to the Geographical Information System for the CAP (GISCAP-CAP) declarations in Andalusia, Spain. The phenometrics extracted were start of the season (SOS), middle of the season (MOS), end of the season (EOS), their respective values of EVI2, and length of the season (LOS) (EOS-SOS). The aim of this study is to characterise the phenology of different winter cereals, through the extraction of phenometrics, and to evaluate whether these latter measures can serve to distinguish them. Results show that the response is quite similar between all of them, except sorghum. Common wheat shows the earliest SOS, followed by barley, durum wheat, triticale and sorghum. Common wheat shows the earliest EOS, followed by durum wheat, barley, triticale and sorghum
    Author(s): Lorenzo C. Quesada-Ruiz, Jose A. Caparros-Santiago, Miguel A. Garcia-Perez, Victor Rodriguez-Galiano, Univ. de Sevilla (Spain)
    On demand
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    Land surface phenology (LSP), the study of seasonal dynamics of vegetation analysing phenological metrics -phenometrics- derived from vegetation indices time series (VI), has emerged as an important research focus in recent decades as LSP patterns have been considered as an important ecological indicator for understanding the functioning of terrestrial ecosystems. LSP from high-spatial-resolution satellite imagery in ecosystems with significant heterogeneity of plant species, such as Macaronesian ecosystems, are needed for a better understanding on how these ecosystems function. The objective of this study was to monitor LSP dynamics of representative species of the Canary Islands: Olea Cerasiformis, Pistacia atlantica, Juniperus turbinata, Pinus canariensis, Myrica Faya and Erica arborea. NDVI (Normalised Difference Vegetation Index) Sentinel-2 time series at a spatial and temporal resolution of 10 meters and 5 days were generated for the 2018-2020 period. Atmospheric disturbances and noise were reduced using a double-logistic function. SOS (start of the growing season), EOS (end of the growing season) and LOS (length of the growing season) were extracted using a threshold-based method. Thermophilus species, such as Olea Cerasiformis and Pistacia atlantica had the SOS in October-November and the EOS between June and July. Juniperus turbinata showed double seasonality in La Palma, being the first growing season between November-December and April-May and the second growing season between May-June and September-October. Growing season of Pinus canariensis started in September-October and ended in April-June, nevertheless a double seasonality was observed in some locations of Pinus canariensis, probably associated to the understory. Subtropical laurel forest composed by different plant species, such as Myrica Faya and Erica arborea, did not show a clear seasonality. The species-specific LSP patterns for the Canary Islands can contribute to stablishing a baseline to monitor future impacts of climate change in Macaronesian biogeographical region.
    Nitrogen and Chlorophyll Monitoring
    Livestream: 14 September 2021 • 14:40 - 15:10 CEST
    Session Chair: Antonino Maltese, Univ. degli Studi di Palermo (Italy)
    In addition to the pre-recorded on-demand presentations available for the presentations listing below, this conference session will also hold a live-stream broadcast of its presentations.
    Times listed are Central European Summer Time, CEST (UTC+2:00 hours)

    14:40 hrs 11856-8: Estimation of chlorophyll content in radish leaves using hyperspectral remote sensing data and machine learning algorithms

    14:50 hrs 11856-9: Predicting leaf chlorophyll content using spectral indices based on the degree of linear polarization

    15:00 hrs 11856-11: Single photon infrared lidar imagers for long range, continuous and autonomous methane monitoring

    CANCELLED: 15:10 hrs 11856-12: Hyperspectral and thermal sensors for distinguishing between nitrogen and water stress in winter wheat

    For timing of sessions 1-2 & 4-11 see the respective session listings.
    Author(s): Adenan Yandra Nofrizal, Rei Sonobe, Hiroto Yamashita, Takashi Ikka, Akio Morita, Shizuoka Univ. (Japan)
    On demand
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    Radish (Raphanus sativus L) belongs to the family Brassicacease. It is popular root vegetable in both tropical and temperate regions. For monitoring of growth Radish plant, changes in chlorophyll content can be a good indicator of disease as well as nutritional and environmental stresses on plants. Chlorophyll pigments (chl) consist of two main types, a (chl a) and b (chl b). Radish (Raphanus sativus L.) plants were cultivated at within-row distances of 60 cm and inter-row spacing of 90 cm on a field at Shizuoka University (Shizuoka, Japan) with the total of 144 leaf sample. As a basal fertilization, 6 kg of N, P and K were supplied per 10 a. 120 kg of silicate fertilizer and 3.6g of boric acid were also supplied. The experiment included a control without slag and a slag fertilizer treatment. Remote sensing is one of the most attractive alternative options for this purpose and it has been revealed that hyperspectral data are useful for evaluating chlorophyll contents. Machine learning methods are powerful tools for estimating agriculture indices of hyperspectral remote sensing data. Especially, Random forest (RF) is a regression technique that combines numerous decisions trees to classify or predict the value of variable, has been used a reported its high performances for regression. Before a RF processed, preprocessing methods including first derivative reflectance (FDR), continuum-removed (CR), standard normal variated (SNV), multiplicative scatter correction (MSC), and de-trending (DT), were applied to original reflectance (OR) to improve regression model’s performance. The objectives of this study were (1) to evaluate the potential hyperspectral data for chlorophyll contents estimation and (2) to evaluate the best pre-processing method for reducing noised. Finally, Root mean square error (RMSE) and Ratio of Deviation shown First Derivative Reflectance (FDR) was the best preprocessing method for estimation of chlorophyll content in Radish plant
    Author(s): Murray K. Reed, QLM Technology Ltd. (United Kingdom)
    On demand
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    QLM are developing a novel remote gas imaging sensor for the detection, imaging, and quantification of methane and other greenhouse gas emissions. The sensor combines aspects of Tunable Diode Laser Absorption Spectroscopy (TDLAS) with Differential Absorption Lidar (DIAL) and Time Correlated Single Photon Counting (TCSPC) to enable remote spectroscopy and ranging with low power semiconductor diode lasers. We directly measure the shape of a gas absorption line by continuously sweeping the output wavelength of a diode laser across the line, and simultaneously modulate the laser output to encode the light signal and then use a digital time-domain correlation algorithm between the transmitted and detected light to identify the returned light. By simultaneously tuning the laser wavelength and modulating the amplitude it is possible to determine both the range the laser light has traveled, as with typical Lidar, and the amount of a particular gas that the laser light has passed through, as with typical TDLAS. Our methane sensors operate around the CH4 absorption line at 1650.9 nm. Tests with calibrated gas cells and controlled gas releases have demonstrated quantification of leak rates as low as 0.01 g/s with accuracy around 25% and detection at distances over 100 m. The accuracy, speed, and practicality of the sensor, combined with an expectation of low-cost in volume, offers the potential that these sensors can be effectively applied for widespread continuous monitoring of industrial methane emissions. We are developing a cloud-based server that automates the collection, analysis and reporting of gas data and provides an autonomous leak monitoring system that can accurately identifying emissions by position, size, and duration.
    Advances in Interpretation and Mapping
    Livestream: 15 September 2021 • 13:10 - 13:50 CEST
    Session Chair: Antonino Maltese, Univ. degli Studi di Palermo (Italy)
    In addition to the pre-recorded on-demand presentations available for the presentations listing below, this conference session will also hold a live-stream broadcast of its presentations.
    Times listed are Central European Summer Time, CEST (UTC+2:00 hours)

    13:10 hrs 11856-14: Deep learning for sub-pixel palm tree classification using spaceborne Sentinel-2 imagery

    13:20 hrs 11856-16: Google Earth Engine for land surface albedo estimation: comparison among different algorithms

    13:30 hrs 11856-17: On the sensitivity of snow bidirectional reflectance to variations in grain characteristics

    Break: 13:40 to 13:50 hrs

    For timing of sessions 1-3 & 5-11 see the respective session listings.
    Author(s): María Culman, KU Leuven (Belgium), VITO NV (Belgium); Andrés C. Rodríguez, Jan Dirk Wegner, ETH Zurich (Switzerland); Stephanie Delalieux, VITO NV (Belgium); Ben Somers, KU Leuven (Belgium)
    On demand
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    The challenge of classifying and locating Phoenix palm trees in different scenes, with different appearances, and with varied ages has been addressed with deep learning object detection over aerial images [1]. Nevertheless, an explicit limitation hereof is that palms should be visually identifiable in the image—i.e., palm crowns should be larger than the pixel size. Unfortunately, high-spatial resolution imagery is not extensible and directly available, and therefore, scarce in the Phoenix palm growing regions, such as the Mediterranean, Middle East, and North Africa. Thus, in this study, we present the implementation of a semantic segmentation architecture that uses convolutional neural networks to classify Phoenix palm pixels. This is applied to freely available medium-spatial resolution spaceborne Sentinel-2 images over the Spanish island of La Gomera (Canary Islands). At the study site, a total of 116,330 Phoenix (incl. canary, date, and hybrid palms) had been inventoried by the local government [2]. Palms appear in multiple, heterogeneous environments among the urban, agricultural, and natural landscapes like streets, gardens, nurseries, dry woodlands, and others. Such background variation is a persistent challenge for palm pixel classification tasks. The implemented architecture is based on a novel deep semantic density estimation method, originally developed for counting objects, such as cars, palm, and olive trees of sub-pixel size in Sentinel-2 satellite images [3]. This proved to be a successful method for classification, thereby compensating for the limited spatial resolution of the Sentinel-2 images. The semantic segmentation model for palm tree sub-pixel classification achieved an accuracy of 0.921, with a recall and precision of 0.438 and 0.522, on the validation dataset. These results demonstrate the potential of working with remote sensing data of medium-spatial resolution for vegetation mapping in applications where trees are sparse and unevenly distributed over extended areas with the support of a specifically designed architecture.
    Author(s): Alessandra Capolupo, Cristina Monterisi, Carlo Barletta, Eufemia Tarantino, Politecnico di Bari (Italy)
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    Albedo has long been recognized as a relevant bio-geophysical variable to model Earth surface and it was involved in all the climate simulation models. Therefore, the correct modelling of albedo is essential to reduce the error propagation in the prediction algorithms. To meet such a purpose, different methods have been developed over the past years. Among them, the simplified approach proposed by Liang in 2000 and the corrected algorithm introduced by Silva et al. (2016) are commonly used. To the best of our knowledge, the outcomes produced by applying such techniques have not been investigated yet. The present paper is intended to explore the potentialities of Google Earth Engine (GEE) platform in estimating land surface albedo from three medium-resolution geospatial data gathered by different Landsat sensors in diverse acquisition periods. Java-script code was developed to numerically implement the above-mentioned algorithms in GEE environment. Their performances were compared and the error committed adopting the simplified method was quantified. As a result, the corrected algorithm reported more accurate values. Nevertheless, its complexity implies a high implementation difficulty and, consequently, a higher processing time is required to handle the data. Conversely, the simplified approach allowed to estimate land surface albedo in a short time. Quantifying the error committed using the simplified approach allows us to correct its results, improving their accuracy. Although obtained results are preliminary, this research enhanced the possibility to model the albedo by adopting the simplified algorithm after correcting it. This implies to reduce error propagation and, simultaneously, to speed up the data handling.
    Author(s): Petri M. Varsa, Gladimir V. G. Baranoski, Univ. of Waterloo (Canada)
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    Snow is a ubiquitous natural material that plays an important role in Earth’s climatological system and energy resource budget. Its insular and reflective properties are key factors contributing to the radiation budget of the cryosphere. Due to its prevalence at extreme latitudes, the monitoring of snowpack quantities is often performed via remote observation. These data are acquired using either satellite readings or by fixing instruments to the underside of aircraft. When acquiring data remotely, it is important to account for the angular configuration of the source illumination and the location of the instrument relative to the surface since reflection is affected by the geometry of the observation. In other words, the bidirectional reflectance distribution function (BRDF) depends on the angle of incidence of the solar illumination and the angle of observation in addition to the wavelength of the incident light. It has been recognized that the granular properties of a snowpack markedly influence its BRDF. Unfortunately, works examining the effects of snow grain characteristics, such as size and facetness, on BRDF outputs are still scarce. Moreover, measured BRDF values from field studies presented in the literature are limited to specific target samples. This further hinders a more comprehensive understanding of the effects of changes in snow characterization parameters on the bidirectional reflectance of snowpack. The measured datasets often do not provide a detailed characterization of the target samples either, which also reduces their usefulness for elucidating these effects. To address these limitations and enhance the current understanding about the sensitivity of snow BRDF to variations in grain characteristics, we have conducted controlled experiments employing a first-principles in silico experimental framework supported by measured data. Our findings unveil the qualitative effects that snow granular properties have on bidirectional reflectance of snowpack, and highlight the importance of accounting for snow granular properties in remote sensing applications. In addition, our in silico experiments provide a high-fidelity assessment of snow BRDF with respect to key wavelengths particularly relevant for remote sensing applications. More broadly, our investigation demonstrates how remote observations of snow-covered terrains can be significantly improved by the correct incorporation of snow grain characteristics into the bidirectional reflectance models used to assess snowpack properties.
    Energy Balance and Evapotranspiration
    Livestream: 15 September 2021 • 13:50 - 14:50 CEST
    Session Chair: María Patrocinio González-Dugo, Instituto de Investigación y Formación Agraria y Pesquera (Spain)
    In addition to the pre-recorded on-demand presentations available for the presentations listing below, this conference session will also hold a live-stream broadcast of its presentations.
    Times listed are Central European Summer Time, CEST (UTC+2:00 hours)

    13:50 hrs 11856-18: Comparative analysis of evapotranspiration using the SEBAL model and the evaporimeter pan method in the Huancane basin of Puno, Peru

    14:00 hrs 11856-20: Comparison of modeled evapotranspiration from the SETMI hybrid model informed with multispectral and thermal infrared imagery acquired with an unmanned aerial system

    14:10 hrs 11856-21: Development of GIS models via optical programming and python scripts to implement four empirical methods of reference and actual evapotranspiration (ETo, ETa) incorporating MODIS LST inputs

    14:20 hrs 11856-19: Remote sensing and GIS-based approaches to estimate evapotranspiration in the arid and semi-arid regions

    Break: 14:30 to 14:50

    For timing of sessions 1-4 & 6-11 see the respective session listings.
    Author(s): Danny X. Aroni-Quispe, Roberto Alfaro-Alejo, Hector A. Huaman-Gutierrez, German Belizario-Quispe, Univ. Nacional del Altiplano (Peru)
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    Remote sensing methods allow obtaining important information from the Earth's surface to effectively evaluate agricultural processes. This research work proposes to carry out the comparative analysis of evapotranspiration using the methodologies of the SEBAL model and the evaporimeter pan in the Huancané basin, Peru. The specific objectives were to estimate the evapotranspiration through the SEBAL model from Landsat 8 images, to estimate the real evapotranspiration through the evaporimeter pan method from meteorological data from the Huancané and Muñani stations, for the comparison and validation of the evapotranspiration results obtained. with the SEBAL model. The methodological stages considered are: the collection of meteorological data from the National Meteorology and Hydrology Service (SENAMHI) of Peru and Landsat 8 satellite images, the application of the SEBAL algorithm and the evaporimeter pan method, to obtain evapotranspiration. For the SEBAL model, the lowest evapotranspiration values correspond to areas with soils without crops or with low vegetation cover (NDVI < 0.21) and for areas covered with vegetation or grasslands (NDVI > 0.41) obtaining values between 1.50 to 4.20 mm/day. The evaporimeter pan method allowed to determine the real evapotranspiration (ETR), for the points of location of the weather stations, values that vary between 1.76 to 2.44 mm/day. The comparison and validation of the evapotranspiration values observed (ETR evaporimeter pan) and estimated (ETR SEBAL), for the analysis areas near the Huancane station present a mean square error of 0.26 and 0.25, coefficient of determination of 0.97 and a Nash-Sutcliffe efficiency of 0.81 and 0.83. Likewise, for the areas near the Muñani station where they show a mean square error of 0.13 and 0.14, coefficient of determination of 0.97 and 0.93; and a Nash-Sutcliffe efficiency of 0.81 and 0.82. The results obtained with the SEBAL model are satisfactory, which shows that its use is feasible.
    Author(s): Fares M. Howari, Manish Sharma, Cijo M. Xavier, Yousef Nazzal, Imen Ben Salem, Zayed Univ. (United Arab Emirates); Fatima Al Aydaroos, United Arab Emirates Space Agency (United Arab Emirates)
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    It is essential that the knowledge on evapotranspiration estimation play a vital role in the management of water resources and for management of agriculture. There are various observation parameters involved in the traditional estimation of ET, which is challenging for larger areas. In this paper we used two methodologies of remote sensing data and its derived variables for the estimation of ET. In the first method, the sensible heat flux was calculated by combining air temperature and the remotely sensed surface temperature for the estimation of ET using Thornthwaite method. We applied and evaluated the ET successfully in the Al-Ain area of United Arab Emirates.In the Second method, vegetation index derived using remote sensing data was used for the determination of surface resistance for latent heat. Landsat 8 OLI were used to derive NDVI using ArcGIS and ENVI software. To derive the predicted ET using NDVI, regression analyses were conducted between data derived from satellites, published field meteorological stations data and ET values. From the collected variables of interest, we have also studied the bivariant density estimation curves. It is evident from the patterns of multimodal data that the data belong to different locations with different ET status. It was also observed that wind velocity (U) seems to be decreasing with increasing ET and rest all variable were increasing with increasing ET, which depend on the saturation vapour pressure (SVP). From this approach, we confirmed that the prediction of ET is achievable from the remotely determined data of the derived variables. It is also confirmed that the predicted ET results gained from the Normalized Difference Vegetation Index (NDVI) regression functions were comparable to the ET values obtained by the previously publish field data. The results showed that indirect application of remotely sensed vegetation index could be used for the ET determination.
    Author(s): Mitch Maguire, Univ. of Nebraska-Lincoln, Daugherty Water For Food Global Institute (United States); Christopher M. U. Neale, Wayne Woldt, Univ. of Nebraska-Lincoln (United States)
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    Estimating actual crop evapotranspiration (ETc) is a critical component in tracking soil water availability and managing near real-time irrigation scheduling. Energy and water balance models are two common approaches for estimating daily crop ETc. The Spatial EvapoTranspiration Modeling Interface (SETMI) hybrid model combines these two approaches and has been used to increase the accuracy of modeled ETc and soil water content by assimilating actual ET values to update the soil water balance. In this study, modeled daily ETc from the two-source energy balance (TSEB), root zone water balance, and the hybrid modeling approach were compared to measured ETc from eddy covariance flux tower systems to quantify model accuracy. The TSEB model used the Priestly-Taylor approximation for estimating ETc and the water balance model was updated with reflectance-based crop coefficients. The models were informed with UAS acquired multispectral reflectance and thermal infrared imagery collected over irrigated and rainfed maize and soybean fields during the 2018-2020 growing seasons.
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    The purpose of the study was the development of GIS models implementing two empirical methods of reference evapotranspiration (ETo), namely FAO-56 Penman-Monteith (FAO-56 PM) and Hansen, and two empirical methods of actual evapotranspiration (ETa), namely Turc and Corrected Turc (CT). GIS software (ArcMap 10.6) model builder environment was used for model-implementation, though the models could be easily transferred in relative software types (e.g. QGIS, ERDAS). The affordances of the presented models are flexibility and applicability to any study area (using the corresponding raster images of remote-derived or interpolated data as inputs), cell-by-cell calculations (estimates for the continuum of space) and dual representation of the empirical formulae; as optical models and as python scripts.Models’ application has been made on Peloponnese, Greece, a complex Land Use Land Cover (LULC) Mediterranean area, engaging MODIS LST Terra day and night inputs which have been acquired from USGS Earth Explorer and NASA EARTH DATA platforms. MODIS LST day and night products have been proved satisfactory proxies of maximum and minimum values (respectively) of local near surface air temperature (Tair). Alternatively, considering overpassing local time for each study-area Aqua or any other satellite products could be used as inputs. Furthermore, 8-day composites of MODIS net evapotranspiration (ET) (MOD16A2V6) have been acquired and compared to model outputs for different time scales and ET types. Corrected Turc and Hansen formulae exhibited estimates satisfactorily close to MODIS ET for different time scales. Overall, the proposed models have been proved time-saving and useful tools for water management applications that can be utilized by inexperienced to advanced users.
    Vegetation Monitoring and Mapping
    Livestream: 15 September 2021 • 14:50 - 16:00 CEST
    Session Chair: Alessandra Capolupo, Politecnico di Bari (Italy)
    In addition to the pre-recorded on-demand presentations available for the presentations listing below, this conference session will also hold a live-stream broadcast of its presentations.
    Times listed are Central European Summer Time, CEST (UTC+2:00 hours)

    14:50 hrs 11856-23: Multisensor data acquisition for assessing the condition of vegetation

    15:00 hrs 11856-24: Satellite imagery and climate variables suggest variations in the phenology of olive groves in southern Spain

    15:10 hrs 11856-25: Use of satellite remote sensing and climate data to predict the potential habitat distribution of Prosopis cineraria in the UAE

    15:20 hrs 11856-26: Assessment of spatial dynamics of riparian vegetation condition in relation to water quality: a case of the Keiskamma River, South Africa

    15:30 hrs 11856-27: Global assessment of FY3D MERSI/NDVI product

    15:40 hrs 11856-28: Exploring the importance of using earth observation to support Burn Severity Assessment of Cyprus’s forest fires events and the influence of forest canopy density

    15:50 hrs 11856-29: Recurrence techniques for the analysis of vegetation indices and climate anomalies: a study case in semiarid grasslands

    For timing of sessions 1-4 & 6-11 see the respective session listings.
    Author(s): Simon Stemmler, Dominic Wiedenmann, Fraunhofer-Institut für Physikalische Messtechnik IPM (Germany)
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    The pressure to perform on agricultural land is becoming ever greater. Higher crop yields and climate changes taking place at the same time are presenting agriculture with enormous challenges. Drought stress, extreme weather events and the cultivation of monocultures stress both, the soil and the crops themselves. Regular monitoring of plant condition as well as soil condition is essential for a sustainable land use. The use of UAVs for aerial structural surveys, the recording of soil parameters such as soil temperature, soil moisture and gas exchange have so far mostly been carried out independently of each other. Combining these measurement techniques, a holistic picture of the state of these ecosystems becomes possible. The Fraunhofer Institute for Physical Measurement Techniques presents here a coherent process chain for the fully comprehensive recording of agricultural ecosystems. A recording by means of LiDAR systems from the air, multispectral aerial images, terrestrial laser scans and the recording of soil temperature, soil moisture and nitrous oxide emission by Tunable Diode Laser Absorption Spectroscopy (TDLAS) are combined with each other. Using GNSS surveyed ground control points, the data from the different sources are linked and homogenized. Thus, we obtain a full structural image of the ecosystem enriched with metadata on plant condition and soil parameters. This forms the basis of an analysis of the overall condition of the full ecosystem, individual vegetation individuals and the contribution to climate change. We present the results of the different sensors and the fused data of a measurement campaign.
    Author(s): Ángel Fernández Carrillo, Freddy W. Rivas-Gonzalez, Beatriz Revilla-Romero, GMV Aerospace & Defence S.A.U. (Spain)
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    Olive grove is the main culture in Spain, with high socioeconomic impacts. Global warming is leading to changes in agrarian systems, affecting the typical phenology and productivity. Remote Sensing data is a valuable source of information to study the current trends in olive groves, given the spectral, spatial and temporal resolution of sensors. In this context, the MED-GOLD project, funded by the European Union's H2020 programme (No. 776467), aims to develop climate services integrating Remote Sensing for olive, grape, and durum wheat crops. In this work, satellite images from the MODIS sensor have been used to study the status of olive groves in 2000-2020 in Southern Spain. Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI) and Normalized Multi-band Drought Index (NMDI) were computed. The correlation of these indices with precipitation and temperature was studied using datasets provided by the Environmental Information Network of Andalusia (REDIAM) and scenarios based on the MED-GOLD outcomes. Additionally, the following NDVI annual indicators were derived to study trends: maximum NDVI (MAX), minimum (MIN), mean (MEAN), relative range of NDVI (RREL), date of the maximum (DMAX) and date of the minimum (DMIN). The correlation between climate and vegetation indices was complex. Good and bad production years were reflected on vegetation indices. Clear trends were observed in annual NDVI indicators. The average photosynthetic activity increased, especially in the minimum, MIN (Δyear = 0.0025; r2 = 0.68). The trend of RREL (Δyear = -0.012; r2 = 0.52) indicated that vegetation is moving to a more constant seasonal behaviour. The beginning and end of the season tend to occur earlier each year, as showed by DMAX (Δyear = -2.7 days) and DMIN (Δyear = -1,7 years). Management practices may require adaptations to the new intensity and seasonal behaviour of herbaceous vegetation, which should affect soil properties.
    Author(s): Remya Kottarathu Kalarikkal, Youngwook Kim, Taoufik Ksiksi, United Arab Emirates Univ. (United Arab Emirates)
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    Predicting species’ suitable habitats is critical to biodiversity conservation planning and implementation. Species habitat distribution is closely linked to environmental and bioclimatic variables which are widely used for estimating habitat suitability (HS) from species distribution models (SDMs). Integration of environmental parameters derived from satellite remote sensing, bioclimatic variables, and edaphic properties has created an advanced way to improve the SDM performance. The objective of this study is to assess the performance for predicting the potential HS of the arid plant species using Maximum Entropy (MaxENT) species distribution model based on an ecological niche machine-learning algorithm.
    Author(s): Andrés Almeida-Ñauñay, Rosa M. Benito, Miguel Quemada, Juan C. Losada, Ana M. Tarquis, Univ. Politécnica de Madrid (Spain)
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    Semiarid grasslands are particularly sensitive to drought and so they are one of the most threatened ecosystems by climate change. In this work, we proposed to assess the performance of two different vegetation indices (VIs) in two zones of a semiarid area. We calculated the standardised VIs and climate anomalies, and then we used correlations methods to evaluate the relationships between them. Finally, we attempted to identified and characterised the VIs and climate anomalies dynamics. To achieve this goal, recurrence techniques such as recurrence plots (RPs), cross recurrence plots (CRPs) and recurrence quantification analysis (RQA) were computed in this work. We selected a study area in the centre of Spain characterised by a Mediterranean climate. This work evaluated two VIs, the Normalized Difference Vegetation Index (NDVI) and the Modified Soil-Adjusted Vegetation Index (MSAVI). We have applied cross-correlations methods to detect the driving factor of VIs anomalies, focusing on the effect of temperature and precipitation. Vegetation cover status could be inferred through VIs. However, natural systems possess complex dynamics, including non-linear relationships inherent to them. RPs were proposed as a methodology to reveal the periodic or chaotical behaviour of a system. CRPs are a bivariate extension of recurrence plots, and they are computed to analyse the relationships of two variables of the same system. Both could be mathematically measured by the RQA allowing to obtain quantitative measures of complexity. We have found that RPs visualising different VIs anomalies patterns in each zone. Furthermore, CRPs revealed the VIs sensitivity to detect and differentiate local conditions. Overall, we have characterised and measured the dynamics of the VIs anomalies, and we have shown that recurrence techniques are a valuable tool to explore drought events in semiarid areas.
    Yield Retrieval and Water Productivity
    Livestream: 16 September 2021 • 15:00 - 15:20 CEST
    Session Chair: Christopher M. U. Neale, Univ. of Nebraska-Lincoln (United States)
    In addition to the pre-recorded on-demand presentations available for the presentations listing below, this conference session will also hold a live-stream broadcast of its presentations.
    Times listed are Central European Summer Time, CEST (UTC+2:00 hours)

    15:00 hrs 11856-32: Estimation of olive groves cover crops net primary productivity using remote sensing data

    15:10 hrs 11856-33: Probing of the multilayer structure of sunflower leaf

    For timing of sessions 1-7 & 9-11 see the respective session listings.
    Author(s): Ángel Blázquez-Carrasco, Pedro Gómez-Giráldez, Juan Castro, Sergio Colombo, Elisabet Carpintero, María Pat González-Dugo, Instituto de Investigación y Formación Agraria y Pesquera (Spain)
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    The use of cover crops in olive groves is a widespread conservation practice that offers multiple environmental benefits. The European Agricultural Policy has implemented several policy instruments to encourage its adoption, and new related schemes are expected. An affordable monitoring system for these schemes, based on robust and affordable data, is required. However, it is challenging and costly to determine the dynamic of these cover crops at a regional scale, given the large variability in ground cover and biomass production over the year under a variety of field management practices. In this context, the use of remote sensing data, easily available, open and provided for free, has great potential. This work represents the first attempt to estimate the biomass produced by olive grove cover crops using Sentinel-2 data. An adaptation of the Monteith efficiencies approach was applied, combining available solar radiation reaching the site with remote sensing derived measure of leaf area absorbing the sunlight. This approach was applied on a daily scale during the 2020/2021 growing season in olive groves distributed in Southern Spain. Climatological data were combined with cloud-free images of the Sentinel-2. The presence and abundance of olive tree canopies were taken into account. The influence of this tree layer over the spectral data was masked by subtracting it from the olive grove emitted radiation. Three types of olive groves crop cover types were considered: - Cover crop in strips - Non-tillage without strips (full coverage) - Conventional tillage Conventional tillage with bare soil was included to evaluate the spectral behaviour of the tree layer along the year. Biomass samples were taken as a validation dataset. The production was calculated using the Comparative Yield Method. The results have provided a first estimation of the cover crop biomass evolution along the year in the pilot farms.
    Author(s): Yannick Abautret, Myriam Zerrad, Institut Fresnel (France); Dominique Coquillat, Laboratoire Charles Coulomb (France); Gabriel Soriano, Institut Fresnel (France); Daphné Héran, INRAE (France); Claude Amra, Institut Fresnel (France)
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    New techniques for agriculture science are widely explored since several decades in order to improve production yield. Measurements of optical properties at different scales of the crop are investigated and exploited to assess different parameters of interest such as state of stress. For instance, nowadays, there exists acquisition systems embedded in drones, mobile machines and satellites that are able to collect huge amount of hyperspectral imaging data. Identification of optical signature extracted from these techniques can help agronomist with adapting irrigation or distinguishing different plant varieties. These techniques allow to improve greatly the agricultural management, however they do not provide information about the internal structure of the plant leaf and their interaction with electromagnetic fields. Knowing precisely the plant leaf structure can bring critical information that can lead to the development of new techniques for phenotyping and precocious stress detection. To do this it is necessary to probe the plant at the leaf scale using THz instead of optical frequencies because the scattering sensitive phenomenon for plants is more drastic at optical frequencies. To find out how the light interact with the leaf, in a deterministic way, we can model the vegetal tissue as a stack of different physical layers characterized by the thickness and the optical index. In this study, funded by ANR project OptiPAG, we use a well-known reverse engineering technique to retrieve leaf architecture from the reflection data. In time domain, a short Terahertz pulse illuminates a multilayer sample that reflects a part of the signal carrying information about the sample structure. Using a numerical fit in the frequency domain allows to identify each layer and deduce the respective optical index over the input frequency range. We use a few classical (inorganic) etalon samples and analyze the echoes to reveal their thicknesses under the assumption of negligible absorption. Then, we use reverse engineering technique to fit the data in the THz range by taking into account the absorption, making an excellent agreement with the previous results with more accuracy. The measured thickness of the samples correspond very well with the manufacturing specifications. And finally we use this technique with vegetal tissues (sunflower leaves), that poses a much more complex situation. Results emphasize a 8-layer stack including trichomes, cuticules, epidermis and mesophyll layers and for each layer we extract the thickness and the complex index. To our knowledge this is the first time that the leaf multilayer structure is extracted with accuracy using a non-contact techniques.
    Water Monitoring Applications
    Livestream: 16 September 2021 • 15:20 - 15:50 CEST
    Session Chair: Antonino Maltese, Univ. degli Studi di Palermo (Italy)
    In addition to the pre-recorded on-demand presentations available for the presentations listing below, this conference session will also hold a live-stream broadcast of its presentations.
    Times listed are Central European Summer Time, CEST (UTC+2:00 hours)

    15:20 hrs 11856-34: Evaluating the effects of distinct water saturation states on the light penetration depths of sand-textured soils

    15:30 hrs 11856-35: Improving water bodies detection from Sentinel-1 in South Africa using drainage and terrain data

    15:40 hrs 11856-36: Coupling physically-based modeling and deep learning for long-term global freshwater availability monitoring and prediction

    For timing of sessions 1-8 & 10-11 see the respective session listings.
    Author(s): Gladimir V. G. Baranoski, Mark Iwanchyshyn, Bradley Kimmel, Petri Varsa, Spencer Van Leeuwen, Univ. of Waterloo (Canada)
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    The high-fidelity estimation of the light penetration depths of dry and wet sand-textured soils is of considerable interest for applied remote sensing and geoscience research initiatives involving a wide range of landscapes, from arable fields and deserts to coastal habitats. These initiatives include the restoration of vegetation in arid regions and the mitigation of weed dissemination in agricultural areas covered by wind-transported layers of these soils. Similarly, the remote detection and analysis of hyperspectral signatures from subsurface targets located in sandy landscapes also requires a sound understanding about the light penetration properties of the covering particulate materials under dry and wet conditions. Despite their relevance, however, there is a lack of data on the light penetration depths of sand-textured soils, notably accounting for their sensitivity to distinct patterns of water presence, either in their pore space or forming films around their grains. In this work, we aim to make inroads, both qualitatively and quantitatively, toward the understanding of key aspects associated with these interconnected factors. In order to achieve this goal without being constrained by laboratory and logistics limitations, we performed an array of controlled in silico experiments to systematically evaluate the effects of distinct water saturation states on the light penetration depths of representative samples of sand-textured soils. Our investigation is centered at the 400-1000 nm spectral domain, relevant for studies involving the mineralogy and morphology of natural sands, and it is based on a first-principles simulation framework supported by actual measured data. By advancing the current knowledge in this area, it is expected to contribute to the development of new technologies aimed at the cost-effective monitoring and management of sandy landscapes, and at the acquisition of more precise data on fundamental biophysical phenomena (e.g., seed germination) with a direct impact on crop yield and the recovery of ecosystems.
    Author(s): Ines Cherif, Georgios Ovakoglou, Thomas K. Alexandridis, Aristotle Univ. of Thessaloniki (Greece); Mahlatse Kganyago, Nosiseko Mashiyi, South African National Space Agency (South Africa)
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    In areas with extensive, nomadic, or transhumant livestock farming, it is important to access regular information on the location of ephemeral surface water bodies. Existing near-real time methods for high-resolution surface water mapping are mainly based on the use of optical satellite imagery. However, the use of optical data restricts the water detection to cloud-free conditions. To overcome this limitation SAR data are used for water bodies mapping. Nevertheless, the implemented techniques are usually not fully automated or are not applicable in hilly landscapes. Indeed, surface roughness, hill shadows, and presence of vegetation are known to affect the backscatter and lead to false alarms. In this study, a SAR-based method was used to map surface water from a set of Sentinel-1 images using the Otsu Valley Emphasis method to automatically detect a threshold for water in the histogram of backscatter. In order to reduce the false alarm rate in the steep areas, five different water masks using terrain and drainage information with different thresholds are compared in the mountainous province of KwaZulu-Natal (KZN) in South-Africa. The quantitative assessment shows that the overall accuracy ranged between 0.865 and 0.958 with the highest value obtained with the HAND (Height Above the Nearest Drainage)-based mask with a threshold of 10m. This mask also minimized the false detection of water with the lowest specificity of 0.037. The visual inspection over two reservoirs (Midmar Dam and Wagendrift Dam) shows that there is high agreement between the produced map and the reference data despite differences in their spatial and temporal coverage. Besides, radiometrically terrain corrected SAR data, which could be advantageous in such landscapes were recently made available by the ASF vertex platform. Even though they are not available in NRT, the potential of using such data for water detection is investigated.
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    The Gravity Recovery and Climate Experiment (GRACE) satellite and its successor GRACE Follow-On provide unprecedented tracking of terrestrial total water storage (TWS) dynamics at global, regional, and basin scales. Recent global estimates of TWS trends suggest increasing water storage in high and low latitudes (wetting), with decreased storage in mid-latitudes (drying) during the GRACE observational period, with their drivers categorized as natural interannual variability, unsustainable groundwater consumption, climate change, or combinations thereof. All indicate potential changes in future access to freshwater, with implications for the sustainability of water for human consumption, irrigation, and food security, and industrial uses. Global models, including global land surface models (LSMs) and global hydrological and water resource models (GHWRMs), offer an alternative way for tracking TWS dynamics (past, current, and future), but the utility of existing models is hampered by conceptual and/or data uncertainties related to various underrepresented and unrepresented processes, such as the lack of surface water and groundwater storage components in most land surface models (LSMs). In this study, a hybrid approach that combines the strengths of physically-based modeling and deep learning is proposed for predicting global TWS anomalies (TWSA). Specifically, we develop a spatiotemporal attention-based deep learning model (STAU-Net), integrating the U-Net architecture with ConvLSTM layer and convolutional block attention module (CBAM) to learn the spatiotemporal patterns of TWSA observed by GRACE, driven under different predictor combinations. Once trained and validated, the model can be used to predict long-term global TWS dynamics without requiring GRACE TWSA as inputs. The evaluation results suggest the hybrid approach is capable of providing improved predictions of global TWSA. Whether the TWSA inconsistencies influenced by different factors can be learned by the hybrid modeling is further discussed. This study demonstrates the unique ability of the hybrid approach in global freshwater availability monitoring and prediction.
    UAV and Airborne Sensing
    Livestream: 16 September 2021 • 16:00 - 17:00 CEST
    Session Chair: Christopher M. U. Neale, Univ. of Nebraska-Lincoln (United States)
    In addition to the pre-recorded on-demand presentations available for the presentations listing below, this conference session will also hold a live-stream broadcast of its presentations.
    Times listed are Central European Summer Time, CEST (UTC+2:00 hours)

    16:00 hrs 11856-54: Estimation of leaf area index at the late growth stage of crops using unmanned aerial vehicle hyperspectral images

    16:10 hrs 11856-55: UAV-based scoring for iron chlorosis in soybean

    Break: 16:20 to 16:40

    16:40 hrs 11856-39: Application of UAV and spectrometric survey results to determine agrochemical parameters of zonal soils used in agriculture (East of European Russia)

    16:50 hrs 11856-41: Using radiative transfer models for mapping soil moisture content under grassland with UAS-borne hyperspectral data

    For timing of sessions 1-9 & 11 see the respective session listings.
    Author(s): Weiping Kong, Wenjiang Huang, Lingling Ma, Binbin Chen, Chuanrong Li, Lingli Tang, Aerospace Information Research Institute CAS (China)
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    Author(s): Chaitanyam Potnuru, Corteva Agrscience (India); Balaji Narayanan, Landon Ries, Brice Floyd, Neil Hausmann, Corteva Agrscience (United States)
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    Iron chlorosis in soybean is a nutrient deficiency condition with general symptoms of chlorosis (yellowing) of soybean foliage and stunting of the plant, in turn impacting crop yield. Identifying, selecting and advancing varieties offering resistance to iron chlorosis is a critical component of soybean breeding. Genetic characterization of various soybean varieties is carried out using phenotypic measurements that are collected manually. Such measurements are extremely subjective confounded with rater variability, compromising measurement quality. Furthermore, manual data collection is labor intensive and expensive. In this study, we propose an automatic scoring system employing an analytical framework that applies image processing and machine learning (ML) techniques on red-green-blue (RGB) color channel images collected via Unmanned Aerial Vehicle (UAV) for quantifying iron chlorosis severity. Results from the machine learning model indicate that the ML-based scores yielded good correlation with the manual scores. Additionally, ML scores demonstrated higher heritability/repeatability compared to those obtained from the manual scores, suggesting the use of UAV imagery in conjunction with machine learning approaches for field assessments of iron chlorosis, reducing long and tedious manual data collection efforts. Moreover, such approaches provide a scalable and high-throughput scoring system, enabling efficient breeding practices.
    Author(s): Sergey V. Vasyukov, Federal Service for State Registration, Cadastre and Cartography (Russian Federation); Vyacheslav V. Sirotkin, Bulat M. Usmanov, Kazan Federal Univ. (Russian Federation); Sergey A. Toguzov, Moscow Polytechnic Univ. (Russian Federation); Iakimovich Dmitriy, Leisan G. Akhmetzyanova, Kazan Federal Univ. (Russian Federation)
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    To determine the degree of degradation of agricultural lands for a number of key values (humus content, mobile potassium, mobile phosphorus, PH), the use of multispectral UAV materials synchronized with ground-based spectrometric imagery is proposed. Spectroradiometer HandHeld 2, soil acidity (pH) meter, satellite GLONASS-GPS receiver of geodetic class were used for field survey. Multispectral orthophoto obtained at the time of ground surveys using multispectral cameras Tetracam Micro-MCA 4 and Tetracam ADC-micro installed on board of the Supercam-S350F UAV. In parallel with the spectrometric work, samples of soils of different soil varieties and washout degree were taken, in representative sites of elementary soil areas. Laboratory studies were carried out with the selected samples, in order to determine the main agrochemical parameters: humus (%), mobile phosphorus (mg), mobile potassium (mg), pH (H2O). The work was tested on two field sites located in the Chuvash Republic (Russia), on cultivated (arable land) forest-steppe zonal soils (leached chernozems, dark gray forest soils). As a result of mathematical data processing, statistically significant relationships were obtained between certain groups of agrochemical indicators and spectral data in different channels of UAV images for specific soil varieties. In the course of the study, relationships were found between the green, ndvi, nir, red channels obtained using the Supercam-S350F unmanned aerial vehicle and laboratory data: humus, phosphorus, potassium and soil pH. In general, the results of the experiment prove the fundamental possibility of using multispectral UAV materials, together with ground spectrometric imagery for automated express determination of agrochemical indicators of agricultural lands. Such information with a short update interval (1-2 times a year) allows reaching a qualitatively new level of agricultural land study and changing key approaches to land management. These methodological developments are in demand by state and municipal land management bodies, large agricultural holdings, and supervisory authorities.
    Author(s): Veronika Döpper, Alby Duarte Rocha, Tobias . Gränzig, Birgit Kleinschmit, Michael Förster, Technische Univ. Berlin (Germany)
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    Soil moisture content (SMC) is a key parameter of environmental processes. Remote sensing provides effective methods for mapping SMC at different spatial resolutions. Using UAS-borne hyperspectral and TIR observations enables a SMC retrieval at sub-meter scales. Canopy reflectance models (CRM) such as ProSAIL or SCOPE include a SMC specific input variable and are thus a potential tool to derive SMC and avoiding extensive reference SMC measurements. The inverse application of CRM supplies information on SMC and plant traits. SCOPE and ProSAIL involve SMC data of the root zone and at the surface, respectively. The combined use of both models offers the possibility to derive SMC at two vertical depths. Moreover, SMC relevant vegetation proxies such as leaf water content can be retrieved and alternatively used as indicator for SMC. Such plant traits are highest correlated to SMC at depths of major water uptake. However, their response can have a significant time-lag. We analyze the derivation of SMC at the soil surface and at the root zone using the SMC parameters within existing CRM. As a first step, we investigate on the sensitivity of ProSAIL and SCOPE to their soil moisture parameters. We apply these findings on UAS-borne hyperspectral and TIR imagery acquired over a pre-alpine TERENO grassland area. The site is equipped with a SoilNet that measures SMC at different depths. Using this data, we assess the vertical extent of both soil moisture content parameters. By inverse modelling of the vegetation parameters and the use of the temporally continuous SoilNet data at root zone level, we analyze the time-lag between changes in SMC and the corresponding vegetation response to optimize the retrieval of SMC. The project is part of the DFG-funded research group Cosmic Sense, which aims to provide interdisciplinary new representative insights into hydrological changes at the land surface.
    Climate, Drought, and Soil Water Content
    Livestream: 16 September 2021 • 17:00 - 17:40 CEST
    Session Chair: Antonino Maltese, Univ. degli Studi di Palermo (Italy)
    In addition to the pre-recorded on-demand presentations available for the presentations listing below, this conference session will also hold a live-stream broadcast of its presentations.
    Times listed are Central European Summer Time, CEST (UTC+2:00 hours)

    17:00 hrs 11856-42: Analysis of agronomic drought context based on satellite remote sensing over Western Mediterranean region

    17:10 hrs 11856-43: ALOS-2 and Sentinel-1 use for retrieving soil moisture over cereal fields in semi-arid area: the Kairouan plain – central Tunisia

    17:20 hrs 11856-44: Root-zone soil moisture from process-based and remote sensing features in ANN

    17:30 hrs 11856-45: Detecting changes in vegetation and climate that serve as early warning signal on land degradation using remote sensing: a review

    For timing of sessions 1-10 see the respective session listings.
    Author(s): Mehrez Zribi, Ctr. d'Etudes Spatiales de la Biosphère, CNRS (France); Simon Nativel, Michel Le Page, Ctr. d'Etudes Spatiales de la Biosphère (France)
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    In semiarid areas, drought is a very frequent phenomenon that generates very serious problems. Within this framework, several studies have been developed over the last twenty years based on time series of remote sensing satellite data. This study aims to analyze agronomic drought in a highly anthropogenic, semiarid region, the western Mediterranean region. The proposed study is based on Moderate-Resolution Imaging Spectroradiometer (MODIS) and Advanced SCATterometer (ASCAT) satellite data describing the dynamics of vegetation cover and soil water content through the Normalized Difference Vegetation Index (NDVI) and soil water index (SWI). Two drought indices were analyzed: the vegetation anomaly index (VAI) and the moisture anomaly index (MAI). The dynamics of the VAI were analyzed as a function of land cover deduced from the Copernicus land cover map. The effect of land cover and anthropogenic agricultural activities such as irrigation on the estimation of the drought index VAI was analyzed. The VAI dynamics were very similar for the shrub and forest classes. The contribution of vegetation cover (VAI) was combined with the effect of soil water content (MAI) through a new drought index called the global drought index (GDI) to conduct a global analysis of drought conditions. This type of combination has been proposed by different studies. However, the weightings of the two indices are generally based on simple addition without discussing different weights. Droughts are generally marked by a decrease in NDVI and SWI products. In this context, weights could be calculated according to the mean level of vegetation or moisture in month i. This means that a higher weight should be given to the vegetation anomaly when the vegetation is well developed. The implementation of this combination on different test areas in the study region is discussed.
    Author(s): Emna Ayari, Ctr. d'Etudes Spatiales de la Biosphère, CNRS (France), Institut National Agronomique de Tunis (Tunisia), Univ. de Carthage (Tunisia); Zeineb Kassouk, Zohra Lili Chabaane, Institut National Agronomique de Tunis (Tunisia), Univ. de Carthage (Tunisia); Nicolas Baghdadi, CIRAD, INRAE, CNRS (France), Territoires, Environnement, Télédétection et Information Spatiale (France), Univ. de Montpellier (France); Safa Bousbih, Ctr. d'Etudes Spatiales de la Biosphère, CNRS (France), Institut National Agronomique de Tunis (Tunisia), Univ. de Carthage (Tunisia); Mehrez Zribi, Ctr. d'Etudes Spatiales de la Biosphère, CNRS (France)
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    Soil moisture plays a primordial role in water resources management especially in irrigation needs scheduling. In the present study, we evaluate the potential of multi-incidence L-band Advanced Land Observing Satellite-2 (ALOS-2) data and C-band Sentinel-1 data to retrieve soil moisture. Therefore, in-situ measurements were acquired during satellite acquisitions over cereal fields in semi-arid area : The Kairouan plain in central Tunisia. Analysing radar data, L-band multi-incidence data (28°, 32.5° and 36°) in HH (L-HH) and HV (L-HV) polarization and C-band like-polarization signal data (C-VV) are strongly impacted by soil roughness. SAR sensitivity to soil moisture were analyzed according to three normalized difference vegetation indices (NDVI) classes : low vegetation cover (NDVI≤0.3), medium cover (0.3< NDVI ≤ 0.6), and dense cover (NDVI>0.6). Results highlight the sensitivity of L-band data to soil moisture in dense cover class. High correlations characterized the relationship between ALOS-2 data and vegetation parameters (Leaf Area Index (LAI), Height of vegetation cover (H) and Vegetation Water Content (VWC)). To link radar data to in-situ measurements, dual-frequency radar signals were simulated for bare and covered soils. For bare soils, two empirical models dependent on roughness parameters and soil moisture, the semi-empirical modified Dubois model (Dubois-B) and the modified IEM model (IEM-B), were tested. Empirical models and IEM-B provide the best accuracy to reproduce radar signal. For cereal fields, two options of Water Cloud Model (WCM) were used ( with and without the integration of soil-vegetation interaction component). Each option of WCM was coupled to the best performance bare soil models : empirical models for the entire database and IEM-B for like-polarized data (L-HH and C-VV). To retrieve soil moisture, the two options of WCM are inverted. Results underline the important contribution of soil-vegetation interaction component to estimate soil moisture with L-HV data compared to a neglected impact on C-band data inversion accuracy.
    Author(s): Roiya Souissi, Mehrez Zribi, Ahmad Al Bitar, Ctr. d'Etudes Spatiales de la Biosphère, Univ. de Toulouse, CNRS (France), Ctr. National d'Études Spatiales (France), Institut National de la Recherche Agronomique (France)
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    Quantification of Root-Zone Soil Moisture (RZSM) is crucial for agricultural applications. It impacts processes like vegetation transpiration and water percolation. The surface soil moisture (SSM) can be assessed through active and passive microwave remote sensing, but no current sensor enables direct retrieval of RZSM. Spatial maps of RZSM can be retrieved via proxy observations (vegetation stress, water storage change, surface soil moisture) or from land surface model predictions. Recently, more interest has risen in the use of data-driven methods to predict RZSM. In this study, we investigated the use of physical-process based features in the context of Artificial Neural Networks (ANN). We integrated the infiltration process information into an ANN model through the use of the recursive exponential filter. We also used a remote sensing-based evaporative efficiency as an input feature. It is important to note that these two processes depend on surface soil moisture which can be assessed through remote sensing. The impact of the use of geophysical variables was also assessed through the use of surface soil temperature and Normalized Difference Vegetation Index (NDVI). At each step of the study, the ANN models were trained using either only in-situ surface soil moisture data provided by the International Soil Moisture Network (ISMN) or an additional geophysical or process- based feature. The results show that the use of more features in addition to SSM information improves the prediction accuracy in specific cases when compared to an ANN model that predicts RZSM based on only SSM. The ability of the developed models to predict RZSM over larger areas will be assessed in the future.
    Author(s): Filippos Eliades, Diofantos Hadjimitsis, Chris Danezis, Cyprus Univ. of Technology (Cyprus)
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    Desertification and land degradation have severe negative effects on land-use, water resources, soil stability, agriculture and biodiversity. Especially, drylands cover 33.8% of northern Mediterranean countries: approximately 69% of Spain and 66% of Cyprus. The European Environment Agency (EEA) indicated that 8% of the territory of the European Union (mostly in Bulgaria, Cyprus, Greece, Italy, Romania, Spain and Portugal) experience a ‘very high’ or ‘high sensitivity’ to desertification. For Cyprus Island, 9.68% of the land area was found to be susceptible to land degradation. Vegetation phenology in considered an important indicator in inter-annual vegetation changes in terrestrial ecosystems and on climate-vegetation interactions. Changes in vegetation phenology have been closely linked to the variability of climate patterns and may have an important impact on the ecological processes of ecosystems. Satellite remote sensing data have been widely used for vegetation phenology monitoring over large geographic domains using various types of observations and methods over the past several decades. The objective of this literature review is to provide a detailed synthesis of the main contributions of the global vegetation phenology research to the development of environmental knowledge based on land degradation/ desertification and EO-based science and technology, identifying the current fields of research and possible research gaps. We selected screened more than 1000 scientific papers from which we reviewed approximately 300 papers, identifying the objectives and remote sensing data used to characterize vegetation phenology. Overall, most of the studies have as a central research object direct human-induced land degradation or the degradation of anthropogenic-modified landscapes, without having considered long-term un-altered natural vegetation, in order to assess the impact and the level of climate change. Hence, a detailed EO-based time-series monitoring and analysis of un-altered natural vegetation could provide indicators that may serve as early warning signals for the scale and level of climate change induced effects on vegetation and ecosystems that might lead to land degradation and even to desertification.
    Poster Session
    Author(s): Montserrat Ferrer-Julià, Univ. de León (Spain); Sergio Fernández-Casado, Losán (Spain); Eduardo Garcia-Melendez, Univ. de León (Spain)
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    While water resources decrease in some areas, irrigation enterprises need to anticipate farmer’s demands to minimize the evaporation amounts from irrigation pools, as well as storing enough water to accomplish crop’s water needs at any time. The main goals of the present research are (i) to classify the agricultural plots according to their land manager style related to irrigation and (ii) to locally characterize the Kc values for each crop’s growth period. The investigation has been focused on 164 corn fields of the northwest of the Iberian Peninsula (province of León, Spain). For this purpose, 25 Sentinel-2 images were analyzed. All of them correspond to the complete corn’s growing period (from April till September) in 2017. After performing their atmospheric correction, a NDVI image was calculated for each date. Next, the Kc values were estimated applying a direct equation from NDVI. Once there is a unique Kc curve per each of the 164 corn plots, the overall curves were used to perform a hierarchical clustering following an agglomerative method. Finally, a correlation between the resultant clusters and irrigation volumes was estimated. Results showed the Kc curves of the 164 corn fields. From them, it was possible to establish the calendar and length of the corn growth stages, as well as the characteristic Kc value for each stage. Besides, the clustering grouped the crop fields in 31 classes. Their comparison with the irrigated water volume used along the corn growth period allowed to identify different farmer’s management styles. Therefore, on one hand, the comparison of different corn’s Kc curves allowed to locally characterize their water needs in each growing stage. On the other hand, the clustering of Kc curves identified different farmer’s management approaches what should facilitate the water storage planning of the irrigation enterprises and minimize their water losses. Acknowledgements: Research financed by FEDER/Spanish Ministry of Science and Innovation – Agencia Estatal de Investigación/ Project ESP2017-89045-R
    Author(s): Pedro Gómez-Giraldez, María Dolores Carbonero, Elisabet Carpintero, María Pat González-Dugo, Instituto de Investigación y Formación Agraria y Pesquera (Spain)
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    The intensity of flowering of the holm oak trees is important both for the annual phenological monitoring and as a predictive index of final acorn production. Their male flowers hand in long catkins of intense yellow colour and the estimation of its abundance in the field is a time-consuming task that becomes unfeasible at large scale. In this work, a methodology was tested to estimate the intensity of flowering of 150 trees using an RGB (Red Green Blue) image provided by an unmanned aerial vehicle. Three aerial images were taken using a drone and an RGB camera (SODA model) obtaining zenith images of 3 cm of spatial resolution over two dehesa farms. At the same time, digital photographs were taken of each tree (50 trees per plot). Finally, the intensity of flowering was visually estimated in each photograph and ranged from 1 (little or no flowering) to 3 (high flowering). A flowering intensity index based on the closeness to pure yellow within a Cartesian RGB space was developed to check the relationship between the drone images and the visually analyzed photographs. This index was built by placing the pixel within the Cartesian space by its digital values of R, G and B and calculating the modulus of the vector that would go from that point to the pure yellow. The results showed that those trees with lower flowering intensity were grouped in higher yellow distances and the high flowering intensity trees in the lower ones. It can be concluded that this simple index, derived from an RGB zenith image was able to identify the qualitative flowering intensity of holm oaks at the farm level and can be useful for future phenological and productive applications.
    Author(s): Maretta Kazaryan, North Ossetian State Medical Academy (Russian Federation); Mikhail Shahramanian, Financial Univ. (Russian Federation); Evgeny A. Semenishchev, Moscow State Univ. of Technology "STANKIN" (Russian Federation)
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    Pollution of the Arctic territories with garbage dumps provides the general warming in the northern latitudes and cooling in the southern latitudes of the Earth. This article examines the state of the cryosphere of the studied territories and the impact on the constituent elements of solid domestic and industrial waste. The necessary information of medium, high spatial resolution for further study can be obtained using technologies for remote sensing of the Earth from spacecraft with hyperspectral measurements. We propose a method for detecting leachate elements in unauthorized dumpsites in the Arctic using space vehicles. This task is relevant for the implementation of geo-ecological monitoring of the Arctic territories covered with snow. An algorithm for finding the creation of leachate under the influence of solid household and industrial waste has been developed. The article examines the consequences of climate change on forming the biomedical component of the process under consideration. We present a comparison of the proposed processing algorithm on the space images of the Arctic and subarctic territories of the Russian Federation.
    Author(s): Daniela Avetisyan, Space Research and Technology Institute (Bulgaria); Galya Cvetanova, Agriculture Experimental Station – Lom, Agricultural Academy (Bulgaria)
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    Due to depletion of natural resources, climate change and their impact on the land-production systems, farmers are facing more and more challenges related to the practical application of the sustainable development paradigm. These problems result in rapid development of precision agriculture as a management strategy, taking advantage of state-of-the-art technologies. In precision agriculture, Variable Rate Application (VRA) technology is focused on the automated application of materials (such as fertilizers, herbicides, and irrigation water) to a given crop field. It involves different approaches, including sensor-based systems for monitoring and assessment of crop status and field environmental conditions. For operational success of VRA reliable data is needed to indicate the variety of processes taking place in the farm field. In the present research, we present spectral signature data for the status of winter wheat (Triticum aestivum L.) development in different growth stages. Spectral signatures vary depending on environmental conditions and related effects for the agroecosystems such as drought stress, crop diseases, and crop nutrient deficiencies. The generated spectral signature profiles are based on the Sentinel-2 satellite data, acquired in three consecutive growing seasons, distinguished with different ecological conditions. Spectral vegetation indices, indirectly representing the manifestation of biophysical processes and drought stress are calculated for each profile. Field climatic data is used for differentiation of the ecological conditions and validation of the results. The present research supports the creation of spectral library and can be used to create machine learning algorithms for monitoring of winter wheat status and application of variable rate technology.
    Author(s): Koichiro Yawata, Satoshi Yamaguchi, Tomonori Yamamoto, Hitachi, Ltd. (Japan)
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    We propose an estimation method of flood water distribution based on satellite borne Synthetic Aperture Radar (SAR) imagery and two-dimensional inundation simulation. Because existence of water on land surface is clearly depicted by SAR imagery, conventional method enables estimation of flooded area. In this research we focus on flood depth distribution, which correspond closely to human lives and damage of properties. In addition, because flood depth distribution is a crucial information to quickly respond flood disaster, the processing time of the estimation should be short. Proposed method consists of the following steps: Step1: we extract the flood area from SAR images. Step 2: we conduct two-dimensional inundation simulation iteratively. In the first iteration, parameters of river levee breach are randomly generated. We use simulation software called DioVISTA/Flood, which use a coupled model of one-dimensional river unsteady flow and two-dimensional inundation flow. The software contains digital elevation model of Japan with horizontal resolution of 5 m. The result is time series of water depth and flow speed in x- and y-directions. Step 3: we calculate similarity degree between the result and the estimated flood area generated in Step 1. Step 4: we update the parameters to find simulation result with higher similarity degree. The parameter is updated based on the past similarity degrees using Bayesian optimization technique. If difference of SAR based flood area and simulated one is significant, the program goes to Step 2. Finally, estimation result of flood depth distribution is generated based on the simulation result with the most consistent with the estimated flood area by SAR imagery. The performance was evaluated based on mean absolute error (MAE) between surveyed water height and predicted water height, taking the flood case on the Chikuma river in 2019 as an example. As a result, the MAE achieved less than 1.0m.
    Author(s): Sunhwa Kim, Jeong Eun, Underwater Survey Technology 21 (Korea, Republic of)
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    Agricultural and forestry satellite for agriculture and forestry monitoring are scheduled to be launched in the Republic of Korea in 2025. The Agricultural and Forestry Satellite CAS500(Compact Advanced Satellite 500)-4 is a multi-spectral satellite with a spatial resolution of 5 m and with a revisit cycle of 3 days. Prior to launch, this study intends to develop a NDVI composite technique to minimize the effect of clouds. A high-altitude Korean cabbage field (>67ha), which has a relatively large area as a single crop field in Korea, was selected as the study area. Sentinel-2A/B (10m spatial resolution, 5-day revisit cycle) acquired from May 2019 to July 2021 for the study area was used. For monthly compositing, the MaxNDVI technique, which is a representative composite technique, and the recently suggested score-based composite technique were applied and compared. The score-based method calculates the fitness score for compositing for each pixel by assigning various factors and weights to minimize the effect of clouds during NDVI composite and maximize temporal representativeness. Therefore, the reflectance of the pixel with the highest score is used for compositing. The reflectance composite image produced in this way is converted to NDVI. Although both composite techniques minimize the effect of clouds, both results show that MaxNDVI shows high NDVI at the end of the month at the time of early growth after sowing, whereas the score-based technique shows NDVI at the middle of the month. Compared to the MODIS composite data from 2019 to 2021, the monthly composite data of Sentinel-2 NDVI showed various growth patterns by site in more detail.
    Conference Chair
    Univ. of Nebraska Lincoln (United States)
    Conference Chair
    Univ. degli Studi di Palermo (Italy)
    Program Committee
    Ludwig-Maximilians-Univ. München (Germany)
    Program Committee
    Alessandra Capolupo
    Politecnico di Bari (Italy)
    Program Committee
    Instituto de Investigación y Formación Agraria y Pesquera (Spain)
    Program Committee
    Univ. degli Studi di Palermo (Italy)
    Program Committee
    Univ. of Nebraska Lincoln (United States)