Novel Satellite-Based Methodologies for Multi-Sensor and Multi-Scale Environmental Monitoring to Preserve Natural Capital

Global warming, as the biggest manifestation of climate change, has changed the distribution of water in the hydrological cycle by increasing the evapotranspiration rate resulting in anthropogenic and natural hazards adversely affecting modern and past human properties and heritage in different parts of the world. The comprehension of environmental issues is critical for ensuring our existence on Earth and environmental sustainability. Environmental modeling can be described as a simplified form of a real system that enhances our knowledge of how a system operates. Such models represent the functioning of various processes of the environment, such as processes related to the atmosphere, hydrology, land surface, and vegetation. The environmental models can be applied on a wide range of spatiotemporal scales (i.e. from local to global and from daily to decadal levels); and they can employ various types of models (e.g. process-driven, empirical or data-driven, deterministic, stochastic, etc.). Satellite remote sensing and Earth Observation techniques can be utilized as a powerful tool for flood mapping and monitoring. By increasing the number of satellites orbiting around the Earth, the spatial and temporal coverage of environmental phenomenon on the planet has in-creased. However, handling such a massive amount of data was a challenge for researchers in terms of data curation and pre-processing as well as required computational power. The advent of cloud computing platforms has eliminated such steps and created a great opportunity for rapid response to environmental crises. The purpose of this study was to gather state-of-the-art remote sensing and/or earth observation techniques and to further the knowledge concerned with any aspect of the use of remote sensing and/or big data in the field of geospatial analysis. In order to achieve the goals of this study, some of the water-related climate-change phenomena were studied via different mathematical, statistical, geomorphological and physical models using different satellite and in-situ data on different centralized and decentralized computational platforms. The structure of this study was divided into three chapters with their own materials, methodologies and results including: (1) flood monitoring; (2) soil water balance modeling; and (3) vegetation monitoring. The results of this part of the study can be summarize in: 1) presenting innovative procedures for fast and semi-automatic flood mapping and monitoring based on geomorphic methods, change detection techniques and remote sensing data; 2) modeling soil moisture and water balance components in the root zone layer using in-situ, drone and satellite data; incorporating downscaling techniques; 3) combining statistical methods with the remote sensing data for detecting inner anomalies in the vegetation covers such as pest emergence; 4) stablishing and disseminating the use of cloud computation platforms such as Google Earth Engine in order to eliminate the unnecessary steps for data curation and pre-processing as well as required computational power to handle the massive amount of RS data. As a conclusion, this study resulted in provision of useful information and methodologies for setting up strategies to mitigate damage and support the preservation of areas and landscape rich in cultural and natural heritage

A modified version of the SMAR model for estimating root-zone soil moisture from time-series of surface soil moisture

Root-zone soil moisture at the regional scale has always been a missing element of the hydrological cycle. Knowing its value could be a great help in estimating evapotranspiration, erosion, runoff, permeability, irrigation needs, etc. The recently developed Soil Moisture Analytical Relationship (SMAR) can relate the surface soil moisture to the moisture content of deeper layers using a physically-based formulation. Previous studies have proved the effectiveness of SMAR in estimating root-zone soil moisture, yet there is still room for improvement in its application. For example, the soil water loss function (i.e. deep percolation and evapotranspiration), assumed to be a linear function in the SMAR model, may produce approximations in the estimation of water losses in the second soil layer. This problem becomes more critical in soils with finer textures. In this regard, the soil moisture profile data from two research sites (AMMA and SCAN) were investigated. The results showed that after a rainfall event, soil water losses decrease following a power pattern until they reach a minimum steady state. This knowledge was used to modify SMAR. In particular, SMAR was modified (MSMAR) by introducing a non-linear soil water loss function that allowed for improved estimates of root zone soil moisture.Keywords: surface soil moisture, root-zone soil moisture, SMAR, soil water loss function, MSMA

Estimation of soil moisture from UAS platforms using RGB and thermal imaging sensors in arid and semi-arid regions

Soil moisture (SM) is a connective hydrological variable between the Earth’s surface and atmosphere and affects various climatological processes. Surface soil moisture (SSM) is a key component for addressing energy and water exchanges and can be estimated using different techniques, such as in situ and remote sensing (RS) measurements. Discrete, costly and prolonged, in situ measurements are rarely capable in demonstration of moisture fluctuations. On the other hand, current high spatial resolution satellite sensors lack the spectral resolution required for many quantitative RS applications, which is critical for heterogeneous covers. RS-based unmanned aerial systems (UASs) represent an option to fill the gap between these techniques, providing low-cost approaches to meet the critical requirements of spatial, spectral and temporal resolutions. In the present study, SM was estimated through a UAS equipped with a thermal imaging sensor. To this aim, in October 2018, two airborne campaigns during day and night were carried out with the thermal sensor for the estimation of the apparent thermal inertia (ATI) over an agricultural field in Iran. Simultaneously, SM measurements were obtained in 40 sample points in the different parts of the study area. Results showed a good correlation (R2=0.81) between the estimated and observed SM in the field. This study demonstrates the potential of UASs in providing high-resolution thermal imagery with the aim to monitor SM over bare and scarcely vegetated soils. A case study based in a wide agricultural field in Iran was considered, where SM monitoring is even more critical due to the arid and semi-arid climate, the lack of adequate SM measuring stations, and the poor quality of the available data

Estimation of the Root-Zone Soil Moisture Using Passive Microwave Remote Sensing and SMAR Model

Estimation of root-zone soil moisture (RZSM) at regional scales is a critical issue in surface hydrology that could be a great help for estimating evapotranspiration, erosion, runoff, and irrigation requirements, etc. A significant number of satellites [soil moisture and ocean salinity (SMOS), special sensor microwave imager (SSM/I), advanced microwave scanning radiometer-EOS (AMSR-E), tropical rain- fall measuring mission/microwave imager (TRMM/TMI), etc.] retrieve surface soil moisture (SSM) using passive microwave remote sensing. This information can be used to derive RZSM using a new mathematical filter. In particular, the recently developed soil moisture analytical relationship (SMAR) can relate the surface soil moisture to the moisture of deeper layer using a relationship derived from a soil water balance equation where infiltration is estimated based on the relative fluctuations of soil moisture in the surface soil layer. In the present paper, the SMAR model is tested on two research databases in Africa and North America [African monsoon multidisciplinary analysis (AMMA) and soil climate analysis network (SCAN), respectively], where field measurements at different depths are available. Furthermore, the TRMM/ TMI Satellite is selected to retrieve the satellite SSM data of the studied regions using the land parameter retrieval model (LPRM). Both remotely sensed SSM and field measurements are used within the SMAR model to explore their ability in reproducing the RZSM and also to explore the existing difference in model parameterization moving from one dataset to the other. The SMAR model is applied using three different schemes: (1) with parameters calibrated using surface field measurements, (2) with parameters calibrated using remotely sensed SSM as input, and finally (3) using the remotely sensed SSM with the same parameters calibrated in Scheme 1. In all cases, SMAR param- eters have been calibrated using a genetic algorithm optimizing the root-mean square error (RMSE) between SMAR prediction and measured RZSM. The results show that remotely sensed data may be coupled with the SMAR model to provide a good description of RZSM dynamics, but it requires a specific parameterization respect to Scheme 1. Nevertheless, it is surprising to observe that two of the four parameters of the model related to the soil texture are relatively stable moving from remote-sensed to field data

Soil Moisture Monitoring in Iran by Implementing Satellite Data into the Root-Zone SMAR Model

Monitoring Surface Soil Moisture (SSM) and Root Zone Soil Moisture (RZSM) dynamics at the regional scale is of fundamental importance to many hydrological and ecological studies. This need becomes even more critical in arid and semi-arid regions, where there are a lack of in situ observations. In this regard, satellite-based Soil Moisture (SM) data is promising due to the temporal resolution of acquisitions and the spatial coverage of observations. Satellite-based SM products are only able to estimate moisture from the soil top layer; however, linking SSM with RZSM would provide valuable information on land surface-atmosphere interactions. In the present study, satellite-based SSM data from Soil Moisture and Ocean Salinity (SMOS), Advanced Microwave Scanning Radiometer 2 (AMSR2), and Soil Moisture Active Passive (SMAP) are first compared with the few available SM in situ observations, and are then coupled with the Soil Moisture Analytical Relationship (SMAR) model to estimate RZSM in Iran. The comparison between in situ SM observations and satellite data showed that the SMAP satellite products provide more accurate description of SSM with an average correlation coefficient (R) of 0.55, root-mean-square error (RMSE) of 0.078 m3 m−3 and a Bias of 0.033 m3 m−3. Thereafter, the SMAP satellite products were coupled with SMAR model, providing a description of the RZSM with performances that are strongly influenced by the misalignment between point and pixel processes measured in the preliminary comparison of SSM data

Enhanced Estimation of Root Zone Soil Moisture at 1 km Resolution Using SMAR Model and MODIS-Based Downscaled AMSR2 Soil Moisture Data

Root zone soil moisture (RZSM) is an essential variable for weather and hydrological prediction models. Satellite-based microwave observations have been frequently utilized for the estimation of surface soil moisture (SSM) at various spatio-temporal resolutions. Moreover, previous studies have shown that satellite-based SSM products, coupled with the soil moisture analytical relationship (SMAR) can estimate RZSM variations. However, satellite-based SSM products are of low-resolution, rendering the application of the above-mentioned approach for local and pointwise applications problematic. This study initially attempted to estimate SSM at a finer resolution (1 km) using a downscaling technique based on a linear equation between AMSR2 SM data (25 km) with three MODIS parameters (NDVI, LST, and Albedo); then used the downscaled SSM in the SMAR model to monitor the RZSM for Rafsanjan Plain (RP), Iran. The performance of the proposed method was evaluated by measuring the soil moisture profile at ten stations in RP. The results of this study revealed that the downscaled AMSR2 SM data had a higher accuracy in relation to the ground-based SSM data in terms of MAE (↓0.021), RMSE (↓0.02), and R (↑0.199) metrics. Moreover, the SMAR model was run using three different SSM input data with different spatial resolution: (a) ground-based SSM, (b) conventional AMSR2, and (c) downscaled AMSR2 products. The results showed that while the SMAR model itself was capable of estimating RZSM from the variation of ground-based SSM data, its performance increased when using downscaled SSM data suggesting the potential benefits of proposed method in different hydrological applications

Discerning <i>Xylella fastidiosa</i>-Infected Olive Orchards in the Time Series of MODIS Terra Satellite Evapotranspiration Data by Using the Fisher–Shannon Analysis and the Multifractal Detrended Fluctuation Analysis

Xylella fastidiosa is a phytobacterium able to provoke severe diseases in many species. When it infects olive trees, it induces the olive quick decline syndrome that leads the tree to a rapid desiccation and then to the death. This phytobacterium has been recently detected in olive groves in southern Italy, representing an important threat to the olive growing of the area. In this paper, in order to identify patterns revealing the presence of Xylella fastidiosa, several hundreds pixels of MODIS satellite evapostranspiration covering infected and healthy olive groves in southern Italy were analyzed by means of the Fisher–Shannon method and the multifractal detrended fluctuation analysis. The analysis of the receiver operating characteric curve indicates that the two informational quantities (the Fisher information measure and the Shannon entropy) and the three multifractal parameters (the range of generalized Hurst exponents and the width and the maximum of the multifractal spectrum) are well suited to discriminate between infected and healthy sites, although the maximum of the multifractal spectrum performs better than the others. These results could suggest the use of both the methods as an operational tool for early detection of plant diseases

Soil moisture monitoring in Iran by implementing satellite data into the Root-Zone SMAR model

Monitoring Surface Soil Moisture (SSM) and Root Zone Soil Moisture (RZSM) dynamics at the regional scale is of fundamental importance to many hydrological and ecological studies. This need becomes even more critical in arid and semi-arid regions, where there are a lack of in situ observations. In this regard, satellite-based Soil Moisture (SM) data is promising due to the temporal resolution of acquisitions and the spatial coverage of observations. Satellite-based SM products are only able to estimate moisture from the soil top layer; however, linking SSM with RZSM would provide valuable information on land surface-atmosphere interactions. In the present study, satellite-based SSM data from Soil Moisture and Ocean Salinity (SMOS), Advanced Microwave Scanning Radiometer 2 (AMSR2), and Soil Moisture Active Passive (SMAP) are first compared with the few available SM in situ observations, and are then coupled with the Soil Moisture Analytical Relationship (SMAR) model to estimate RZSM in Iran. The comparison between in situ SM observations and satellite data showed that the SMAP satellite products provide more accurate description of SSM with an average correlation coefficient (R) of 0.55, root-mean-square error (RMSE) of 0.078 m3 m-3 and a Bias of 0.033 m3 m-3. Thereafter, the SMAP satellite products were coupled with SMAR model, providing a description of the RZSM with performances that are strongly influenced by the misalignment between point and pixel processes measured in the preliminary comparison of SSM data

Exploring Long-Term Anomalies in the Vegetation Cover of Peri-Urban Parks Using the Fisher-Shannon Method

The main goal of this study was to evaluate the potential of the Fisher-Shannon statistical method applied to the MODIS satellite time series to search for and explore any small multiyear trends and changes (herein also denoted as inner anomalies) in vegetation cover. For the purpose of our investigation, we focused on the vegetation cover of three peri-urban parks close to Rome and Naples (Italy). For each of these three areas, we analyzed the 2000–2020 time variation of four MODIS-based vegetation indices: evapotranspiration (ET), normalized difference vegetation index (NDVI), leaf area index (LAI), and enhanced vegetation index (EVI). These data sets are available in the Google Earth Engine (GEE) and were selected because they are related to the interactions between soil, water, atmosphere, and plants. To account for the great variability exhibited by the seasonal variations while identifying small multiyear trends and changes, we devised a procedure composed of two steps: (i) application of the Singular Spectrum Analysis (SSA) to each satellite-based time series to detect and remove the annual cycle including the seasonality and then (ii) analysis of the detrended signals using the Fisher-Shannon method, which combines the Shannon entropy and the Fisher Information Measure (FIM). Our results indicate that among all the three pilot test areas, Castel Volturno is characterized by the highest Shannon entropy and the lowest FIM that indicate a low level of order and organization of vegetation time series. This behaviour can be linked to the degradation phenomena induced by the parasite (Toumeyella parvicornis) that has affected dramatically the area in recent years. Our results were nicely confirmed by the comparison with in situ analyzed and independent data sets revealing the existence of subtle, small multiyear trends and changes in MODIS-based vegetation indices

Estimation of soil moisture from UAS platforms using RGB and thermal imaging sensors in arid and semi-arid regions

Soil moisture (SM) is a connective hydrological variable between the Earth's surface and atmosphere and affects various climatological processes. Surface soil moisture (SSM) is a key component for addressing energy and water exchanges and can be estimated using different techniques, such as in situ and remote sensing (RS) measurements. Discrete, costly and prolonged, in situ measurements are rarely capable in demonstration of moisture fluctuations. On the other hand, current high spatial resolution satellite sensors lack the spectral resolution required for many quantitative RS applications, which is critical for heterogeneous covers. RS-based unmanned aerial systems (UASs) represent an option to fill the gap between these techniques, providing low-cost approaches to meet the critical requirements of spatial, spectral and temporal resolutions. In the present study, SM was estimated through a UAS equipped with a thermal imaging sensor. To this aim, in October 2018, two airborne campaigns during day and night were carried out with the thermal sensor for the estimation of the apparent thermal inertia (ATI) over an agricultural field in Iran. Simultaneously, SM measurements were obtained in 40 sample points in the different parts of the study area. Results showed a good correlation (R2=0.81) between the estimated and observed SM in the field. This study demonstrates the potential of UASs in providing high-resolution thermal imagery with the aim to monitor SM over bare and scarcely vegetated soils. A case study based in a wide agricultural field in Iran was considered, where SM monitoring is even more critical due to the arid and semi-arid climate, the lack of adequate SM measuring stations, and the poor quality of the available data