This PDF file contains the front matter associated with SPIE Proceedings Volume 10421, including the Title Page, Copyright information, Table of Contents, and Conference Committee listing.

Flooding is the most common and costly natural disaster around the world, causing the loss of human life and billions in economic and insured losses each year. In 2016, pluvial and fluvial floods caused an estimated 5.69 billion USD in losses worldwide with the most severe events occurring in Germany, France, China, and the United States. While catastrophe modeling has begun to help bridge the knowledge gap about the risk of fluvial flooding, understanding the extent of a flood – pluvial and fluvial – in near real-time allows insurance companies around the world to quantify the loss of property that their clients face during a flooding event and proactively respond. To develop this real-time, global analysis of flooded areas and the associated losses, a new methodology utilizing optical multi-spectral imagery from DigitalGlobe (DGI) WorldView satellite suite is proposed for the extraction of pluvial and fluvial flood extents. This methodology involves identifying flooded areas visible to the sensor, filling in the gaps left by the built environment (i.e. buildings, trees) with a nearest neighbor calculation, and comparing the footprint against an Industry Exposure Database (IE) to calculate a loss estimate. Full-automation of the methodology allows production of flood extents and associated losses anywhere around the world as required. The methodology has been tested and proven effective for the 2016 flood in Louisiana, USA.

In order to map the inundation area, flooding events are investigated using unique RGB composite imagery based on the MODIS surface reflectance data obtained from the Terra satellite, which is used to visualize and analyze these events. This study proposes using an RGB combination of MODIS band 6 (1.64 μm), band 5 (1.24 μm), and band 2 (0.86 μm) data from the visible and the near-infrared spectral ranges to map flood events. The flooding events that were investigated in this study occurred on October 25, 2015 along the Pampanga River in the Philippines. This estimate was indirectly compared with the results obtained from SENTINEL-1A Synthetic Aperture Radar (SAR) data. In addition, RGB imagery results using MODIS 6-5-2 bands were supported by the refractive index retrieval along the inundation area, and the derived technique is applied to the data from Himawari-8 satellite. This study shows that the RGB composite techniques using advanced sensors with more bands and higher spatio-temporal resolutions and supported by the refractive index retrieval method, are useful for estimating flood events.

As the tenth-largest river basin in the world and one of the largest in the Russian Federation, the Amur River basin’s water resources have changed greatly in the last decades. More comprehensive understanding of hydrological process in the Amur River basin based on hydrological model is needed. With the increased availability of remotely sensed information, some hydrological variables assessed through remote measurements can be used to complement discharge data and a different respect of hydrological observations into the modelling process. In this paper, the calibration and validation of a semi-distributed hydrological model in the Amur River basin using remote sensing data were presented. The long-term hydrological processes of the Amur River basin for 2000-2013 was simulated based on Soil and Water Assessment Tool (SWAT) and the changes of the hydrological variables were analyzed. The total water storage change (TWSC) derived from the Gravity Recovery And Climate Experiment (GRACE), the actual evapotranspiration (ET) calculated using Moderate Resolution Imaging Spectroradiometer (MODIS) and advanced very high resolution radiometer (AVHRR) data, and multi-site river discharge data were used in the model calibration and validation. This study showed that the streamflow, evapotranspiration, surface runoff, soil water content and groundwater discharge into reach had all changed to varying degrees in Amur River basin during the period 2000-2013 under the influence of climate changes and human activities. Remotely sensed information was demonstrated useful in successful application of the model calibration and validation, and especially in reducing the equifinality for different parameters.

Multi-model approach for remote sensing data processing and interpretation is described. The problem of satellite data utilization in multi-modeling approach for socio-ecological risks assessment is formally defined. Observation, measurement and modeling data utilization method in the framework of multi-model approach is described. Methodology and models of risk assessment in framework of decision support approach are defined and described. Method of water quality assessment using satellite observation data is described. Method is based on analysis of spectral reflectance of aquifers. Spectral signatures of freshwater bodies and offshores are analyzed. Correlations between spectral reflectance, pollutions and selected water quality parameters are analyzed and quantified. Data of MODIS, MISR, AIRS and Landsat sensors received in 2002-2014 have been utilized verified by in-field spectrometry and lab measurements. Fuzzy logic based approach for decision support in field of water quality degradation risk is discussed. Decision on water quality category is making based on fuzzy algorithm using limited set of uncertain parameters. Data from satellite observations, field measurements and modeling is utilizing in the framework of the approach proposed. It is shown that this algorithm allows estimate water quality degradation rate and pollution risks. Problems of construction of spatial and temporal distribution of calculated parameters, as well as a problem of data regularization are discussed. Using proposed approach, maps of surface water pollution risk from point and diffuse sources are calculated and discussed.

The Variable Infiltration Capacity (VIC) hydrologic model was adopted for investigating spatial and temporal variability of hydrologic impacts of climate change over the Nenjiang River Basin (NRB) based on a set of gridded forcing dataset at 1/12th degree resolution from 1970 to 2013. Basin-scale changes in the input forcing data and the simulated hydrological variables of the NRB, as well as station-scale changes in discharges for three major hydrometric stations were examined, which suggested that the model was performed fairly satisfactory in reproducing the observed discharges, meanwhile, the snow cover and evapotranspiration in temporal and spatial patterns were simulated reasonably corresponded to the remotely sensed ones. Wetland maps produced by multi-sources satellite images covering the entire basin between 1978 and 2008 were also utilized for investigating the responses and feedbacks of hydrological regimes on wetland dynamics. Results revealed that significant decreasing trends appeared in annual, spring and autumn streamflow demonstrated strong affection of precipitation and temperature changes over the study watershed, and the effects of climate change on the runoff reduction varied in the sub-basin area over different time scales. The proportion of evapotranspiration to precipitation characterized several severe fluctuations in droughts and floods took place in the region, which implied the enhanced sensitiveness and vulnerability of hydrologic regimes to changing environment of the region. Furthermore, it was found that the different types of wetlands undergone quite unique variation features with the varied hydro-meteorological conditions over the region, such as precipitation, evapotranspiration and soil moisture. This study provided effective scientific basis for water resource managers to develop effective eco-environment management plans and strategies that address the consequences of climate changes.

The aim of this study was to retrieve empirical formulas for water quality of three coastal lakes using remote sensing data - HySpex airborne imaging spectrometer and Sentinel-2A data. The Lebsko Lake, the Gardno Lake and the Great Dolgie Lake are salt-water lakes located in Slowinski National Park in Poland on the Baltic Sea coast. They all are shallow and turbid reservoirs prone to cyanobacteria blooms and eutrophication. Hyperspectral remote sensing data were acquired by the HySpex airborne sensor (in the range of 400-2500 nm) on 03.08.2015, multispectral data was acquired on the same date by MSI imager from Sentinel-2A satellite (in the range of 443-2190 nm). The ground measurements campaign was conducted 2-4.08.2015. The ground measurements consisted of two parts. First part included spectral reflectance sampling with spectroradiometer ASD FieldSpec 3, which covered the wavelength range of 350-2500 nm at ̴5 nm intervals. In situ data were collected both for water and for specific objects within the area. Second part of the campaign included water parameters in 48 points such as Secchi disc depth (SDD), electric conductivity (EC), pH, temperature, water chemistry (F, Br, Cl, NO2 NO3, PO4, SO4, Li, Na, NH4, Mn, Ca, N, DOC) and, phytoplankton groups. The empirical formulas for our water parameters were retrieved based on the reflecatance data from different remote sensed data sources from the time of field campaign. This study compared utility of HySpex and Sentinel-2A data sources - their means and limitations in terms of water quality modelling.

The real time monitoring of storms is important for the management and prevention of flood risks. However, in the southeast of Spain, it seems that the density of the rain gauge network may not be sufficient to adequately characterize the rainfall spatial distribution or the high rainfall intensities that are reached during storms. Satellite precipitation products such as PERSIANN-CCS (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks - Cloud Classification System) could be used to complement the automatic rain gauge networks and so help solve this problem. However, the PERSIANN-CCS product has only recently become available, so its operational validity for areas such as south-eastern Spain is not yet known. In this work, a methodology for the hourly validation of PERSIANN-CCS is presented. We used the rain gauge stations of the SIAM (Sistema de Información Agraria de Murcia) network to study three storms with a very high return period. These storms hit the east and southeast of the Iberian Peninsula and resulted in the loss of human life, major damage to agricultural crops and a strong impact on many different types of infrastructure. The study area is the province of Murcia (Region of Murcia), located in the southeast of the Iberian Peninsula, covering an area of more than 11,000 km2 and with a population of almost 1.5 million. In order to validate the PERSIANN-CCS product for these three storms, contrasts were made with the hyetographs registered by the automatic rain gauges, analyzing statistics such as bias, mean square difference and Pearson’s correlation coefficient. Although in some cases the temporal distribution of rainfall was well captured by PERSIANN-CCS, in several rain gauges high intensities were not properly represented. The differences were strongly correlated with the rain gauge precipitation, but not with satellite-obtained rainfall. The main conclusion concerns the need for specific local calibration for the study area if PERSIANN-CCS is to be used as an operational tool for the monitoring of extreme meteorological phenomena.

Drought is one of the most hazardous natural disasters for human beings and the environment. Using only rain gauge is insufficient to monitor the drought pattern effectively as it impacts large areas. This situation is more critical on small island countries, with limited rain gauges for monitoring drought pattern over the ocean regions. This study aims to assess the capability of Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B43 product in monitoring drought in Singapore from 1998 to 2014. The Standardized Precipitation Index (SPI) at various time-scales is used for identifying drought patterns. Results show moderate to good correlations between TMPA- 3B43 and rain gauges in the SPI estimations. Besides that, TMPA-3B43 exhibits a similar temporal drought behavior as the rain gauges. These findings indicate the TMPA 3B43 product as a very useful tool to study drought pattern over Singapore.

Climate variability has become a matter worth our attention as this issue has unveiled to the extreme water-related disasters such as flood and drought. Increments in heavy precipitation have happened over the past century and future climate scenarios show that it may alter the recurrence, timing, force, and length of these occasions. Satellite precipitation products (SPPs) could be used as representation of precipitation over a large region. This could be useful for the monitoring of the precipitation pattern as well as extreme events. Nevertheless, application of these products in monitoring extreme precipitation is still limited because insufficiency of quality assessment. This study aims to evaluate the performance of the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) 3B42V7 product in capturing the behavior of extreme precipitation events over Peninsular Malaysia from 2000 to 2015. Four extreme precipitation indices, in two general categories of absolute threshold (R10mm, R20mm and R50mm) and maximum (Rx1d) indices that recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI) were used. General evaluation has shown that the TRMM 3B42V7 product performed good on the measurements of monthly and annual precipitation. In the respect of extreme precipitation measurements, weak to moderate positive correlations were found between the TRMM 3B42 product and rain gauges over Peninsular Malaysia. The TRMM 3B42V7 product overestimated the R10mm and R20mm indices, while an underestimation was found for the R50mm and Rx1d indices.

Evapotranspiration (ET) is the flux of water between the surface (vegetation, soil and water bodies) and the atmosphere. Monitoring this water loss may be of crucial importance for applications in hydrology, agriculture, water use efficiency studies and drought monitoring. We introduce one of the few satellite-based operational evapotranspiration products, generated continuously and in near real-time over Europe, Africa and part of South America. The ET products (30 minutes and daily) are generated at the EUMETSAT’s Satellite Application Facility on Land Surface Analysis (LSA-SAF) operations centre (http://landsaf.ipma.pt). Following our commitments to our user’s community, we are continuously looking for new ways to improve the product. To accomplish this, the feedback from users and potential users of the products is of great interest. In this contribution we present the ET products characteristics and recent improvements gained thanks to the inclusion in our ET algorithm of new variables derived from Earth observation by MSG SEVIRI. We show examples of the ET products and we highlight their potential in droughts detection and monitoring. Some examples of possible applications are presented to invite users and researchers to explore the possibilities offered by LSA-SAF evapotranspiration products.

In the northwestern side of São Paulo state, irrigated crops are replacing natural vegetation, bringing importance for the development and applications of tools to quantify the energy and water balances. Remote sensing together with geostatistical tools are suitable for these tasks, being the surface temperature (T0) one of the radiation balance modelling input parameters. However, due to the importance of high both spatial and temporal resolutions to capture the dynamics of water and vegetation conditions, when the thermal bands are absent in several high-resolution satellites, applications on water resources studies are limited. This paper aimed to test the Moving Average (MA) and the Nearest Point (NP) geostatistical interpolation methods for estimate T0 with and without the Landsat 8 (L8) thermal bands by using a net of agrometeorological stations. In the case of using the L8 satellite thermal radiances, the Plankꞌs low was applied to its bands 10 and 11. Without these bands, T₀ was retrieved as residue in the radiation balance. Up scaling the satellite overpass T0 to daily scale resulted in a root mean square error (RMSE) of only 1.72 and 1.74 K when compared with values resulted from the MA and NP applications with the residual method, respectively. However, the MA method seemed to be more suitable than the NP one, being concluded that the coupled use of high spatial resolution images without a thermal band and interpolated weather data throughout the MA method is suitable for large-scale energy and water balance studies.

Satellite imagery provides a dependable basis for computational models that aimed to determine actual evapotranspiration (ET) by surface energy balance. Satellite-based models enables quantifying ET over large areas for a wide range of applications, such as monitoring water distribution, managing irrigation and assessing irrigation systems’ performance. With the aim to evaluate the energy and water consumption of a large scale on-turn pressurized irrigation system in the district of Aguas Nuevas, Albacete, Spain, the satellite-based image-processing model SEBAL was used for calculating actual ET. The model has been applied to quantify instantaneous, daily, and seasonal actual ET over high- resolution Landsat images for the peak water demand season (May to September) and for the years 2006 – 2008. The model provided a direct estimation of the distribution of main energy fluxes, at the instant when the satellite overpassed over each field of the district. The image acquisition day Evapotranspiration (ET₂₄) was obtained from instantaneous values by assuming a constant evaporative fraction (Λ) for the entire day of acquisition; then, monthly and seasonal ET were estimated from the daily evapotranspiration (ET_daily) assuming that ET₂₄ varies in proportion to reference ET (ET_r) at the meteorological station, thus accounting for day to day variation in meteorological forcing. The comparison between the hydrants water consumption and the actual evapotranspiration, considering an irrigation efficiency of 85%, showed that a considerable amount of water and energy can be saved at district level.

Thermal infrared (T IR) imagery is normally acquired at coarser pixel resolution than that of shortwave sensors on the same satellite platform. Often, T IR resolution is not suitable for monitoring crop conditions of individual fields or the impacts of land cover changes that are at significantly finer spatial scales. Consequently, thermal sharpening techniques have been developed to sharpen T IR imagery to shortwave band pixel resolutions. One of the most classic thermal sharpening technique is T sHARP . It uses a relationship between land surface temperature and normalized vegetation index (N DV I). However, there are several studies that prove that a single relationship between T IR and N DV I may only exist for a limited class of landscape. Our work hypothesis stated that it is possible to improve the spatial resolution of T IR imagery considering a relationship between vegetation and several soil spectral indexes and T IR as well the spatial context information. In this work, the potential of Superpixels (SP ) combined with Regression Random Forest (RRF ) is used to augmenting the spatial resolution of the Landsat 8 T IR (Band 10 and 11) imagery to their visible (V IS) spatial resolution. The SP allows to consider the contextual information over the land cover, and RF allows to integrate in a unique model the relationship between five spectral indices and T IR data. The results obtained by SP-RRF approach shows the potential of this methodology, compared with classical T sHARP method.

Carbon dioxide (CO₂) measurement has an important role in atmosphere monitoring. Usually, two types of measurements are carried out. The first one is based on gas concentration measurement while the second involves gas exchange rate measurement between earth surface and atmosphere [1]. There are several methods which allow gas concentration measurement. However, most of them require expensive instrumentation or large devices (i.e. gas chambers). In order to precisely measure either CO₂ concentration or CO₂ exchange rate, preferably a sensors network should be used. These sensors must have small dimensions, low power consumption, and they should be cost-effective. Therefore, this creates a great demand for a robust low-power and low-cost CO₂ sensor [2,3]. As a solution, we propose a photonic sensor that can measure CO₂ concentration and also can be used to measure gas exchange by using the Eddy covariance method [1].

Evapotranspiration is so crucial for determining amount of the irrigation and the effective water management planning. Moreover, it is vital for determining agricultural drought management and determination the actual evapotranspiration ın a region is critical for early drought warning systems. The main object of this study was to assess accuracy of the remote sensing method (METRIC) by calibrating with the bowen ratio observations at the same time. The research was carried out in the west of Marmara Region, Turkey. Landsat 5 images was used to determine the metric algorithm. By using this algorithms are found. Landsat 5 images file were used to determine actual evapotranspiration and the image’s date was June 11 in 2010. This date was used for calibration with available terrestrial observation by using bowen ratio in that time. Landsat images obtained from the web site, earthexplorer.usgs.gov, and results of bowen ratio taken from micrometeorology station. As a result, energy balance parameters that are net radiation, soil heat flux and latent heat flux were compared both metric algorithm and the bowen ration in the images time. The results are found so close to each other.

Characterization of seagrass spectral reflectance response is important to understand seagrass condition and for the possibility of mapping activities using remote sensing data, which is important for the management, monitoring, and evaluation of seagrass ecosystem. This paper presents the spectral reflectance response of several tropical seagrass species. These species are Enhalus acoroides (Ea), Thalassia hemprichii (Th) and Cymodocea rotundata (Cr). Spectral reflectance response of healthy seagrass, epiphyte-covered seagrass, and damaged seagrass leaves for each species were measured using Jaz EL-350 field spectrometer ranged from 350 - 1100 nm. Repeated measurements were performed above water on harvested seagrass leaves. The results indicate that there is a change in spectral reflectance response of damaged or epiphyte-covered seagrass leaves compared to the healthy leaves. The results show similar pattern for the three species, where the peak reflectance in visible wavelengths shifted toward longer wavelengths on damaged seagrass leaves. The results of this research open up a possibility of mapping seagrass health condition using remote sensing image.

Drought is one of the most important and frequent natural hazards to agriculture production in North China Plain. To improve agriculture water management, accurate drought monitoring information is needed. This study proposed a method for comprehensive drought monitoring by combining a meteorological index and three satellite drought indices of TM data together. SPI (Standard Precipitation Index), the meteorological drought index, is used to measure precipitation deficiency. Three satellite drought indices (Temperature Vegetation Drought Index, Land Surface Water Index, Modified Perpendicular Drought Index) are used to evaluate agricultural drought risk by exploring data from various channels (VIS, NIR, SWIR, TIR). Considering disparities in data ranges of different drought indices, normalization is implemented before combination. First, SPI is normalized to 0 — 100 given that its normal range is -4 - +4. Then, the three satellite drought indices are normalized to 0 - 100 according to the maximum and minimum values in the image, and aggregated using weighted average method (the result is denoted as ADI, Aggregated drought index). Finally, weighed geometric mean of SPI and ADI are calculated (the result is denoted as DIcombined). A case study in North China plain using three TM images acquired during April-May 2007 show that the method proposed in this study is effective. In spatial domain, DI_combined demonstrates dramatically more details than SPI; in temporal domain, DI_combined shows more reasonable drought development trajectory than satellite indices that are derived from independent TM images.

Peatlands cover ~3% of the globe and are key ecosystems for climate regulation. To better understand the potential effects of climate change in peatlands, a major challenge is to determine the complex relationship between hydrology, microtopography, vegetation patterns, and gas exchange. Here we study the spectral and spatial relationship of microtopographic features (e.g. hollows and hummocks) and near-surface water through narrow-band spectral indices derived from hyperspectral imagery. We used a very high resolution digital elevation model (2.5 cm horizontal, 2.2 cm vertical resolution) derived from an UAV based Structure from Motion photogrammetry to map hollows and hummocks in the peatland area. We also created a 2 cm spatial resolution orthophoto mosaic to enhance the visual identification of these hollows and hummocks. Furthermore, we collected SWIR airborne hyperspectral (880-2450 nm) imagery at 1 m pixel resolution over four time periods, from April to June 2016 (phenological gradient: vegetation greening). Our results revealed an increase in the water indices values (NDWI1640 and NDWI2130) and a decrease in the moisture stress index (MSI) between April and June. In addition, for the same period the NDWI2130 shows a bimodal distribution indicating potential to quantitatively assess moisture differences between mosses and vascular plants. Our results, using the digital surface model to extract NDWI2130 values, showed significant differences between hollows and hummocks for each time period, with higher moisture values for hollows (i.e. moss dominated). However, for June, the water index for hummocks approximated the values found in hollows. Our study shows the advantages of using fine spatial and spectral scales to detect temporal trends in near surface water in a peatland.

Given the importance of penetration of light in the soil for seed germination, soil warming, and the photolytic degradation of pesticides, directional transmission of thin sand samples are studied in this paper under both dry and saturated conditions. The detector views upward through a glass-bottom sample holder, filled to 3 or 4 mm with a coarse, translucent, quartz sand sample. Transmission through the samples was measured as the illumination zenith angle moved from 0 to 70° in 5° intervals. In the most cases, transmission decreased monotonically, but slowly with increasing illumination angle at all wavelengths. A peak in transmission only appeared at 0° illumination for the low bulk density, dry sample at 3 mm depth. The 0° peak disappeared when the sample was wetted, when the bulk density increased, or when the depth of the sample increased, which indicates that the radiation transmitting through a sand layer can be diffused thoroughly with a millimeters-thin sand layer. For the saturated samples, water influences light transmission in contrasting ways in shorter and longer wavelength. Transmission increased in the VNIR when saturated relative to dry, while transmission decreased sharply after 1300 nm, with spectral absorption features characteristic of water absorption. In VNIR region, water absorption is low and the low relative index of refraction enhanced transmission through sand sample. In contrast, water absorption became dominant at longer wavelengths region leading to the strongly reduced transmission.

Direct observations of transmission through a thin layer of quartz sand indicate that the transmitted radiation – from the visible through the shortwave infrared – is essentially diffuse after little more than one attenuation length. Except for an anomalously high transmission in a dry, 3-mm deep quartz sample when the detector was directly aligned with the light source, no complex forward scattering features were apparent. A simple model designed to describe the observations is explored for insight into the angular dependence and the spectral distribution of the transmitted radiation. The model suggests that the observed variation of the transmittance with illumination angle can be attributed to surface effects (including absorption), that much of the transmitted light has passed through the sand particles, and that a wavelengthdependence of the attenuation in the visible is consistent with scattering within the sand particles.

Generally, soil moisture plays an important role in water cycle, water resources and other diverse applications over land. Passive microwave remote sensors (e.g., ASCAT, AMSR-E, SMOS, and SMAP) have successfully used for estimating the amount of soil moisture irrespective of their low temporal and special resolutions. In this study, we present a TVDI (temperature-vegetation dryness index)-based soil moisture retrieval algorithm based on visible and infrared remote sensors. The TERRA/MODIS products such LST (MOD11A2) and NDVI (MOD13A2) data were used. Far-East Asia area including the Korean peninsula were investigated for the case study. In particular, we found the elevation dependence on the soil moisture retrieval. We developed a correction method for this elevation effect. The proposed TVDI-based soil moisture algorithm in visible and infrared bands were compared and validated with soil moisture contents estimated from GCOM-W1/AMSR-2 observations in microwave bands.

Deforestation in Amazon basin due, among other factors, to frequent wildfires demands continuous post-fire monitoring of soil and vegetation. Thus, the study posed two objectives: (1) evaluate the capacity of Visible – Near InfraRed – ShortWave InfraRed (VIS-NIR-SWIR) spectroscopy to estimate soil organic matter (SOM) in fire-affected soils, and (2) assess the feasibility of SOM mapping from satellite images. For this purpose, 30 soil samples (surface layer) were collected in 2016 in areas of grass and riparian vegetation of Campos Amazonicos National Park, Brazil, repeatedly affected by wildfires. Standard laboratory procedures were applied to determine SOM. Reflectance spectra of soils were obtained in controlled laboratory conditions using Fieldspec4 spectroradiometer (spectral range 350nm– 2500nm). Measured spectra were resampled to simulate reflectances for Landsat-8, Sentinel-2 and EnMap spectral bands, used as predictors in SOM models developed using Partial Least Squares regression and step-down variable selection algorithm (PLSR-SD). The best fit was achieved with models based on reflectances simulated for EnMap bands (R²=0.93; R²cv=0.82 and NMSE=0.07; NMSEcv=0.19). The model uses only 8 out of 244 predictors (bands) chosen by the step-down variable selection algorithm. The least reliable estimates (R²=0.55 and R²cv=0.40 and NMSE=0.43; NMSEcv=0.60) resulted from Landsat model, while Sentinel-2 model showed R²=0.68 and R²cv=0.63; NMSE=0.31 and NMSEcv=0.38. The results confirm high potential of VIS-NIR-SWIR spectroscopy for SOM estimation. Application of step-down produces sparser and better-fit models. Finally, SOM can be estimated with an acceptable accuracy (NMSE~0.35) from EnMap and Sentinel-2 data enabling mapping and analysis of impacts of repeated wildfires on soils in the study area.

Sustainable forest management requires consistent and simple approaches for characterizing forest changes through time and space at the landscape scale. Landsat satellite data, with its long archive and comprehensive spatial, temporal and spectral detail, could enable us to achieve this goal. This study develops a consistent approach for mapping both disturbance and recovery for forest dynamic estimation across large areas over a 30 year period (1988 to 2016) using Landsat time series data. We analyzed dynamic Eucalypt/ Sclerophyll public forests in south eastern Australia which have been impacted by a series of disturbances including fire and logging over the last 30 years. We first prepared annual satellite composites and fitted spectral time series trajectories on a per-pixel basis using the LandTrendr algorithm, from which we derived a range of spatial disturbance and recovery metrics. We then simultaneously modeled disturbance and consequent recovery levels using the Random Forest classifier. Using derived change information and a one-off forest cover dataset, we estimated change in forest extent throughout the time series. Disturbance and consequent recovery were simultaneously detected with an overall accuracy of 80.2%, while the model of change levels classification obtained an overall accuracy of 76.5%. Over the 30 year period, approximately 49.5% of the study area was disturbed, 92% of which has fully recovered. Forest extent was found to be quite dynamics throughout the time period and comprised between 80.2% to 88.3% of public forest estate.

Forest species composition is a fundamental indicator of forest study and management. However, describing forest species composition at large scales and of highly diverse populations remains an issue for which remote sensing can provide significant contribution, in particular, Airborne Laser Scanning (ALS) data. Riparian corridors are good examples of highly valuable ecosystems, with high species richness and large surface areas that can be time consuming and expensive to monitor with in situ measurements. Remote sensing could be useful to study them, but few studies have focused on monitoring riparian tree species using ALS data. This study aimed to determine which metrics derived from ALS data are best suited to identify and map riparian tree species. We acquired very high density leaf-on and leaf-off ALS data along the Sélune River (France). In addition, we inventoried eight main riparian deciduous tree species along the study site. After manual segmentation of the inventoried trees, we extracted 68 morphological and structural metrics from both leaf-on and leaf-off ALS point clouds. Some of these metrics were then selected using Sequential Forward Selection (SFS) algorithm. Support Vector Machine (SVM) classification results showed good accuracy with 7 metrics (0.77). Both leaf-on and leafoff metrics were kept as important metrics for distinguishing tree species. Results demonstrate the ability of 3D information derived from high density ALS data to identify riparian tree species using external and internal structural metrics. They also highlight the complementarity of leaf-on and leaf-off Lidar data for distinguishing riparian tree species.

Connection of similar landscape patches with ecological corridors supports habitat quality of these patches, increases urban ecological quality, and constitutes an important living and expansion area for wild life. Furthermore, habitat connectivity provided by urban green areas is supporting biodiversity in urban areas. In this study, possible ecological connections between landscape patches, which were achieved by using Expert classification technique and modeled with probabilistic connection index. Firstly, the reflection responses of plants to various bands are used as data in hypotheses. One of the important features of this method is being able to use more than one image at the same time in the formation of the hypothesis. For this reason, before starting the application of the Expert classification, the base images are prepared. In addition to the main image, the hypothesis conditions were also created for each class with the NDVI image which is commonly used in the vegetation researches. Besides, the results of the previously conducted supervised classification were taken into account. We applied this classification method by using the raster imagery with user-defined variables. Hereupon, to provide ecological connections of the tree cover which was achieved from the classification, we used Probabilistic Connection (PC) index. The probabilistic connection model which is used for landscape planning and conservation studies via detecting and prioritization critical areas for ecological connection characterizes the possibility of direct connection between habitats. As a result we obtained over % 90 total accuracy in accuracy assessment analysis. We provided ecological connections with PC index and we created inter-connected green spaces system. Thus, we offered and implicated green infrastructure system model takes place in the agenda of recent years.

Eucalyptus (Ec) and pasture (Pt) are expanding while natural vegetation (Nv) are losing space in the Paraíba Valley, eastern side of the São Paulo state, Southeast Brazil. For quantification of water and vegetation conditions, the MODIS product MOD13Q1 was used together with a net of weather stations and vegetation land masks during the year 2015. The SAFER algorithm was applied to retrieve the actual evapotranspiration (ET), which was combined with the Monteithꞌs radiation use efficiency (RUE) model to estimate the biomass production (BIO). Three moisture indices were applied, the climatic water balance ratio (WB_r), the ratio of precipitation (P) to ET, the water balance deficit (WB_d), the difference between P and ET, and the evapotranspiration ratio (ETr), the ratio of ET to the reference evapotranspiration (ET₀). On the one hand, the highest ET rates for the Ec ecosystem should be a negative aspect under water scarcity conditions; however, it presented the best water productivity. Although the Ec ecosystem presenting the lowest WB_r and WBd values, it had the highest ET_r, averaging 0.92, when comparing to those for Nv (0.88) and Pt (0.79). These results indicated that eucalyptus plants have greater ability of conserving soil moisture in their root zones, increasing WP, when comparing with Pt and Nv ecosystems. These water relationships are relevant issues under the land-use change conditions in the Paraiba Valley, confirming the suitability of using the MODIS products together with weather stations to study the ecosystem dynamics.

The paper aims at demonstrating the assumptions and achievements of the Pilot Utilization Plan Activities performed within the Project ASAP “Advanced Sustainable Agricultural Production”, co-financed by European Space Agency under the ARTES IAP Programme. Within the course of the project, the Pilot Utilization Plan (PilUP) activities are performed in order to develop the remote sensing based models, and further calibrate and validate them in order to achieve the accuracy, which meets the requirements of paying customers. The completion of the first PilUP resulted in development of the following models based of Landsat 8 and Sentinel 2 satellite data: model of homogenous polygons demarcation on the basis of comparison of electromagnetic scanning results and bare soil spectral reflectance, model of problematic areas indication and model for yield potential, delivered on the basis of NDVI map developed 1 month before harvest and the map of yield/collected yield derived from Users participating in PilUP. The second edition of the PilUP is being conducted between March 2017 until the end of 2017. This edition includes farmers and insurance companies. The following activities are planned: development of model for delimitation of loses due to unfavorable wintering of winter crops and validation of the model with in-situ data collected by the insurance companies in-field investigators, further enhancement of the model for homogenous polygons delimitation and primary indication of soil productivity and testing of the applicability and viability of map of problematic areas with the farmers.

Agricultural production is one of the most important Brazilian economic activities accounting for about 21,5% of total Gross Domestic Product. In this scenario, the use of satellite images for estimating biophysical parameters along the phenological development of agricultural crops allows the conclusion about the sanity of planting and helps the projection on design production trends. The objective of this study is to analyze the temporal patterns and variation of six vegetion indexes obtained from the bands of Sentinel 2A satellite, associated with greenness (NDVI and ClRE), senescence (mARI and PSRI) and water content (DSWI and NDWI) to estimate maize production. The temporal pattern of the indices was analyzed in function of productivity data collected in-situ. The results obtained evidenced the importance of the SWIR and Red Edge ranges with Pearson correlation values of the temporal mean for NDWI 0.88 and 0.76 for CLRE.

Rapid real-time monitoring of wheat nitrogen (N) status is crucial for precision N management during wheat growth. In this study, Multi Lookup Table (Multi-LUT) approach based on the N-PROSAIL model parameters setting at different growth stages was constructed to estimating canopy N density (CND) in winter wheat. The results showed that the estimated CND was in line with with measured CND, with the determination coefficient (R²) and the corresponding root mean square error (RMSE) values of 0.80 and 1.16 g m^-2, respectively. Time-consuming of one sample estimation was only 6 ms under the test machine with CPU configuration of Intel(R) Core(TM) i5-2430 @2.40GHz quad-core. These results confirmed the potential of using Multi-LUT approach for CND retrieval in winter wheat at different growth stages and under variables climatic conditions.

To convince new users of the advantages of the Sentinel_2 sensor, a simplification of classic remote sensing tools allows to create a platform of communication among domain specialists of agricultural analysis, visual image interpreters and remote sensing programmers. An index value, known in the remote sensing user domain as “Zabud” was selected to represent, in color, the essentials of a time series analysis. The color index used in a color atlas offers a working platform for an agricultural field control. This creates a database of test and training areas that enables rapid anomaly detection in the agricultural domain. The use cases and simplifications now function as an introduction to Sentinel_2 based remote sensing, in an area that before relies on VHR imagery and aerial data, to serve mainly the visual interpretation. The database extension with detected anomalies allows developers of open source software to design solutions for further agricultural control with remote sensing.

Farmers throughout the world are constantly searching for ways to maximize their returns. Remote Sensing applications are designed to provide farmers with timely crop monitoring and production information. Such information can be used to identify crop vigor problems. Vegetation indices (VIs) derived from satellite data have been widely used to assess variations in the physiological state and biophysical properties of vegetation. However, due to the various sensor characteristics, there are differences among VIs derived from multiple sensors for the same target. Therefore, multi-sensor VI capability and effectiveness are critical but complicated issues in the application of multi-sensor vegetation observations. Various factors such as the atmospheric conditions during acquisition, sensor and geometric characteristics, such as viewing angle, field of view, and sun elevation influence direct comparability of vegetation indicators among different sensors. In the present study, two experimental areas were used which are located near the villages Nea Lefki and Melia of Larissa Prefecture in Thessaly Plain area, containing a wheat and a cotton crop, respectively. Two satellite systems with different spatial resolution, WorldView-2 (W2) and Sentinel-2 (S2) with 2 and 10 meters pixel size, were used. Normalized Difference Vegetation Index (NDVI) and Leaf Area Index (LAI) were calculated and a statistical comparison of the VIs was made to designate their correlation and dependency. Finally, several other innovative indices were calculated and compared to evaluate their effectiveness in the detection of problematic plant growth areas.

Food security is one of the most important issue for Indonesia. The huge population number and high population growing rate has made the food security a critical issue. This paper describe the application of remote sensing data to (1) map agroecosystem zones in Bantul District, Special Region of Yogyakarta, Indonesia in 2012 and (2) analyze the food security in the study area based on the resulting agro-ecosystem map. Bantul District is selected as the pilot area because this area is among the highest food crop production area in the Province. ALOS AVNIR-2 image accquired on 15 June 2010 was integrated with Indonesian Surface map (RBI map), soil types map, and slope steepness map. Population statistics data was also used to calculate the food needs. Field survey was conducted to obtain the crop field productivity information on each agro-ecosystem zone and assess the accuracy of the model. This research indicates that (1) Bantul District can be divided into three agroecosystem zones, where each zone has unique topograhic configuration and soil types composition, and (2) Bantul Distict is categorized as food secure area since the rice production in 2012 managed to cover the food needs of the people with the surplus of 33,208.6 tonnes of rice. However, when the analysis was conducted at sub-district level, there are four subdistrict with food insecurity where the food needs surpass the rice production. These sub-district are Kasihan Sub-district (-5,598.4 t), Banguntapan Sub-district (-2,483.4 t), Pajangan Sub-district (-1,039.6 t) and Dlingo Sub-district (-798.7 t).

Leaf Water Content (LWC) is an essential constituent of plant leaves that determines vegetation heath and its productivity. An accurate and on-time measurement of water content is crucial for planning irrigation, forecasting drought and predicting woodland fire. The retrieval of LWC from Visible to Shortwave Infrared (VSWIR: 0.4-2.5 μm) has been extensively investigated but little has been done in the Mid and Thermal Infrared (MIR and TIR: 2.50 -14.0 μm), windows of electromagnetic spectrum. This study is mainly focused on retrieval of LWC from Mid and Thermal Infrared, using Genetic Algorithm integrated with Partial Least Square Regression (PLSR). Genetic Algorithm fused with PLSR selects spectral wavebands with high predictive performance i.e., yields high adjusted-R² and low RMSE. In our case, GA-PLSR selected eight variables (bands) and yielded highly accurate models with adjusted-R² of 0.93 and RMSEcv equal to 7.1 %. The study also demonstrated that MIR is more sensitive to the variation in LWC as compared to TIR. However, the combined use of MIR and TIR spectra enhances the predictive performance in retrieval of LWC. The integration of Genetic Algorithm and PLSR, not only increases the estimation precision by selecting the most sensitive spectral bands but also helps in identifying the important spectral regions for quantifying water stresses in vegetation. The findings of this study will allow the future space missions (like HyspIRI) to position wavebands at sensitive regions for characterizing vegetation stresses.

Accurate crop growth stage estimation is important in precision agriculture as it facilitates improved crop management, pest and disease mitigation and resource planning. Earth observation imagery, specifically Synthetic Aperture Radar (SAR) data, can provide field level growth estimates while covering regional scales. In this paper, RADARSAT-2 quad polarization and TerraSAR-X dual polarization SAR data and ground truth growth stage data are used to model the influence of canola growth stages on SAR imagery extracted parameters. The details of the growth stage modeling work are provided, including a) the development of a new crop growth stage indicator that is continuous and suitable as the state variable in the dynamic estimation procedure; b) a selection procedure for SAR polarimetric parameters that is sensitive to both linear and nonlinear dependency between variables; and c) procedures for compensation of SAR polarimetric parameters for different beam modes. The data was collected over three crop growth seasons in Manitoba, Canada, and the growth model provides the foundation of a novel dynamic filtering framework for real-time estimation of canola growth stages using the multi-sensor and multi-mode SAR data. A description of the dynamic filtering framework that uses particle filter as the estimator is also provided in this paper.

Land Use and Land Cover (LULC) information are significant to observe and evaluate environmental change. LULC classification applying remotely sensed data is a technique popularly employed on a global and local dimension particularly, in urban areas which have diverse land cover types. These are essential components of the urban terrain and ecosystem. In the present, object-based image analysis (OBIA) is becoming widely popular for land cover classification using the high-resolution image. COSMO-SkyMed SAR data was fused with THAICHOTE (namely, THEOS: Thailand Earth Observation Satellite) optical data for land cover classification using object-based. This paper indicates a comparison between object-based and pixel-based approaches in image fusion. The per-pixel method, support vector machines (SVM) was implemented to the fused image based on Principal Component Analysis (PCA). For the objectbased classification was applied to the fused images to separate land cover classes by using nearest neighbor (NN) classifier. Finally, the accuracy assessment was employed by comparing with the classification of land cover mapping generated from fused image dataset and THAICHOTE image. The object-based data fused COSMO-SkyMed with THAICHOTE images demonstrated the best classification accuracies, well over 85%. As the results, an object-based data fusion provides higher land cover classification accuracy than per-pixel data fusion.

The Paracatu River is the largest affluent of the São Francisco River, Brazil. The main water use in the Paracatu river basin is irrigation, which occupies an area of 37,150 ha. The objective in this study was to obtain water indicators at irrigated areas using the SAFER (Simple Algorithm For Evapotranspiration Retrieving) and the Penman-Monteith models with images of SPOT 6 satellite (without the thermal band). The parameters obtained are evapotranspiration (ET), albedo (α), biomass (BIO), surface temperature (Tsup) and water productivity (PA) in irrigated areas of Paracatu River Basin. We used 2 satellite images by the sensor SPOT6 (by Astrium Company) with a spatial resolution of 6 m (August 8, 2014 and August 23, 2015) and data from meteorological stations. In irrigated areas, the NDVI reached values higher than 0.76, due the response of vegetation to irrigation. The daily average albedo was 0.18 ± 0.01 and 0.02 ± 0.17 respectively. In the analysis of the surface temperature (Tsup), it can be observed that in the image of 2015, mean values higher than those observed in the image of 2014 (303.03 ± 1.97 K and 299.34 ± 3.47 K, respectively). In 2015, due to increased atmospheric evaporative demand, ET reached values higher than those seen in the scene in 2014. The average daily evapotranspiration rate in Paracatu for 2014 scene was of 0.81±1.49 mm, with a maximum value of 8.96 mm at the irrigated areas. In image of 2015 the average evapotranspiration (ET) values was 1.87±1.27 mm. The results obtained in this study may assist in the monitoring of irrigated agriculture to face a trend of scarcity of water resources and of increasing conflicts over water use as occurs in the Paracatu River Basin.

The matching between reservoirs’ water edge and digital elevation model’s (DEM) contour lines allowed determining the water level at the acquisition date of satellite images. A preliminary study was conducted on the Castello dam (Magazzolo Lake), between Alessandria della Rocca and Bivona (Agrigento, south-Italy). The accuracy assessment of the technique was than evaluated from the comparison between classified and reference objects using similarity metrics about the shape, theme, edge and position, through the plugin STEP of open source software GIS. Moreover, an independent GIS technique was implemented to evaluate the water level, based on a distances’ array between existing contour lines and nodes extracted from vectorised classification images. Results have shown the potentiality of the techniques when applied on an ideal case; advantages and disadvantages when the images are characterized by clear sky, and limits when images are acquired during not ideal atmospheric conditions.

The United Arab Emirates (UAE) has given great attention to the environment and sustainable development through applications of best practices of global standards that ensure optimal investment in natural resources. Since the UAE is located in an arid region which is known as dry, sandy and get a small amount of rainfall, thus the water resources are limited and accordingly, the government has initiated an integrated water resources management (IWRM) strategy to meet the increasing demands of water. Dams are considered as one of the important strategies that are suitable for this arid region. An event of rainfall if between heavy to severe in a short duration could cause flash floods and damages to population centers and areas of agriculture nearby. To prevent that from happening, several dams and barriers were built to protect human life and infrastructure. Besides contribution to enhance the water resources and use them optimally to irrigate the growing agricultural areas across the country. Geographically, most of the dams were located in the northern and eastern part of the UAE, around mountainous areas. This study aims to monitor the changes that occurred to five dams of the north-eastern region of the UAE during 2015 and 2016 through the use of remote sensing technology of optical images captured by "DubaiSat-2". The segmentation approach utilized in this study is based on a band ratio technique called Normalized Difference Water Index (NDWI). The experimental results revealed that the proposed approach is efficient in detecting dams from multispectral satellite images.

Monitoring total nitrogen content (TNC) in soil of cultivated land quantitively is significant for fertility adjustment, yield improvement and sustainable development of agriculture. Analyzing the hyperspectrum response on soil TNC is the basis of remote sensing monitoring in a wide range. The study aimed to develop a universal method to monitor total nitrogen content in soil of cultivated land by hyperspectrum data. The correlations between soil TNC and the hyperspectrum reflectivity and its mathematical transformations were analyzed. Then the feature bands and its transformations were screened to develop the optimizing model of monitoring soil TNC based on the method of multiple linear regression. Results showed that the bands with good correlation of soil TNC were concentrated in visible bands and near infrared bands. Differential transformation was helpful for reducing the noise interference to the diagnosis ability of the target spectrum. The determination coefficient of the first order differential of logarithmic reciprocal transformation was biggest (0.56), which was confirmed as the optimal inversion model for soil TNC. The determination coefficient (R²) of testing samples was 0.45, while the RMSE was 0.097 mg/kg. It indicated that the inversion model of soil TNC in the cultivated land with the one differentiation of logarithmic reciprocal transformation of hyperspectral data could reach high accuracy with good stability.

Monitoring soil organic matter (SOM) of cultivated land quantitively and mastering its spatial change are helpful for fertility adjustment and sustainable development of agriculture. The study aimed to analyze the response between SOM and reflectivity of hyperspectral image with different pixel size and develop the optimal model of estimating SOM with imaging spectral technology. The wavelet transform method was used to analyze the correlation between the hyperspectral reflectivity and SOM. Then the optimal pixel size and sensitive wavelet feature scale were screened to develop the inversion model of SOM. Result showed that wavelet transform of soil hyperspectrum was help to improve the correlation between the wavelet features and SOM. In the visible wavelength range, the susceptible wavelet features of SOM mainly concentrated 460 ~ 603 nm. As the wavelength increased, the wavelet scale corresponding correlation coefficient increased maximum and then gradually decreased. In the near infrared wavelength range, the susceptible wavelet features of SOM mainly concentrated 762 ~ 882 nm. As the wavelength increased, the wavelet scale gradually decreased. The study developed multivariate model of continuous wavelet transforms by the method of stepwise linear regression (SLR). The CWT-SLR models reached higher accuracies than those of univariate models. With the resampling scale increasing, the accuracies of CWT-SLR models gradually increased, while the determination coefficients (R²) fluctuated from 0.52 to 0.59. The R² of 5*5 scale reached highest (0.5954), while the RMSE reached lowest (2.41 g/kg). It indicated that multivariate model based on continuous wavelet transform had better ability for estimating SOM than univariate model.

Chestnuts have been part of the landscape and popular culture of the Canary Islands (Spain) since the sixteenth century. Many crops of this species are in state of abandonment and an updated mapping for its study and evaluation is needed. This work proposes the elaboration of this cartography using two satellite images of very high spatial resolution captured on two different dates and representing well-differentiated phenological states of the chestnut: a WorldView-2 image of March 10th, 2015 and a WorldView-3 image of May 12th, 2015 (without and with leaves respectively). Two study areas were selected within the municipality of La Orotava (Tenerife Island). One of the areas contains chestnut trees dispersed in an agricultural and semi-urban environment and in the other one, the specimens are grouped forming a forest merged with Canarian pines and other species of Monteverde. The Maximum Likelihood (ML), the Artificial Neural Networks (ANN) and the Spectral Angle Mapper (SAM) classification algorithms were applied to the multi-temporal image resulting from the combination of both dates. The results show the benefits of using the multi-temporal image for Pinolere with the ANN algorithm and for Chasna area with ML algorithm, in both cases providing an overall accuracy close to 95%.

Portugal is one of the most affected countries in Europe by forest fires. Every year in the summer, hundreds of hectares burn, destroying goods and forests at an alarming rate. The objective of this work was to analyze the forest areas burned in Portugal in 2016 (summer) using different satellite data with different spatial resolution (Sentinel-2A MSI and Landsat 8 OLI) in two affected areas. Data from spring from 2016 and 2017 were chosen (pre-fire event and post-fire event) in order to maximize the Normalized Difference Vegetation Index (NDVI) values. The QGIS software's plugin - Semi- Automatic Classification Plugin- which allowed to obtain NDVI values for the Landsat 8 OLI and Sentinel- 2A was used. The results showed that the NDVI decreased considerably in Arouca and Vila Nova de Cerveira after de fire event, meaning a marked drop in vegetation level. In Sintra municipality this change was not verified because non forest fire was registered in this area during the study period. The results from the Sentinel-2A and Landsat 8 OLI data analysis are in agreement, however the Sentinel-2A satellite gives results more accurate than Landsat-8 OLI since it has best spatial resolution. This study could help the experts to understand both the causes and consequences of spatial variability of post-fire effects. Other vegetation spectral indices related with fire and burnt areas could also be calculated in order to discriminate burnt areas. Added to the best spatial resolution of Sentinel-2A (10 m), the temporal resolution of Sentinel- 2A (10 days) was increased with the launch of the twin Sentinel–2B (very recently) and therefore the frequency of the combined constellation revisit will be 5 days. However, for historical studies, the Landsat program remains the best option.

Due to anthropogenic and climatic changes, Carpathian Mountains forests in Romania experience environmental degradation. As a result of global climate change, there is growing evidence that some of the most severe weather events could become more frequent in Romania over the next 50 to 100 years. In the case of Carpathian mountain forests, winter storms and heat waves are considered key climate risks, particularly in prealpine and alpine areas. Effects of climate extremes on forests can have both short-term and long-term implications for standing biomass, tree health and species composition. The preservation and enhancement of mountain forest vegetation cover in natural, semi-natural forestry ecosystems is an essential factor in sustaining environmental health and averting natural hazards. This paper aims to: (i) describe observed trends and scenarios for summer heat waves, windstorms and heavy precipitation, based on results from satellite time series NOAA AVHRR, MODIS Terra/Aqua and Landsat TM/ETM+/OLI NDVI and LAI data recorded during 2000-2016 period correlated with meteorological parameters, regional climate models, and other downscaling procedures, and (ii) discuss potential impacts of climate changes and extreme events on Carpathian mountain forest system in Romania. The response of forest land cover vegetation in Carpathian Mountains, Romania to climatic factors varies in different seasons of the years, the diverse vegetation feedbacks to climate changes being related to different vegetation characteristics and meteorological conditions. Based on integrated analysis of satellite and field data was concluded that forest ecosystem functions are responsible of the relationships between mountain specific vegetation and climate.

Mangroves are known as salt-tolerant evergreen forests, whereas its create land-ocean interface ecosystems. Besides, mangroves bring direct and indirect benefits to human activities and play a major role as significant habitat for sustaining biodiversity. However, mangrove ecosystem study based on the mangrove species are very crucial to get a better understanding of their characteristics and ways to separate among them. In this paper, discriminant functions obtained using statistical approach were used to generate the score range for six mangrove species (Rhizophora apiculata, Acrostichum aurem, Acrostichum speciosum, Acanthus ilicifolius, Ceriops tagal and Sonneratia ovata) in Matang Mangrove Forest Reserve (MMFR), Perak. With the computation of score range for each species, the fraction of the species can be determined using the proposed algorithm. The results indicate that by using 11 discriminant functions out of 16 are more effective to separate the mangrove species as the higher accuracy was obtained. Overall, the determination of leaf sample’s species is chosen base on the highest fraction measured among the six mangrove species. The obtained accuracy for mangrove species using statistical approach is low since it is impossible to successfully separate all the mangrove species in leaf level using their inherent reflectance properties. However, the obtained accuracy results are satisfactory and able to discriminate the examined mangrove species at species scale.

This work was performed in the Paraíba do Sul basin, within the limits of the São Paulo state, southeastern Brazil, in order to assess the dynamics of the land-use and land-cover changes at the Paraíba do Sul river's floodplains between 1985 and 2016. We focused on investigating the development of agricultural areas used for the production of wetland rice and of areas featuring artificial lakes produced by sand mining. We mapped the land cover in 1985 using images made by the Landsat 5 satellite's Moderate Resolution Imaging Spectroradiometer (MODIS) and Thematic Mapper (TM) sensors, which were segmented to produce vectors featuring homogeneous characteristics, and which were classified by means of visual interpretation. Similarly, we applied the maximum likelihood classification and used spectral curve inserts and adjustments to study and analyze the same area using a Landsat 8's Operational Land Imager (OLI) image made in 2016. Our results show significant reduction of areas used for rice crops, and increase in areas featuring sandmining pits. The rice crop areas decreased approximately 43% from 24,131.4 ha in 1985 to 13,789.8 ha in 2016. Over this 30-year period, the area covered by sand-mining lakes increased from 615 ha to 3,876 ha (+ 630%), and the number of lakes increased from 54 to 316. Sand mining and urbanization are the main factors causing the reduction in wetland rice areas. The absence of environmental management actions at the basin interferes with the rice production, which depends on the Paraíba do Sul river's floodplains.

Remote sensing techniques used in the precision agriculture and farming that apply imagery data obtained with sensors mounted on UAV platforms became more popular in the last few years due to the availability of low- cost UAV platforms and low- cost sensors. Data obtained from low altitudes with low- cost sensors can be characterised by high spatial and radiometric resolution but quite low spectral resolution, therefore the application of imagery data obtained with such technology is quite limited and can be used only for the basic land cover classification. To enrich the spectral resolution of imagery data acquired with low- cost sensors from low altitudes, the authors proposed the fusion of RGB data obtained with UAV platform with multispectral satellite imagery. The fusion is based on the pansharpening process, that aims to integrate the spatial details of the high-resolution panchromatic image with the spectral information of lower resolution multispectral or hyperspectral imagery to obtain multispectral or hyperspectral images with high spatial resolution. The key of pansharpening is to properly estimate the missing spatial details of multispectral images while preserving their spectral properties. In the research, the authors presented the fusion of RGB images (with high spatial resolution) obtained with sensors mounted on low- cost UAV platforms and multispectral satellite imagery with satellite sensors, i.e. Landsat 8 OLI. To perform the fusion of UAV data with satellite imagery, the simulation of the panchromatic bands from RGB data based on the spectral channels linear combination, was conducted. Next, for simulated bands and multispectral satellite images, the Gram-Schmidt pansharpening method was applied. As a result of the fusion, the authors obtained several multispectral images with very high spatial resolution and then analysed the spatial and spectral accuracies of processed images.

The growth rate monitoring of crops throughout their biological cycle is very important as it contributes to the achievement of a uniformly optimum production, a proper harvest planning, and reliable yield estimation. Fertilizer application often dramatically increases crop yields, but it is necessary to find out which is the ideal amount that has to be applied in the field. Remote sensing collects spatially dense information that may contribute to, or provide feedback about, fertilization management decisions. There is a potential goal to accurately predict the amount of fertilizer needed so as to attain an ideal crop yield without excessive use of fertilizers cause financial loss and negative environmental impacts. The comparison of the reflectance values at different wavelengths, utilizing suitable vegetation indices, is commonly used to determine plant vigor and growth. Unmanned Aerial Vehicles (UAVs) have several advantages; because they can be deployed quickly and repeatedly, they are flexible regarding flying height and timing of missions, and they can obtain very high-resolution imagery. In an experimental crop field in Eleftherio Larissa, Greece, different dose of pre-plant and in-season fertilization was applied in 27 plots. A total of 102 aerial photos in two flights were taken using an Unmanned Aerial Vehicle based on the scheduled fertilization. Α correlation of experimental fertilization with the change of vegetation indices values and with the increase of the vegetation cover rate during those days was made. The results of the analysis provide useful information regarding the vigor and crop growth rate performance of various doses of fertilization.

The largest inhabited island, Zhoushan Island, is the center of economy, culture, shipping, and fishing in the Zhoushan Archipelago New Area. Its coastal wetland and tidal flats offer significant ecological services including floodwater storage, wildlife habitat, and buffers against tidal surges. Yet, large-scale land reclamation and new land development may dramatically change ecosystem services. In this research, we assess changes in ecosystem service values in Zhoushan Island during 1990-2000-2011. Three LANDSAT TM and/or ETM data sets were used to determine the spatial pattern of land use, and previously published value coefficients were used to calculate the ecosystem service values delivered by each land category. The results show that total value of ecosystem services in Zhoushan Island declined by 11% from 2920.07 billion Yuan to 2609.77 billion Yuan per year between 1990 and 2011. This decrease is largely attributable to the 51% loss of tidal flats. The combined ecosystem service values of woodland, paddy land and tidal flats were over 90% of the total values. The result indicates that future land-use policy should pay attention to the conservation of these ecosystems over uncontrolled reclamation and coastal industrial development, and that further coastal reclamation should be on rigorous environmental impact analyses.

The estimation and quantification of vegetation parameters on field-scale is necessary to make statements about potential yield and the heterogeneity of its spatial distribution. The ESA satellite mission Sentinel-2 provides optical remote sensing data with a high temporal resolution allowing for an extensive monitoring of agricultural fields. In order to quantify the vegetation parameters as well as to calibrate and validate regression models, additional in-situ measurements are essential. Comprehensive field measurements in two study areas in Germany have been conducted in the growing season 2017 parallel to Sentinel-2 image acquisitions. All ground truth data form a dense time series of the vegetation parameters crop height, crop coverage, chlorophyll content, leaf area index, and wet and dry biomass. First results show a strong linear relation between dry and wet biomass, whereas the slope of the regression line changes with increasing phenological growth stage. Furthermore, there is a clear relationship between in-situ measured wet and dry biomass and NDVI in the early vegetation period, but a saturation occurs in later growth stages. The paper represents a status report of current work in progress, reports first results and gives an outlook of future work.

Remote sensing techniques owe their great popularity to the possibility to obtain of rapid, accurate and information over large areas with optimal time, spatial and spectral resolutions. The main areas of interest for remote sensing research had always been concerned with environmental studies, especially water bodies monitoring. Many methods that are using visible and near- an infrared band of the electromagnetic spectrum had been already developed to detect surface water reservoirs. Moreover, the usage of an image obtained in visible and infrared spectrum allows quality monitoring of water bodies. Nevertheless, retrieval of water boundaries and mapping surface water reservoirs with optical sensors is still quite demanding. Therefore, the microwave data could be the perfect complement to data obtained with passive optical sensors to detect and monitor aquatic environment especially surface water bodies. This research presents the methodology to detect water bodies with open- source satellite imagery acquired with both optical and microwave sensors. The SAR Sentinel- 1 and multispectral Sentinel- 2 imagery were used to detect and monitor chosen reservoirs in Poland. In the research Level, 1 Sentinel- 2 data and Level 1 SAR images were used. SAR data were mainly used for mapping water bodies. Next, the results of water boundaries extraction with Sentinel-1 data were compared to results obtained after application of modified spectral indices for Sentinel- 2 data. The multispectral optical data can be used in the future for the evaluation of the quality of the reservoirs. Preliminary results obtained in the research had shown, that the fusion of data obtained with optical and microwave sensors allow for the complex detection of water bodies and could be used in the future quality monitoring of water reservoirs.

TerraSAR-X images have been tested for agricultural fields of corn and wheat. The main purpose was to evaluate the impact of daily temperatures in crop development to optimize climate induced factors on the plant growth anomalies. The results are completed by utilizing Geographic Information Science, e.g. tools of ArcMap 10.3.1 and databases of ground truth and meteorological information. Synthetic Aperture Radar (SAR) images from German Aerospace Center (DLR) are acquired and the field survey datasets are sampled, each per month for three years (2010-2012) but only for the crop seasons (April-October). Correlation between SAR images and farmland anomalies is investigated in accordance with daily heat accumulations and a comparison of the three years’ SAR backscatter signatures is explained for corn and wheat. Finding the influence of daily temperatures on crops and hence on the TerraSAR-X backscatter is developed by Growing Degree Days (GDD) which appears to be the most suitable parameter for this purpose. Observation of GDD permits that the coolest year was 2010, either rest of the years were warmer and GDD accumulated in 2011 was higher as compared to that of 2012 in the first half of the year, however 2012 had rather more heat accumulation in the second half of the year. SAR backscatter from farmland depicts the crop development stages which depend upon the time when satellite captures data during the crop season. It varies with different development stages of crop plants. Backscatter of each development stage changes as the roughness and the moisture content (dielectric property) of the plants changes and local temperature directly impacts crop growth and hence the development stages.

This paper presents a methodology for monitoring vegetation in the Pays de Brest using new series of Sentinel-1 satellite images combining with Sentinel-2 and SPOT-6. This work consists of establishing an interferogram method of the main types of vegetation in order to achieve the coherence of a multi-temporal Sentinel-1 radar image series, in SLC format (C band, VV and VH polarization), between 2015 and 2016. We then proceed to calculating the radar backscatter coefficient based on Sentinel 1 images in GRD format. Multi-date and multipolarized color compositions will be made to detect changes. It also shows the importance of data synergy to obtain an excellent accuracy using Random Forest classification.