Integrating Vegetation Indices Models and Phenological Classification with Composite SAR and Optical Data for Cereal Yield Estimation in Finland (Part I)

Special Issue Microwave Remote Sensing.Abstract: During 1996–2006 the Ministry of Agriculture and Forestry in Finland, MTT Agrifood Research Finland and the Finnish Geodetic Institute carried out a joint remote sensing satellite research project. It evaluated the applicability of composite multispectral SAR and optical satellite data for cereal yield estimations in the annual crop inventory program. Three Vegetation Indices models (VGI, Infrared polynomial, NDVI and Composite multispetral SAR and NDVI) were validated to estimate cereal yield levels using solely optical and SAR satellite data (Composite Minimum Dataset). The average R2 for cereal yield (yb) was 0.627. The averaged composite SAR modeled grain yield level was 3,750 kg/ha (RMSE = 10.3%, 387 kg/ha) for high latitude spring cereals (4,018 kg/ha for spring wheat, 4,037 kg/ha for barley and 3,151 kg/ha for oats). Keywords: Composite multispectral modeling; SAR; classification; SatPhenClass algorithm; minimum dataset; cereal yield; phenology; LAI-bridge; CAP; IACS; FLPISPeer reviewe

Cereal yield modeling in Finland using optical and radar remote sensing : Special Issue Global Croplands

Peer reviewe

Effects of changing cultural practices on C-band SAR backscatter using Envisat ASAR data in the Mekong River Delta

International audienceChanges in rice cultivation systems have been observed in the Mekong River Delta, Vietnam. Among the changes in cultural practices, the change from transplanting to direct sowing, the use of water-saving technology, and the use of high production method could have impacts on radar remote sensing methods previously developed for rice monitoring. Using Envisat (Environmental Satellite) ASAR (Advanced Synthetic Aperture Radar) data over the province of An Giang, this study showed that the radar backscattering behaviour is much different from that of the reported traditional rice. At the early stage of the season, direct sowing on fields with rough and wet soil surface provides very high backscatter values for HH (Horizontal transmit - Horizontal receive polarisation) and VV (Vertical transmit - Vertical receive polarisation) data, as a contrast compared to the very low backscatter of fields covered with water before emergence. The temporal increase of the backscatter is therefore not observed clearly over direct sowing fields. Hence, the use of the intensity temporal change as a rice classifier proposed previously may not apply. Due to the drainage that occurs during the season, HH, VV and HH/VV are not strongly related to biomass, in contrast with past results. However, HH/VV ratio could be used to derive the rice/non-rice classification algorithm for all conditions of rice fields in the test province. The mapping results using the HH/VV polarization ratio at a single date in the middle period of the rice season were assessed using statistical data at different districts in the province, where very high accuracy was found. The method can be applied to other regions, provided that the synthetic aperture radar data are acquired during the peak period of the rice season, and that few training fields provide adjusted threshold values used in the method

Coupled modelling of land surface microwave interactions using ENVISAT ASAR data

In the last decades microwave remote sensing has proven its capability to provide valuable information about the land surface. New sensor generations as e.g. ENVISAT ASAR are capable to provide frequent imagery with an high information content. To make use of these multiple imaging capabilities, sophisticated parameter inversion and assimilation strategies have to be applied. A profound understanding of the microwave interactions at the land surface is therefore essential. The objective of the presented work is the analysis and quantitative description of the backscattering processes of vegetated areas by means of microwave backscattering models. The effect of changing imaging geometries is investigated and models for the description of bare soil and vegetation backscattering are developed. Spatially distributed model parameterisation is realized by synergistic coupling of the microwave scattering models with a physically based land surface process model. This enables the simulation of realistic SAR images, based on bioand geophysical parameters. The adequate preprocessing of the datasets is crucial for quantitative image analysis. A stringent preprocessing and sophisticated terrain geocoding and correction procedure is therefore suggested. It corrects the geometric and radiometric distortions of the image products and is taken as the basis for further analysis steps. A problem in recently available microwave backscattering models is the inadequate parameterisation of the surface roughness. It is shown, that the use of classical roughness descriptors, as the rms height and autocorrelation length, will lead to ambiguous model parameterisations. A new two parameter bare soil backscattering model is therefore recommended to overcome this drawback. It is derived from theoretical electromagnetic model simulations. The new bare soil surface scattering model allows for the accurate description of the bare soil backscattering coefficients. A new surface roughness parameter is introduced in this context, capable to describe the surface roughness components, affecting the backscattering coefficient. It is shown, that this parameter can be directly related to the intrinsic fractal properties of the surface. Spatially distributed information about the surface roughness is needed to derive land surface parameters from SAR imagery. An algorithm for the derivation of the new surface roughness parameter is therefore suggested. It is shown, that it can be derived directly from multitemporal SAR imagery. Starting from that point, the bare soil backscattering model is used to assess the vegetation influence on the signal. By comparison of the residuals between measured backscattering coefficients and those predicted by the bare soil backscattering model, the vegetation influence on the signal can be quantified. Significant difference between cereals (wheat and triticale) and maize is observed in this context. It is shown, that the vegetation influence on the signal can be directly derived from alternating polarisation data for cereal fields. It is dependant on plant biophysical variables as vegetation biomass and water content. The backscattering behaviour of a maize stand is significantly different from that of other cereals, due to its completely different density and shape of the plants. A dihedral corner reflection between the soil and the stalk is identified as the major source of backscattering from the vegetation. A semiempirical maize backscattering model is suggested to quantify the influences of the canopy over the vegetation period. Thus, the different scattering contributions of the soil and vegetation components are successfully separated. The combination of the bare soil and vegetation backscattering models allows for the accurate prediction of the backscattering coefficient for a wide range of surface conditions and variable incidence angles. To enable the spatially distributed simulation of the SAR backscattering coefficient, an interface to a process oriented land surface model is established, which provides the necessary input variables for the backscattering model. Using this synergistic, coupled modelling approach, a realistic simulation of SAR images becomes possible based on land surface model output variables. It is shown, that this coupled modelling approach leads to promising and accurate estimates of the backscattering coefficients. The remaining residuals between simulated and measured backscatter values are analysed to identify the sources of uncertainty in the model. A detailed field based analysis of the simulation results revealed that imprecise soil moisture predictions by the land surface model are a major source of uncertainty, which can be related to imprecise soil texture distribution and soil hydrological properties. The sensitivity of the backscattering coefficient to the soil moisture content of the upper soil layer can be used to generate soil moisture maps from SAR imagery. An algorithm for the inversion of soil moisture from the upper soil layer is suggested and validated. It makes use of initial soil moisture values, provided by the land surface process model. Soil moisture values are inverted by means of the coupled land surface backscattering model. The retrieved soil moisture results have an RMSE of 3.5 Vol %, which is comparable to the measurement accuracy of the reference field data. The developed models allow for the accurate prediction of the SAR backscattering coefficient. The various soil and vegetation scattering contributions can be separated. The direct interface to a physically based land surface process model allows for the spatially distributed modelling of the backscattering coefficient and the direct assimilation of remote sensing data into a land surface process model. The developed models allow for the derivation of static and dynamic landsurface parameters, as e.g. surface roughness, soil texture, soil moisture and biomass from remote sensing data and their assimilation in process models. They are therefore reliable tools, which can be used for sophisticated practice oriented problem solutions in manifold manner in the earth and environmental sciences

Offshore oil spill detection using synthetic aperture radar

Among the different types of marine pollution, oil spill has been considered as a major threat to the sea ecosystems. The source of the oil pollution can be located on the mainland or directly at sea. The sources of oil pollution at sea are discharges coming from ships, offshore platforms or natural seepage from sea bed. Oil pollution from sea-based sources can be accidental or deliberate. Different sensors to detect and monitor oil spills could be onboard vessels, aircraft, or satellites. Vessels equipped with specialised radars, can detect oil at sea but they can cover a very limited area. One of the established ways to monitor sea-based oil pollution is the use of satellites equipped with Synthetic Aperture Radar (SAR).The aim of the work presented in this thesis is to identify optimum set of feature extracted parameters and implement methods at various stages for oil spill detection from Synthetic Aperture Radar (SAR) imagery. More than 200 images of ERS-2, ENVSAT and RADARSAT 2 SAR sensor have been used to assess proposed feature vector for oil spill detection methodology, which involves three stages: segmentation for dark spot detection, feature extraction and classification of feature vector. Unfortunately oil spill is not only the phenomenon that can create a dark spot in SAR imagery. There are several others meteorological and oceanographic and wind induced phenomena which may lead to a dark spot in SAR imagery. Therefore, these dark objects also appear similar to the dark spot due to oil spill and are called as look-alikes. These look-alikes thus cause difficulty in detecting oil spill spots as their primary characteristic similar to oil spill spots. To get over this difficulty, feature extraction becomes important; a stage which may involve selection of appropriate feature extraction parameters. The main objective of this dissertation is to identify the optimum feature vector in order to segregate oil spill and ‘look-alike’ spots. A total of 44 Feature extracted parameters have been studied. For segmentation, four methods; based on edge detection, adaptive theresholding, artificial neural network (ANN) segmentation and the other on contrast split segmentation have been implemented. Spot features are extracted from both the dark spots themselves and their surroundings. Classification stage was performed using two different classification techniques, first one is based on ANN and the other based on a two-stage processing that combines classification tree analysis and fuzzy logic. A modified feature vector, including both new and improved features, is suggested for better description of different types of dark spots. An ANN classifier using full spectrum of feature parameters has also been developed and evaluated. The implemented methodology appears promising in detecting dark spots and discriminating oil spills from look-alikes and processing time is well below any operational service requirements

Simulating SAR geometric distortions and predicting Persistent Scatterer densities for ERS-1/2 and ENVISAT C-band SAR and InSAR applications: nationwide feasibility assessment to monitor the landmass of Great Britain with SAR imagery

We assess the feasibility of monitoring the landmass of Great Britain with satellite Synthetic Aperture Radar (SAR) imagery, by analysing ERS-1/2 SAR and ENVISAT IS2 Advanced SAR (ASAR) archive data availability, geometric distortions and land cover control on the success of (non-)interferometric analyses. Our assessment both addresses the scientific and operational question of whether a nationwide SAR-based monitoring of ground motion would succeed in Great Britain, and helps to understand controlling factors and possible solutions to overcome the limitations of undertaking SAR-based imaging of the landmass. This is the first time such a nationwide assessment is performed in preparation for acquisition and processing of SAR data in the United Kingdom, and any other country in the world. Analysis of the ERS-1/2 and ENVISAT archives reveals potential for multi-interferogram SAR Interferometry (InSAR) for the entirety of Britain using ERS-1/2 in descending mode, with 100% standard image frames showing at least 20 archive scenes available. ERS-1/2 ascending and both ENVISAT modes show potential for non-interferometric and single-pair InSAR for the vast majority of Britain, and multi-interferogram only for 13% to 38% of the available standard frames. Based on NEXTMap® Britain Digital Terrain Model (DTM) we simulate SAR layover, foreshortening and shadow to the ERS-1/2 and ENVISAT Lines-Of-Sight (LOS), and quantify changes of SAR distortions with variations in mode, LOS incidence angles and ground track angles, local terrain orientation, and the effect of scale due to the input DTM resolution. The simulation is extended to the ~ 230,000 km2 landmass, and shows limited control of local topography on the radar terrain visibility. According to the 50 m to 5 m DTM-based simulations, ~ 1.0–1.4% of Great Britain could potentially be affected by shadow and layover in each mode. Only ~ 0.02–0.04% overlapping between ascending and descending mode distortions is found, this indicating the negligible proportion of the landmass that cannot be monitored using either imaging mode. We calibrate the CORINE Land Cover 2006 (CLC2006) using Persistent Scatterer (PS) datasets available for London, Stoke-On-Trent, Newcastle and Bristol, to quantify land cover control on the PS distribution and characterise the CLC2006 classes in terms of the potential PS density they could provide. Despite predominance of rural land cover types, we predict potential for over 12.8 M monitoring targets for each acquisition mode using a set of image frames covering the entire landmass. We validate our assessment by processing with the Interferometric Point Target Analysis (IPTA) 55 ERS-1/2 SAR scenes depicting South Wales between 1992 and 1999. Although absolute differences between predicted and observed target density are revealed, relative densities and rankings among the various CLC2006 classes are found constant across the calibration and validation datasets. Rescaled predictions for Britain show potential for a total of 2.5 M monitoring targets across the landmass. We examine the use of the topographic and land cover feasibility maps for landslide studies in relation to the British Geological Survey's National Landslide Database and DiGMapGB mass movement layer. Building upon recent literature, we finally discuss future perspectives relating to the replication of our feasibility assessment to account for higher resolution SAR imagery, new Earth explorers (e.g., Sentinel-1) and improved processing techniques, showing potential to generate invaluable sources of information on land motions and geohazards in Great Britai

The hydrology of sand rivers in Zimbabwe and the use of remote sensing to assess their level of saturation

Sand rivers are ephemeral watercourses containing sand that are occasionally flooded with rainwater runoff during the rainy season. Although the riverbed appears dry for most of the year, there is perennial groundwater flow within the sand. This water flowing beneath the surface is a valuable resource for local communities; nonetheless our understanding of such river systems is limited. Hence, this paper aims to improve our understanding of the hydrology of sand rivers and to examine the potential use of remote sensing to detect the presence of water in the sand. The relationship between rainfall events and changes in the water level of two sand rivers in the Matabeleland South Province of Zimbabwe was investigated. A lagged relationship was observed for the Manzamnyama River but for the Shashani River the relationship was seen only when considering cumulative rainfall events. The comparison of the modelled flow as simulated by a water balance model with observations revealed the important influence of the effective sediment depth on the recharge and recession of the alluvial channels in addition to the length of the channel. The possibility of detecting water in the alluvial sands was investigated using remote sensing. During the wet season, optical images showed that the presence of water on the riverbed was associated with a smooth signal, as it tends to reflect the incident radiation. A chronological analysis of radar images for different months of the year demonstrates that it is possible to detect the presence of water in the sand rivers. These results are a first step towards the development of a methodology that would aim to use remote sensing to help reducing survey costs by guiding exploratory activities to areas showing signs of water abstraction potential