Francesco Holecz

Towards an Operational SAR-Based Rice Monitoring System in Asia: Examples from 13 Demonstration Sites across Asia in the RIICE Project

Rice is the most important food security crop in Asia. Information on its seasonal extent forms part of the national accounting of many Asian countries. Synthetic Aperture Radar (SAR) imagery is highly suitable for detecting lowland rice, especially in tropical and subtropical regions, where pervasive cloud cover in the rainy seasons precludes the use of optical imagery. Here, we present a simple, robust, rule-based classification for mapping rice area with regularly acquired, multi-temporal, X-band, HH-polarized SAR imagery and site-specific parameters for classification. The rules for rice detection are based on the well-studied temporal signature of rice from SAR backscatter and its relationship with crop stages. We also present a procedure for estimating the parameters based on “temporal feature descriptors” that concisely characterize the key information in the rice signatures in monitored field locations within each site. We demonstrate the robustness of the approach on a very large dataset. A total of 127 images across 13 footprints in six countries in Asia were obtained between October 2012, and April 2014, covering 4.78 m ha. More than 1900 in-season site visits were conducted across 228 monitoring locations in the footprints for classification purposes, and more than 1300 field observations were made for accuracy assessment. Some 1.6 m ha of rice were mapped with classification accuracies from 85% to 95% based on the parameters that were closely related to the observed temporal feature descriptors derived for each site. The 13 sites capture much of the diversity in water management, crop establishment and maturity in South and Southeast Asia. The study demonstrates the feasibility of rice detection at the national scale using multi-temporal SAR imagery with robust classification methods and parameters that are based on the knowledge of the temporal dynamics of the rice crop. We highlight the need for the development of an open-access library of temporal signatures, further investigation into temporal feature descriptors and better ancillary data to reduce the risk of misclassification with surfaces that have temporal backscatter dynamics similar to those of rice. We conclude with observations on the need to define appropriate SAR acquisition plans to support policies and decisions related to food security.

Snow cover monitoring using multitemporal ERS-1 SAR data

A method is presented for the application of ERS-1 SAR images to snow cover monitoring in high mountain areas. It is based on an image coregistration as well as a geometric and radiometric correction to remove the relief induced distortions. Using the optimal resolution approach ORA synthetic SAR images are calculated to significantly improve the thematic information content. The multitemporal optimal resolution approach MORA uses a sequence of ascending and descending scenes. It builds on the functionality of ORA by coregistering image pairs and extracting geoecophysical parameters through successive ratioing of the backscatter information. Wet snow cover monitoring was done by calculating ratios between the backscattering coefficients of the synthetic SAR images and a snow-free reference scene. The method was applied to a dataset covering the 1993 melting season. The climbing of the snow-line could clearly be detected. The potentials and limitations of the approach are discussed.

Exploitation of SAR and Optical Sentinel Data to Detect Rice Crop and Estimate Seasonal Dynamics of Leaf Area Index

This paper presents and evaluates multitemporal LAI estimates derived from Sentinel-2A data on rice cultivated area identified using time series of Sentinel-1A images over the main European rice districts for the 2016 crop season. This study combines the information conveyed by Sentinel-1A and Sentinel-2A into a high-resolution LAI retrieval chain. Rice crop was detected using an operational multi-temporal rule-based algorithm, and LAI estimates were obtained by inverting the PROSAIL radiative transfer model with Gaussian process regression. Direct validation was performed with in situ LAI measurements acquired in coordinated field campaigns in three countries (Italy, Spain and Greece). Results showed high consistency between estimates and ground measurements, revealing high correlations (R2 > 0.93) and good accuracies (RMSE < 0.83, rRMSEm < 23.6% and rRMSEr < 16.6%) in all cases. Sentinel-2A estimates were compared with Landsat-8 showing high spatial consistency between estimates over the three areas. The possibility to exploit seasonally-updated crop mask exploiting Sentinel-1A data and the temporal consistency between Sentinel-2A and Landsat-7/8 LAI time series demonstrates the feasibility of deriving operationally high spatial-temporal decametric multi-sensor LAI time series useful for crop monitoring.

Downstream Services for Rice Crop Monitoring in Europe: From Regional to Local Scale

The ERMES agromonitoring system for rice cultivations integrates EO data at different resolutions, crop models, and user-provided in situ data in a unified system, which drives two operational downstream services for rice monitoring. The first is aimed at providing information concerning the behavior of the current season at regional/rice district scale, while the second is dedicated to provide farmers with field-scale data useful to support more efficient and environmentally friendly crop practices. In this contribution, we describe the main characteristics of the system, in terms of overall architecture, technological solutions adopted, characteristics of the developed products, and functionalities provided to end users. Peculiarities of the system reside in its ability to cope with the needs of different stakeholders within a common platform, and in a tight integration between EO data processing and information retrieval, crop modeling, in situ data collection, and information dissemination. The ERMES system has been operationally tested in three European rice-producing countries (Italy, Spain, and Greece) during growing seasons 2015 and 2016, providing a great amount of near-real-time information concerning rice crops. Highlights of significant results are provided, with particular focus on real-world applications of ERMES products and services. Although developed with focus on European rice cultivations, solutions implemented in the ERMES system can be, and are already being, adapted to other crops and/or areas of the world, thus making it a valuable testing bed for the development of advanced, integrated agricultural monitoring systems.

Height model generation, automatic geocoding and a mosaicing using airborne AeS-1 InSAR data

The goal of this paper is to present the generation of high resolution digital surface models using airborne AeS-1 interferometric SAR data, their automatic geocoding and mosaicing. In order to be able to carry out these steps, high precision differential Global Positioning System data, high frequency attitude data of the platform, exact time synchronization and range delay of the system must be known. Since in the airborne case motion instabilities are large, due to dynamic properties of the aircraft and atmospheric turbulences, precise motion measurements of the platform are extracted and considered during the SAR processing. Once that all these basic requirements are fulfilled, one is able, using the processing reference tracks, and exploiting a forward-backward geocoding, to convert the phase differences to elevation data and to geolocate them by taking into account all geodetic and cartographic transforms. Results based on 400 MHz X-band InSAR data show that the derived surface model has a positioning accuracy in the order of 0.5 m and a height accuracy better than 0.3 m.

Rapid Assessment of Crop Status: An Application of MODIS and SAR Data to Rice Areas in Leyte, Philippines Affected by Typhoon Haiyan

Asian countries strongly depend on rice production for food security. The major rice-growing season (June to October) is highly exposed to the risk of tropical storm related damage. Unbiased and transparent approaches to assess the risk of rice crop damage are essential to support mitigation and disaster response strategies in the region. This study describes and demonstrates a method for rapid, pre-event crop status assessment. The ex-post test case is Typhoon Haiyan and its impact on the rice crop in Leyte Province in the Philippines. A synthetic aperture radar (SAR) derived rice area map was used to delineate the area at risk while crop status at the moment of typhoon landfall was estimated from specific time series analysis of Moderate Resolution Imaging Spectroradiometer (MODIS) data. A spatially explicit indicator of risk of standing crop loss was calculated as the time between estimated heading date and typhoon occurrence. Results of the analysis of pre- and post-event SAR images showed that 6500 ha were flooded in northeastern Leyte. This area was also the region most at risk to storm related crop damage due to late establishment of rice. Estimates highlight that about 700 ha of rice (71% of which was in northeastern Leyte) had not reached maturity at the time of the typhoon event and a further 8400 ha (84% of which was in northeastern Leyte) were likely to be not yet harvested. We demonstrated that the proposed approach can provide pre-event, in-season information on the status of rice and other field crops and the risk of damage posed by tropical storms.

Desertification - a land degradation support service

As the land degradation is a complex process, influenced also by climatic and human-induced factors, its understanding and mapping requires a methodology based on Earth Observation integrated with ancillary data such as socio- economic and, optionally, meteorological data. For this reason, the service, which has been defined, implemented, and validated in close cooperation with End Users, is based both on scaleable indexes - derived from Earth Observation data - and on indicators relevant to the influence of human and animal pressure on natural resources. The ultimate goal is the generation of vulnerability maps to provide to decision makers for prevention purposes.

Impact of Topographic Correction on Estimation of Aboveground Boreal Biomass Using Multi-temporal, L-Band Backscatter

Synthetic aperture radar (SAR) has been shown to be a useful tool for estimating aboveground biomass (AGB), due to the strong correlation between the biomass and backscatter. In particular, L-band SAR is effective for estimating the lower range of biomass that characterizes most boreal forests. Unfortunately, the topographic impact on backscatter can dominate the normal forest signal variation. Since many boreal environments have significant topography, we investigate several topographic correction techniques to determine their effect upon AGB prediction accuracy. Different approaches to addressing the topography include: 1) no correction, 2) local incidence angle (LIA) correction, 3) pixel-area correction, and 4) a novel empirical slope correction. The investigation was performed for a data-rich experimental area near Tok, Alaska, for which Advanced Land Observing Satellite Phased Array type L-Band Synthetic Aperture Radar (ALOS PALSAR), field plots, lidar acquisitions, and a high-quality digital elevation model (DEM) existed. Biomass estimations were performed using both single- and dual-polarization (HH and HV) regressions against field plot data. The biomass estimation for each of the topographic corrections was compared with the field plot biomass, as well as more extensive lidar biomass estimations. The results showed a clear improvement in AGB estimation accuracy from no correction, to LIA, to pixel-area, to the novel pixel-area plus empirical slope correction. Using the field plot data for validation, the SAR root mean square error (RMSE) derived from the best approach was found to be 37.3 Mg/ha over a biomass range of 0-250 Mg/ha, only marginally less accurate than the 33.5 Mg/ha accuracy of the much more expensive lidar technique.

Radiometric calibration of airborne SAR imagery

A method to compute a fully calibrated airborne synthetic aperture radar (SAR) image is presented. In a first step the data is processed taking motion compensation errors into account by using the reflectivity displacement method (RDM). This paper focuses on the second step, namely the rigorous determination of the backscattering coefficient /spl sigma//sup 0/ and /spl gamma/. To determine the exact local area and the local incidence angle, high precision flight path data, a high resolution digital elevation model (DEM), as well as sensor and processor characteristics need to be considered. Radiometric distortions due to the antenna diagram, to the reciprocal value of the third power of the slant range, and to the sine of the local incidence angle are also corrected, by taking into account aircraft displacements and terrain height variations. Magnitude data of the E-SAR system of the German Aerospace Research Establishment (DLR) collected over a hilly region in Switzerland are used to demonstrate the calibration technique.<<ETX>>

Generation of synthetic images for the alleviation of radiometric influences induced by the topography

This paper presents results from the application of model based techniques for an efficient correction to the topographically induced radiometric influences on the remotely sensed imagery. Thus, in a first step, relief induced geometric distortions of optical imagery must be removed, taking terrain elevation into account. In a second step the radiometry of the image is considered. Synthetic images are generated based on the Digital Elevation Models, and sun and satellite position at the time of acquisition of the image. The synthetically derived images model the image formation process for the direct lighted and shaded areas, using direct, indirect, and diffuse illumination. The resemblance of the synthesized image to reality is evaluated for a mountainous alpine region covered by snow. In a last step the data derived from the synthetic image are used for radiometric correction of the effects of the topography.