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Setiyono T.D.,International Rice Research Institute | Holecz F.,Sarmap | Khan N.I.,International Rice Research Institute | Barbieri M.,Sarmap | And 5 more authors.
IOP Conference Series: Earth and Environmental Science | Year: 2017

Reliable and regular rice information is essential part of many countries' national accounting process but the existing system may not be sufficient to meet the information demand in the context of food security and policy. Synthetic Aperture Radar (SAR) imagery is highly suitable for detecting lowland paddy rice, especially in tropical region where pervasive cloud cover in the rainy seasons limits the use of optical imagery. This study uses multi-temporal X-band and C-band SAR imagery, automated image processing, rule-based classification and field observations to classify rice in multiple locations across Tropical Asia and assimilate the information into ORYZA Crop Growth Simulation model (CGSM) to generate high resolution yield maps. The resulting cultivated rice area maps had classification accuracies above 85% and yield estimates were within 81-93% agreement against district level reported yields. The study sites capture much of the diversity in water management, crop establishment and rice maturity durations and the study demonstrates the feasibility of rice detection, yield monitoring, and damage assessment in case of climate disaster at national and supra-national scales using multi-temporal SAR imagery combined with CGSM and automated methods. © Published under licence by IOP Publishing Ltd.

Campos-Taberner M.,University of Valencia | Garcia-Haro F.J.,University of Valencia | Camps-Valls G.,University of Valencia | Grau-Muedra G.,University of Valencia | And 10 more authors.
Remote Sensing | Year: 2017

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.

Raviz J.,International Rice Research Institute | Laborte A.,International Rice Research Institute | Barbieri M.,Sarmap | Mabalay M.R.,Philippine Rice Research Institute PhilRice | And 4 more authors.
37th Asian Conference on Remote Sensing, ACRS 2016 | Year: 2016

This study aims to assess the use of X- and C-band Synthetic Aperture Radar (SAR) for mapping rice areas. Specifically, we used TerraSAR-X (TSX, X-band, HH polarization) and Sentinel-1A (S1A, C-band, VV and VH polarizations) data from two cropping seasons: 2015 wet season (WS, June to October) and 2016 dry season (DS, October to February) in Central Luzon, Philippines. A total of 130 TSX and 80 S1A images were used to map rice areas in these two seasons. We used the image processing software MAPscape-RICE® to process SAR images and to generate the rice maps. The procedure involved three main steps: (1) basic processing, (2) rice area mapping, and (3) validation. In basic processing (step 1), original SAR data were converted to terrain-geocoded images (backscatter δo values). The multi-temporal SAR signature from step 1 were then analysed to set the appropriate thresholds following a rule-based rice detection algorithm (step 2). The rules for rice detection were based on a well-studied temporal signature of rice gathered from monitored sites and its relationship with SAR backscatter. Although the same procedure was followed, different thresholds were applied to the SAR data. Accuracies were compared using ground observations collected towards the end of each growing season (step 3). Based on 126 ground observations gathered in the study area in 2015 WS, the overall accuracies of the rice maps generated were 86% (kappa=0.71) for TSX, 80% (kappa=0.60) for S1A-VV, and 76% (kappa=0.52) for S1A-VH. In 2016 DS, the overall accuracies based on 132 ground observations were 86% for TSX, 82% for S1A-VV and 70% for S1A-VH. From this study, TSX consistently provided the highest accuracies and S1A-VH the lowest. S1A-VH may not be useful for mapping rice areas. S1A-VV, on the other hand, still provided high accuracies. Better thresholds will be explored to improve accuracies.

Chen L.,Peking University | Qin Q.,Sarmap | Chen C.,Orange Group | Jiang H.,Sarmap
International Geoscience and Remote Sensing Symposium (IGARSS) | Year: 2012

Remote sensing technology is considered a fast and effective method to prospect ore. Now, this method is used in Gejiu tin deposit of YunNan in order to extract more accurate mineralization abnormal information. In this study, first through the band math method and principal component analysis method, the mineralization alternation can be extracted in ETM data. Then using ASTER data the limonitization, the chloritization and the dolomitization are extracted by the spectral angle method. At last, the trace elements of the vegetation are statistically analyzed and the vegetation mineralization alteration information is extracted by two different methods in ASTER data. The result shows that the alternation information distributions are consistent in the east-south study area and match with the field exploration. Consequently the extracted results are effective. © 2012 IEEE.

Pazhanivelan S.,Tamil Nadu Agricultural University | Kannan P.,Tamil Nadu Agricultural University | Christy Nirmala Mary P.,Tamil Nadu Agricultural University | Subramanian E.,Tamil Nadu Agricultural University | And 6 more authors.
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives | Year: 2015

Rice is the most important cereal crop governing food security in Asia. Reliable and regular information on the area under rice production is the basis of policy decisions related to imports, exports and prices which directly affect food security. Recent and planned launches of SAR sensors coupled with automated processing can provide sustainable solutions to the challenges on mapping and monitoring rice systems. High resolution (3m) Synthetic Aperture Radar (SAR) imageries were used to map and monitor rice growing areas in selected three sites in TamilNadu, India to determine rice cropping extent, track rice growth and estimate yields. A simple, robust, rule-based classification for mapping rice area with multi-temporal, X-band, HH polarized SAR imagery from COSMO Skymed and TerraSAR X and site specific parameters were used. The robustness of the approach is demonstrated on a very large dataset involving 30 images across 3 footprints obtained during 2013-14. A total of 318 in-season site visits were conducted across 60 monitoring locations for rice classification and 432 field observations were made for accuracy assessment. Rice area and Start of Season (SoS) maps were generated with classification accuracies ranging from 87-92 per cent. Using ORYZA2000, a weather driven process based crop growth simulation model; yield estimates were made with the inclusion of rice crop parameters derived from the remote sensing products viz., seasonal rice area, SoS and backscatter time series. Yield Simulation accuracy levels of 87 per cent at district level and 85-96 per cent at block level demonstrated the suitability of remote sensing products for policy decisions ensuring food security and reducing vulnerability of farmers in India.

Asilo S.,University of Twente | Asilo S.,International Rice Research Institute | de Bie K.,University of Twente | Skidmore A.,University of Twente | And 3 more authors.
Remote Sensing | Year: 2014

Different rice crop information can be derived from different remote sensing sources to provide information for decision making and policies related to agricultural production and food security. The objective of this study is to generate complementary and comprehensive rice crop information from hypertemporal optical and multitemporal high-resolution SAR imagery. We demonstrate the use of MODIS data for rice-based system characterization and X-band SAR data from TerraSAR-X and CosmoSkyMed for the identification and detailed mapping of rice areas and flooding/transplanting dates. MODIS was classified using ISODATA to generate cropping calendar, cropping intensity, cropping pattern and rice ecosystem information. Season and location specific thresholds from field observations were used to generate detailed maps of rice areas and flooding/transplanting dates from the SAR data. Error matrices were used for the accuracy assessment of the MODIS-derived rice characteristics map and the SAR-derived detailed rice area map, while Root Mean Square Error (RMSE) and linear correlation were used to assess the TSX-derived flooding/transplanting dates. Results showed that multitemporal high spatial resolution SAR data is effective for mapping rice areas and flooding/transplanting dates with an overall accuracy of 90% and a kappa of 0.72 and that hypertemporal moderate-resolution optical imagery is effective for the basic characterization of rice areas with an overall accuracy that ranged from 62% to 87% and a kappa of 0.52 to 0.72. This study has also provided the first assessment of the temporal variation in the backscatter of rice from CSK and TSX using large incidence angles covering all rice crop stages from pre-season until harvest. This complementarity in optical and SAR data can be further exploited in the near future with the increased availability of space-borne optical and SAR sensors. This new information can help improve the identification of rice areas.

Milisavljevic N.,Royal Military Academy | Holecz F.,Sarmap | Bloch I.,Orange Group | Closson D.,Royal Military Academy | Collivignarelli F.,Sarmap
International Geoscience and Remote Sensing Symposium (IGARSS) | Year: 2012

The aim of the approach proposed in this paper is to determine a potential crop extent prior to the crop season, by determining regions that might change in time vs. those that surely do not change. We use multi-annual PALSAR-1 data since in dry conditions, L-band HH/HV data have a potential of distinguishing between bare soil and other classes. In addition, a more accurate map can be reached with multi-temporal data than using a single date. We work on HH and HV data sets separately and analyze the two outputs using ground-truth information. In a final phase, we combine these two outputs and compare the result with the ground-truth too, to test the usefulness of fusing the HH/HV information. This approach is the first step in our three-step procedure for estimation of cultivated area in small plot agriculture in Malawi. Validation results show that the proposed approach is promising. © 2012 IEEE.

Lisini G.,Centro Studi Rischio e Sicurezza | Gamba P.,University of Pavia | Dell'Acqua F.,University of Pavia | Holecz F.,Sarmap
International Journal of Image and Data Fusion | Year: 2011

In this article, we introduce a unitary approach to road extraction in wide area images, obtained by means of satellite sensors in both the optical/infrared and microwave domains. Despite the large amount of methodologies discussed in technical literature for road extraction, they have been mostly tested on relatively small portions of satellite images. Moreover, in many cases, the method targeted an optical or a synthetic aperture radar (SAR) image, and a unitary strategy is missing. This study is aimed at bridging these gaps and provides a unique framework for the extraction of roads with different characteristics using optical or SAR data sets. The approach exploits a multi-scale analysis to adapt to the different resolutions of data and a pre-processing step to adapt to the different wavelengths of data. When possible, the framework allows the fusion of the road networks extracted from optical and SAR data of the same area. The soundness of the approach is proved by means of the analysis of Landsat and ALOS data of an area in Congo. © 2011 Taylor & Francis.

Milisavljevic N.,Royal Military Academy | Closson D.,Royal Military Academy | Holecz F.,Sarmap | Collivignarelli F.,Sarmap | Pasquali P.,Sarmap
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives | Year: 2015

Land-cover changes occur naturally in a progressive and gradual way, but they may happen rapidly and abruptly sometimes. Very high resolution remote sensed data acquired at different time intervals can help in analyzing the rate of changes and the causal factors. In this paper, we present an approach for detecting changes related to disasters such as an earthquake and for mapping of the impact zones. The approach is based on the pieces of information coming from SAR (Synthetic Aperture Radar) and on their combination. The case study is the 22 February 2011 Christchurch earthquake. The identification of damaged or destroyed buildings using SAR data is a challenging task. The approach proposed here consists in finding amplitude changes as well as coherence changes before and after the earthquake and then combining these changes in order to obtain richer and more robust information on the origin of various types of changes possibly induced by an earthquake. This approach does not need any specific knowledge source about the terrain, but if such sources are present, they can be easily integrated in the method as more specific descriptions of the possible classes. A special task in our approach is to develop a scheme that translates the obtained combinations of changes into ground information. Several algorithms are developed and validated using optical remote sensing images of the city two days after the earthquake, as well as our own ground-truth data. The obtained validation results show that the proposed approach is promising.

Holecz F.,Sarmap | Gatti L.,Sarmap | Collivignarelli F.,Sarmap | Barbieri M.,Sarmap
International Geoscience and Remote Sensing Symposium (IGARSS) | Year: 2015

Irrespective if remote sensing data are acquired by active or passive sensors, high or medium resolution, key information is the temporal signature. This is particularly true - but not limited to - agriculture, where the spatio-temporal dynamic is significant. Spectral (here meant in frequency and polarimetric terms) information, definitely, complements the temporal one. In this paper, temporal-spectral descriptors are derived from sigma nought time series acquired from various Synthetic Aperture Radar (SAR) systems over different agro-ecological zones in Senegal, The Gambia, Vietnam. It is shown that: - a limited set of temporal descriptors is sufficient to generate a reliable crop map; - the selection of the appropriate time period is crucial; - the temporal combination of wavelengths and polarizations may enhance the level of detail and product's reliability; - the use of temporal descriptors derived from multi-annual, annual, and seasonal time series data provides, from an agronomic perspectives, complementary information; - temporal-spectral descriptors have an agronomic meaning, hence they should be used in knowledge based classifiers; - by sparse time series the adoption of temporal-spectral descriptors is more effective than a dedicated crop detection algorithm. © 2015 IEEE.

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