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

Estimating rice production in the Mekong Delta, Vietnam, utilizing time series of Sentinel-1 SAR data

Rice is the most important food crop in Asia and rice exports can significantly contribute to a country's GDP. Vietnam is the third largest exporter and fifth largest producer of rice, the majority of which is grown in the Mekong Delta. The cultivation of rice plants is important, not only in the context of food security, but also contributes to greenhouse gas emissions, provides man-made wetlands as an ecosystem, sustains smallholders in Asia and influences water resource planning and run-off water management. Rice growth can be monitored with Synthetic Aperture Radar (SAR) time series due to the agronomic flooding followed by rapid biomass increase affecting the backscatter signal. With the advent of Sentinel-1 a wealth of free and open SAR data is available to monitor rice on regional or larger scales and limited data availability should not be an issue from 2015 onwards. We used Sentinel-1 SAR time series to estimate rice production in the Mekong Delta, Vietnam, for three rice seasons centered on the year 2015. Rice production for each growing season was estimated by first classifying paddy rice area using superpixel segmentation and a phenology based decision tree, followed by yield estimation using random forest regression models trained on in situ yield data collected by surveying 357 rice farms. The estimated rice production for the three rice growing seasons 2015 correlates well with data at the district level collected from the province statistics offices with R2s of 0.93 for the Winter–Spring, 0.86 for the Summer–Autumn and 0.87 for the Autumn–Winter season

Rice crop detection using LSTM, Bi-LSTM, and machine learning models from sentinel-1 time series

The Synthetic Aperture Radar (SAR) time series allows describing the rice phenological cycle by the backscattering time signature. Therefore, the advent of the Copernicus Sentinel-1 program expands studies of radar data (C-band) for rice monitoring at regional scales, due to the high temporal resolution and free data distribution. Recurrent Neural Network (RNN) model has reached state-of-the-art in the pattern recognition of time-sequenced data, obtaining a significant advantage at crop classification on the remote sensing images. One of the most used approaches in the RNN model is the Long Short-Term Memory (LSTM) model and its improvements, such as Bidirectional LSTM (Bi-LSTM). Bi-LSTM models are more effective as their output depends on the previous and the next segment, in contrast to the unidirectional LSTM models. The present research aims to map rice crops from Sentinel-1 time series (band C) using LSTM and Bi-LSTM models in West Rio Grande do Sul (Brazil). We compared the results with traditional Machine Learning techniques: Support Vector Machines (SVM), Random Forest (RF), k-Nearest Neighbors (k-NN), and Normal Bayes (NB). The developed methodology can be subdivided into the following steps: (a) acquisition of the Sentinel time series over two years; (b) data pre-processing and minimizing noise from 3D spatial-temporal filters and smoothing with Savitzky-Golay filter; (c) time series classification procedures; (d) accuracy analysis and comparison among the methods. The results show high overall accuracy and Kappa (>97% for all methods and metrics). Bi-LSTM was the best model, presenting statistical differences in the McNemar test with a significance of 0.05. However, LSTM and Traditional Machine Learning models also achieved high accuracy values. The study establishes an adequate methodology for mapping the rice crops in West Rio Grande do Sul

CHARACTERIZING RICE RESIDUE BURNING AND ASSOCIATED EMISSIONS IN VIETNAM USING A REMOTE SENSING AND FIELD-BASED APPROACH

Agricultural residue burning, practiced in croplands throughout the world, adversely impacts public health and regional air quality. Monitoring and quantifying agricultural residue burning with remote sensing alone is difficult due to lack of field data, hazy conditions obstructing satellite remote sensing imagery, small field sizes, and active field management. This dissertation highlights the uncertainties, discrepancies, and underestimation of agricultural residue burning emissions in a small-holder agriculturalist region, while also developing methods for improved bottom-up quantification of residue burning and associated emissions impacts, by employing a field and remote sensing-based approach. The underestimation in biomass burning emissions from rice residue, the fibrous plant material left in the field after harvest and subjected to burning, represents the starting point for this research, which is conducted in a small-holder agricultural landscape of Vietnam. This dissertation quantifies improved bottom-up air pollution emissions estimates through refinements to each component of the fine-particulate matter emissions equation, including the use of synthetic aperture radar timeseries to explore rice land area variation between different datasets and for date of burn estimates, development of a new field method to estimate both rice straw and stubble biomass, and also improvements to emissions quantification through the use of burning practice specific emission factors and combustion factors. Moreover, the relative contribution of residue burning emissions to combustion sources was quantified, demonstrating emissions are higher than previously estimated, increasing the importance for mitigation. The dissertation further explored air pollution impacts from rice residue burning in Hanoi, Vietnam through trajectory modelling and synoptic meteorology patterns, as well as timeseries of satellite air pollution and reanalysis datasets. The results highlight the inherent difficulty to capture air pollution impacts in the region, especially attributed to cloud cover obstructing optical satellite observations of episodic biomass burning. Overall, this dissertation found that a prominent satellite-based emissions dataset vastly underestimates emissions from rice residue burning. Recommendations for future work highlight the importance for these datasets to account for crop and burning practice specific emission factors for improved emissions estimates, which are useful to more accurately highlight the importance of reducing emissions from residue burning to alleviate air quality issues

Télédétection radar appliquée au suivi des rizières. Méthodes utilisant le rapport des intensités de rétrodiffusion.

Because of the importance of rice in global food security and of the role of rice paddies in methane emissions, a large-scale near-real-time monitoring system of rice fields appears to be particularly useful. The objective of this work is to develop methods aiming at an effective use of remote sensing data from past and future satellites for rice fields monitoring. Radar imagery is preferred to optical imagery, due to its all-weather ability. Two methods are considered, both involving a C-band SAR intensity ratio as a classification feature: the HH/VV polarization ratio and the co-polarized temporal change HHdate2/HHdate1. First, a statistical study of intensity ratios is done, leading to the development of an error model that estimates the performance of the classification methods. The error model is also used to assess the impact of SAR system parameters (calibration, ambiguity ratio, revisit frequency) on the classification accuracy. Then, these classification methods are applied to two datasets acquired by the ASAR instrument onboard ENVISAT over the Mekong Delta, Vietnam, in order to map rice fields at two scales. The first approach relies on the use of the HH/VV polarization ratio calculated from the Alternating Polarization mode of ASAR, and is applied to produce a rice map covering one province in the delta. The second approach uses the HH temporal change of Wide-Swath mode images from ASAR, and allows mapping rice fields over the whole delta. Both methods are validated with success through the use of the cultivated areas reported in national statistics.En raison de l'importance du riz dans l'alimentation mondiale et du rôle des rizières dans les émissions de méthane, un suivi à grande échelle et en temps quasi-réel des surfaces cultivées en riz semble particulièrement utile. L'objectif de cette thèse est de développer des méthodes permettant une utilisation effective des données de télédétection des satellites présents et futurs pour le suivi des rizières. L'imagerie radar est privilégiée car elle permet des acquisitions sous toutes les conditions météorologiques, contrairement à l'imagerie optique. Deux méthodes sont retenues qui font intervenir un rapport d'intensité de deux images SAR en bande C : le rapport de polarisation HH/VV ou le changement temporel en co-polarisation HHdate2/HHdate1. Dans un premier temps, une étude statistique des rapports d'intensité de rétrodiffusion est effectuée, qui conduit au développement d'un modèle d'erreur permettant d'estimer la performance des méthodes de classification. Ce modèle d'erreur est également utilisé pour évaluer l'impact des paramètres des systèmes SAR (Synthetic Aperture Radar) sur la performance de la classification. Il s'agit des paramètres concernant l'étalonnage, l'ambiguïté, la fréquence de revisite. Dans un second temps, les méthodes de classification ainsi développées sont appliquées à deux jeux de données de l'instrument ASAR du satellite ENVISAT sur le delta du Mékong au Vietnam, pour faire la cartographie des rizières à deux échelles différentes. La première méthode repose sur l'utilisation du rapport HH/VV à partir de données du mode Alternating Polarization d'ASAR, qui permet de produire une carte de rizières couvrant une province du delta. La seconde méthode tire parti du changement temporel de HH sur des images du mode Wide-Swath d'ASAR, et est utilisée pour cartographier les rizières de l'ensemble du delta. Les deux méthodes sont validées avec succès en utilisant les surfaces cultivées données par les statistiques nationales

Land cover mapping of the Mekong Delta with sentinel-1 synthetic aperture radar

Synthetic aperture radar (SAR) has great potential for land cover/land use (LCLU) mapping, especially in tropical regions, where frequent cloud cover obstructs optical remote sensing. The use of SAR data derived mapping results plays crucial role in urban and suburban extents characterizations, urban services, rice crop distribution delineation, and land use changes detection. As the Mekong Delta is a significant location ecologically, economically, and socially, food security, forest conservation, natural resource management, and urbanization are a matter of great concern. Urban expansion and conversion wetland areas to aquaculture have impacts on natural forest and coastal ecosystems in the Mekong Delta. Therefore, the use of latest Sentinel-1 C-band SAR data characterizing LCLU including urban expansion, aquaculture development, and productive land and unproductive lands is essential for natural resource management and land use planning. This thesis demonstrated the use of Sentinel-1 SAR data and Google Earth Engine to map the LCLU of the Mekong Delta. The research in this thesis is divided into three parts: 1) the classification of multi-temporal Sentinel-1A C-band SAR imagery for characterizing the LCLU to support natural resource management; 2) identifying and mapping persistent building structures from coastal plains to high plateaus, as well as on the sea surface; 3) detecting and mapping persistent surface water and seasonal inundated LCLU. Part 1 of the thesis investigated the classification of multi-temporal Sentinel-1A C-band SAR imagery for characterizing LCLU to support natural resource management for land use planning and monitoring. Twenty-one SAR images acquired in 2016 over Bạc Liêu province, a rapidly developing province of the Mekong Delta, Vietnam were classified. To reduce the effects of rainfall variation confounding the classification, the images were divided into two categories: dry season (Jan–April) and wet season (May–December) and three input image sets were produced: 1) a single-date composite image, 2) a multi-temporal composite image and 3) a multi-temporal and textural composite image. Support Vector Machines (SVM) and Random Forest (RF) classifiers were then applied to characterize urban, forest, aquaculture, and rice paddy field for the three input image sets. A combination of input images and classification algorithms was tested, and the mapping results showed that no matter the classification algorithms used, multi-temporal images had a higher overall classification accuracy than single-date images and that differences between classification algorithms were minimal. The results demonstrated the potential use of SAR as an up-to date complementary data source of land cover information for local authorities, to support their land use master plan and to monitor illegal land use changes. Part 2 of the thesis developed novel and robust methods using time-series data acquired from Sentinel-1 C-band SAR to identify and map persistent building structures from coastal plains to high plateaus, as well as on the sea surface. Mapping building structures is crucial for environmental change and impact assessment and is especially important to accurately estimate fossil fuel CO2 emissions from human settlements. From annual composites of SAR data in the two-dimensional VV-VH polarization space, the VV-VH domain was determined for detecting building structures, whose persistence was defined based on the number of times that a pixel was identified as a building in time-series data. Moreover, the algorithm accounted for misclassified buildings due to water-tree interactions in radar signatures and due to topography effects in complex mountainous landforms. The methods were tested in five cities (Bạc Liêu, Cà Mau, Sóc Trăng, Tân An, and Phan Thiết) in Vietnam located in different socio-environmental regions with a range of urban configurations. Using in-situ data and field observations, the methods were validated, and the results were found to be accurate, with an average false negative rate of 10.9% and average false positive rate of 6.4% for building detection. The new approach was developed to be robust against variations in SAR incidence and azimuth angles. The results demonstrated the potential use of satellite dual-polarization SAR to identify persistent building structures annually across rural–urban landscapes and on sea surfaces with different environmental conditions. The final part of the thesis developed a novel method to map persistent surface water and seasonal inundated land cover and land use. The super-intensive shrimp culture in the Mekong Delta region brings substantial profits to the local economy but it poses major challenges to soil and surface water in wetland areas. The use of geospatial data in monitoring the aquaculture areas is necessary but it has been inadequate in aquaculture areas in the Mekong Delta. In this study, a new algorithm was developed to address the problem of detecting LCLU that contains water such as persistent surface water (permanent lake, permanent rivers, persistently denuded unproductive land) and seasonal inundated land cover (rice paddy and aquaculture) in different environmental conditions. The three-dimensional (3-D) space of VV-VH polarization of the SAR data and Season space was introduced. This study found that the use of the three-dimensional polarization of the SAR and season space is successfully in detecting rice paddy, aquaculture, and persistent surface water. Therefore, the novel method can be utilized to monitor aquaculture in other wetland regions. In conclusion, this thesis demonstrated the potential use of Sentinel-1 C-band SAR data to map LCLU across the urban suburban to rural-natural landscape on level terrains. The proposed methods can be used for urbanization monitoring, aquaculture development monitoring, and illegal land use change

Land cover mapping of the Mekong Delta with sentinel-1 synthetic aperture radar

Synthetic aperture radar (SAR) has great potential for land cover/land use (LCLU) mapping, especially in tropical regions, where frequent cloud cover obstructs optical remote sensing. The use of SAR data derived mapping results plays crucial role in urban and suburban extents characterizations, urban services, rice crop distribution delineation, and land use changes detection. As the Mekong Delta is a significant location ecologically, economically, and socially, food security, forest conservation, natural resource management, and urbanization are a matter of great concern. Urban expansion and conversion wetland areas to aquaculture have impacts on natural forest and coastal ecosystems in the Mekong Delta. Therefore, the use of latest Sentinel-1 C-band SAR data characterizing LCLU including urban expansion, aquaculture development, and productive land and unproductive lands is essential for natural resource management and land use planning. This thesis demonstrated the use of Sentinel-1 SAR data and Google Earth Engine to map the LCLU of the Mekong Delta. The research in this thesis is divided into three parts: 1) the classification of multi-temporal Sentinel-1A C-band SAR imagery for characterizing the LCLU to support natural resource management; 2) identifying and mapping persistent building structures from coastal plains to high plateaus, as well as on the sea surface; 3) detecting and mapping persistent surface water and seasonal inundated LCLU. Part 1 of the thesis investigated the classification of multi-temporal Sentinel-1A C-band SAR imagery for characterizing LCLU to support natural resource management for land use planning and monitoring. Twenty-one SAR images acquired in 2016 over Bạc Liêu province, a rapidly developing province of the Mekong Delta, Vietnam were classified. To reduce the effects of rainfall variation confounding the classification, the images were divided into two categories: dry season (Jan–April) and wet season (May–December) and three input image sets were produced: 1) a single-date composite image, 2) a multi-temporal composite image and 3) a multi-temporal and textural composite image. Support Vector Machines (SVM) and Random Forest (RF) classifiers were then applied to characterize urban, forest, aquaculture, and rice paddy field for the three input image sets. A combination of input images and classification algorithms was tested, and the mapping results showed that no matter the classification algorithms used, multi-temporal images had a higher overall classification accuracy than single-date images and that differences between classification algorithms were minimal. The results demonstrated the potential use of SAR as an up-to date complementary data source of land cover information for local authorities, to support their land use master plan and to monitor illegal land use changes. Part 2 of the thesis developed novel and robust methods using time-series data acquired from Sentinel-1 C-band SAR to identify and map persistent building structures from coastal plains to high plateaus, as well as on the sea surface. Mapping building structures is crucial for environmental change and impact assessment and is especially important to accurately estimate fossil fuel CO2 emissions from human settlements. From annual composites of SAR data in the two-dimensional VV-VH polarization space, the VV-VH domain was determined for detecting building structures, whose persistence was defined based on the number of times that a pixel was identified as a building in time-series data. Moreover, the algorithm accounted for misclassified buildings due to water-tree interactions in radar signatures and due to topography effects in complex mountainous landforms. The methods were tested in five cities (Bạc Liêu, Cà Mau, Sóc Trăng, Tân An, and Phan Thiết) in Vietnam located in different socio-environmental regions with a range of urban configurations. Using in-situ data and field observations, the methods were validated, and the results were found to be accurate, with an average false negative rate of 10.9% and average false positive rate of 6.4% for building detection. The new approach was developed to be robust against variations in SAR incidence and azimuth angles. The results demonstrated the potential use of satellite dual-polarization SAR to identify persistent building structures annually across rural–urban landscapes and on sea surfaces with different environmental conditions. The final part of the thesis developed a novel method to map persistent surface water and seasonal inundated land cover and land use. The super-intensive shrimp culture in the Mekong Delta region brings substantial profits to the local economy but it poses major challenges to soil and surface water in wetland areas. The use of geospatial data in monitoring the aquaculture areas is necessary but it has been inadequate in aquaculture areas in the Mekong Delta. In this study, a new algorithm was developed to address the problem of detecting LCLU that contains water such as persistent surface water (permanent lake, permanent rivers, persistently denuded unproductive land) and seasonal inundated land cover (rice paddy and aquaculture) in different environmental conditions. The three-dimensional (3-D) space of VV-VH polarization of the SAR data and Season space was introduced. This study found that the use of the three-dimensional polarization of the SAR and season space is successfully in detecting rice paddy, aquaculture, and persistent surface water. Therefore, the novel method can be utilized to monitor aquaculture in other wetland regions. In conclusion, this thesis demonstrated the potential use of Sentinel-1 C-band SAR data to map LCLU across the urban suburban to rural-natural landscape on level terrains. The proposed methods can be used for urbanization monitoring, aquaculture development monitoring, and illegal land use change

Mapping of multitemporal rice (Oryza sativa L.) growth stages using remote sensing with multi-sensor and machine learning : a thesis dissertation presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Earth Science at Massey University, Manawatū, New Zealand

Figure 2.1 is adapted and re-used under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.Rice (Oryza Sativa) plays a pivotal role in food security for Asian countries, especially in Indonesia. Due to the increasing pressure of environmental changes, such as land use and climate, rice cultivation areas need to be monitored regularly and spatially to ensure sustainable rice production. Moreover, timely information of rice growth stages (RGS) can lead to more efficient of inputs distribution from water, seed, fertilizer, and pesticide. One of the efficient solutions for regularly mapping the rice crop is using Earth observation satellites. Moreover, the increasing availability of open access satellite images such as Landsat-8, Sentinel-1, and Sentinel-2 provides ample opportunities to map continuous and high-resolution rice growth stages with greater accuracy. The majority of the literature has focused on mapping rice area, cropping patterns and relied mainly on the phenology of vegetation. However, the mapping process of RGS was difficult to assess the accuracy, time-consuming, and depended on only one sensor. In this work, we discuss the use of machine learning algorithms (MLA) for mapping paddy RGS with multiple remote sensing data in near-real-time. The study area was Java Island, which is the primary rice producer in Indonesia. This study has investigated: (1) the mapping of RGS using Landsat-8 imagery and different MLAs, and their rigorous performance was evaluated by conducting a multitemporal analysis; (2) the temporal consistency of predicting RGS using Sentinel-2, MOD13Q1, and Sentinel-1 data; (3) evaluating the correlation of local statistics data and paddy RGS using Sentinel-2, PROBA-V, and Sentinel-1 with MLAs. The ground truth datasets were collected from multi-year web camera data (2014-2016) and three months of the field campaign in different regions of Java (2018). The study considered the RGS in the analysis to be vegetative, reproductive, ripening, bare land, and flooding, and MLAs such as support vector machines (SVMs), random forest (RF), and artificial neural network (ANN) were used. The temporal consistency matrix was used to compare the classification maps within three sensor datasets (Landsat-8 OLI, Sentinel-2, and Sentinel-2, MOD13Q1, Sentinel-1) and in four periods (5, 10, 15, 16 days). Moreover, the result of the RGS map was also compared with monthly data from local statistics within each sub-district using cross-correlation analysis. The result from the analysis shows that SVM with a radial base function outperformed the RF and ANN and proved to be a robust method for small-size datasets (< 1,000 points). Compared to Sentinel-2, Landsat-8 OLI gives less accuracy due to the lack of a red-edge band and larger pixel size (30 x 30 m). Integration of Sentinel-2, MOD13Q1, and Sentinel-1 improved the classification performance and increased the temporal availability of cloud-free maps. The integration of PROBA-V and Sentinel-1 improved the classification accuracy from the Landsat-8 result, consistent with the monthly rice planting area statistics at the sub-district level. The western area of Java has the highest accuracy and consistency since the cropping pattern only relied on rice cultivation. In contrast, less accuracy was noticed in the eastern area because of upland rice cultivation due to limited irrigation facilities and mixed cropping. In addition, the cultivation of shallots to the north of Nganjuk Regency interferes with the model predictions because the cultivation of shallots resembles the vegetative phase due to the water banks. One future research idea is the auto-detection of the cropping index in the complex landscape to be able to use it for mapping RGS on a global scale. Detection of the rice area and RGS using Google Earth Engine (GEE) can be an action plan to disseminate the information quickly on a planetary scale. Our results show that the multitemporal Sentinel-1 combined with RF can detect rice areas with high accuracy (>91%). Similarly, accurate RGS maps can be detected by integrating multiple remote sensing (Sentinel-2, Landsat-8 OLI, and MOD13Q1) data with acceptable accuracy (76.4%), with high temporal frequency and lower cloud interference (every 16 days). Overall, this study shows that remote sensing combined with the machine learning methodology can deliver information on RGS in a timely fashion, which is easy to scale up and consistent both in time and space and matches the local statistics. This thesis is also in line with the existing rice monitoring projects such as Crop Monitor, Crop Watch, AMIS, and Sen4Agri to support disseminating information over a large area. To sum up, the proposed workflow and detailed map provide a more accurate method and information in near real-time for stakeholders, such as governmental agencies against the existing mapping method. This method can be introduced to provide accurate information to rice farmers promptly with sufficient inputs such as irrigation, seeds, and fertilisers for ensuring national food security from the shifting planting time due to climate change

Radarsat backscattering from an agricultural scene

A faixa espectral das microondas tem uma importância para o sensoriamento remoto agrícola, por ser uma faixa em que se tem uma quase certeza de disponibilidade de imagens de satélite, independentemente das condições atmosféricas. Entretanto, embora haja satélites em operação na faixa do radar, o aproveitamento desses dados ainda não é plenamente satisfatório, principalmente em virtude da falta de entendimento das interações que ocorrem entre o radar e os alvos agrícolas. Neste trabalho, são utilizadas três passagens do Radarsat para agrupar os valores de retroespalhamento (so) representativos das diversas condições dos talhões agrícolas em uma região intensamente cultivada. Num diagrama de dispersão dos valores de so, pôde-se verificar a existência de três grandes regiões: uma, caracterizada por baixos valores, e constituída por solos expostos; a segunda, com valores intermediários e constituída por culturas bem desenvolvidas; e uma terceira, com altos valores de retroespalhamento, constituída por superfícies muito rugosas, particularmente quando os sulcos de plantio são perpendiculares à direção de visada do satélite. Os resultados deste trabalho indicam que o uso de imagens Radarsat para agricultura é mais otimizado quando se faz uma análise multitemporal, aproveitando o calendário agrícola e a dinâmica das diferentes culturas.Orbital remote sensing in the microwave electromagnetic region has been presented as an important tool for agriculture monitoring. The satellite systems in operation have almost all-weather capability and high spatial resolution, which are features appropriated for agriculture. However, for full exploration of these data, an understanding of the relationships between the characteristics of each system and agricultural targets is necessary. This paper describes the behavior of backscattering coefficient (so) derived from calibrated data of Radarsat images from an agricultural area. It is shown that in a dispersion diagram of so there are three main regions in which most of the fields can be classified. The first one is characterized by low backscattering values, with pastures and bare soils; the second one has intermediate backscattering coefficients and comprises well grown crops mainly; and a third one, with high backscattering coefficients, in which there are fields with strong structures causing a kind of double bounce effect. The results of this research indicate that the use of Radarsat images is optimized when a multitemporal analysis is done making the best use of the agricultural calendar and of the dynamics of different cultures