Mapping Rice Area in the Cauvery Delta Zone of Tamil Nadu Using Sentinel 1A Synthetic Aperture Radar (SAR) Data

Since remote sensing based crop inventory provides accurate and timely information as compared to the conventional survey methods of estimating area, Multi-temporal Sentinel 1A Synthetic Aperture Radar data was used for the estimation of rice area during Samba season 2022 in the Cauvery delta zone comprising Thanjavur, Thiruvaru, Mayiladuthurai, and Nagapatnam districts of Tamil Nadu. SAR data was preferred over optical satellite data due to excess cloud cover during cropping the major season in Tamil Nadu. Temporal back-scatter (dB) signature of rice crop was generated from the multi-temporal processed SAR data utilizing the modules of a fully automated MAPscape software aiding the discriminating of the crop from others. The signatures revealed that the dB levels to be the lowest during agronomic floods, reached the highest during maximum tillering stage and started declining thereafter. Multi-temporal feature extraction module of Mapscape was used to estimate rice area and validated for accuracy using ground truth data collected during survey. A total of 3.05 lakh ha of rice area was estimated with an overall accuracy of 90.8 % and 0.82 kappa coefficient. Largest area of 1.12 lakh ha was recorded in Thanjavur followed by Thiruvarur and Mayiladuthurai with 0.95 and 0.51 lakh ha respectively.

Estimation of Surface and Subsurface Soil Moisture Using Microwave Remote Sensing: A Typical Analysis

Accurate measurement and monitoring of surface and subsurface soil moisture is essential for understanding hydrological processes, crop growth modeling, crop water requirement, and climate studies. Accurate measurement of the soil moisture content (SMC) in the root zone is essential for precise irrigation authority and plant water stress evaluation. However, the existing passive microwave satellite missions, Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP), that operate at L-band, can only estimate the top 5 cm of soil moisture. Microwave remote sensing has proven to be a valuable tool for non-invasive soil moisture estimation. This research aims to investigate and develop a methodology for estimating surface and subsurface soil moisture using microwave data from Sentinel-1. The study was conducted to establish the relationship between surface & the backscatter coefficient derived using the Sentinel-1 SAR microwave remote sensing satellite imagery, and relationship between surface and subsurface soil moisture at different depths, in the Godhra region. Two seasons namely summer (Zaid) and monsoon (Kharif) were taken into consideration to build up the relationship between surface soil moisture and co-polarization backscatter coefficient ( For the summer (Zaid) and monsoon (Kharif) seasons, the co-polarization backscatter coefficient ( and surface soil moisture (0-5, cm) were found to have a correlation in terms of R2 as 0.91 and 0.90, respectively. The study explores the relationship between microwave signals and surface soil moisture content (0-5, cm) and then the relationship between surface soil moisture and soil moisture at various depths were also modeled thereby contributing to improved soil moisture estimation techniques and applications. The value of the coefficient of determination (R2) of surface soil moisture (0-5, cm) to subsurface soil moisture at 6-20 cm, 21-40 cm, and 41-60 cm depths were found to be 0.60, 0.51, and 0.46, respectively, in the summer (Zaid) season. The value of the coefficient of determination (R2) of surface soil moisture (0-5, cm) to subsurface soil moisture at 6-20 cm, 21-40 cm, 41-60 cm, 61-80 cm, and 81-100 cm depths were found to be 0.83, 0.61, 0.51, 0.26, and 0.13, respectively. According to the study, it is observed that the relationship between co-polarization backscatter coefficient ( and soil moisture weakens as the depth of soil moisture increases. Overall, the regression models developed between the co-polarization backscatter coefficient ( and surface soil moisture showed very good results, whereas the regression models developed between the surface soil moisture and soil moisture at various depths showed reasonably acceptable results up to the depth of 60 cm. The findings in the present study suggest that Sentinel-1A C-band SAR data can be used to estimate surface soil moisture. It is also shown in this study that the surface soil moisture can be correlated with the subsurface soil moisture up to the depth of 60 cm, satisfactorily using regression equations.

Land cover classification of Lago Grande de Curuai floodplain (Amazon, Brazil) using multi-sensor and image fusion techniques / Classificação da cobertura da terra na planície de inundação do Lago Grande de Curuai (Amazônia, Brasil) utilizando dados multisensor e fusão de imagens

Given the limitations of different types of remote sensing images, automated land-cover classifications of the Amazon várzea may yield poor accuracy indexes. One way to improve accuracy is through the combination of images from different sensors, by either image fusion or multi-sensor classifications. Therefore, the objective of this study was to determine which classification method is more efficient in improving land cover classification accuracies for the Amazon várzea and similar wetland environments - (a) synthetically fused optical and SAR images or (b) multi-sensor classification of paired SAR and optical images. Land cover classifications based on images from a single sensor (Landsat TM or Radarsat-2) are compared with multi-sensor and image fusion classifications. Object-based image analyses (OBIA) and the J.48 data-mining algorithm were used for automated classification, and classification accuracies were assessed using the kappa index of agreement and the recently proposed allocation and quantity disagreement measures. Overall, optical-based classifications had better accuracy than SAR-based classifications. Once both datasets were combined using the multi-sensor approach, there was a 2% decrease in allocation disagreement, as the method was able to overcome part of the limitations present in both images. Accuracy decreased when image fusion methods were used, however. We therefore concluded that the multi-sensor classification method is more appropriate for classifying land cover in the Amazon várzea.

Dadas as limitações de diferentes tipos de imagens de sensores remotos, classificações automáticas do uso e cobertura do solo na várzea Amazônica podem resultar em índices de acurácia insatisfatórios. Uma das maneiras de melhorar esses índices é através da combinação de dados de distintos sensores, por fusão de imagens ou através de classificações multi-sensores. Desta forma, o presente estudo teve o objetivo de determinar qual método de classificação é mais eficiente em melhorar os índices de acurácia das classificações do uso e cobertura do solo para a várzea Amazônica e áreas úmidas similares - (a) a fusão sintética de imagens SAR e ópticas ou (b) a classificação multi-sensor de imagens ópticas e SAR pareadas. Classificações da cobertura do solo com base em imagens de um único sensor (Landsat TM ou Radarsat-2) foram comparadas com as classificações multi-sensor e classificações baseadas em fusão de imagens. A análise de imagens baseada em objetos (OBIA) e o algoritmo de mineração de dados J.48 foram utilizados para realizar a classificação automática, cuja precisão foi avaliada com o índice kappa e com as medidas de discordância de alocação e de quantidade, recentemente propostas na literatura. Em geral, as classificações baseadas em imagens ópticas apresentaram melhor precisão do que as classificações baseadas em dados SAR. Uma vez que ambos os conjuntos de dados foram combinados em uma abordagem multi-sensores, houve uma redução de 2% no erro de alocação da classificação, uma vez que o método foi capaz de superar parte das limitações presentes em ambas as imagens. Contudo, a precisão diminuiu quando foram usados métodos de fusão de imagens. Concluiu-se que o método de classificação multi-sensor é mais apropriado para classificações de uso do solo na várzea amazônica.

Discriminação de fitofisionomias de floresta de várzea a partir do algoritmo Iterated Conditional Modes aplicado aos dados SAR/R99 (QUAD-POL/Banda L) / Discrimination among flooded forest phytophysiognomies from Iterated Conditional Modes algorithm applied to SAR Data R99 (QUAD POL/L-Band)

Utilizando-se dados do sensor aerotransportado SAR R99, adquiridos na banda L (1,28 GHz) em amplitude e com quatro polarizações (HH, VV, HV e VH), avaliou-se a distinção de fitofisionomias de floresta de várzea existentes nas Reservas de Desenvolvimento Sustentável Amanã e Mamirauá e áreas adjacentes, com a aplicação do algoritmo Iterated Conditional Modes (ICM) de classificação polarimétrica pontual/contextual. Os resultados mostraram que o uso das distribuições multivariadas em amplitude, conjuntamente com uma banda de textura, produziu classificações de qualidade superior àquelas obtidas com dados polarimétricos uni/bivariados. Esta abordagem permitiu a obtenção de um índice Kappa de 0,8963, discriminando as três classes vegetacionais de interesse, comprovando assim o potencial dos dados do SAR R99 e do algoritmo ICM no mapeamento de florestas de várzea da Amazônia.

This study seeks to evaluate the capability of data generated by the synthetic aperture radar SAR R99 sensor to map phytophysiognomies found in the Amanã and Mamirauá Sustainable Development Reserves (RDSA and RDSM). By means of L-band (1.28 GHz), full polarimetric (HH, VV, VH, HV), amplitude data acquired with the SAR R99 sensor, distinctions among flooded forest phytophysiognomies in the RDSA and RDSM and around were achieved. The Iterated Conditional Modes (ICM) algorithm was employed to perform the local/contextual polarimetric classification of the data. Results showed that the use of multivariate distributions in amplitude with a band of texture produced classifications of superior quality in relation to those obtained with the uni/bivariate polarimetric data. This approach allowed to obtain a Kappa index of 0,8963 and the distinction of three vegetation classes of interest, demonstrating the potential of SAR R99 and the ICM algorithm to map flooded vegetation of the Amazon.