Analysis of Temporal Backscattering of Cotton Crops Using a Semiempirical Model

Maity, Saroj; Patnaik, C.; Chakraborty, Manab; Panigrahy, Sushma

doi:10.1109/tgrs.2003.821888

Cited by 37 publications

(25 citation statements)

References 24 publications

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“…In some studies, the models have been shown to be quite accurate under sparsely vegetated soil surfaces [32], although the errors increase with growing vegetation cover. On the other hand, the semi-empirical water cloud model, devised by Attema & Ulaby [104], has been shown in various studies [105][106][107] to adequately represent the backscatter from a vegetation canopy as well as the underlying soil during the crop's phenological cycle. According to the model, the total backscatter at a co-polarised channel qq (σ°q q ), is the incoherent sum of the contribution from the vegetation (σ°v eg ) and the soil (σ°s oil ), and the two way attenuation of the vegetation layer (τ 2 ).…”

Section: Soil Moisture Retrieval Using Semi-empirical Scattering Modelsmentioning

confidence: 99%

Soil Moisture Retrieval from Active Spaceborne Microwave Observations: An Evaluation of Current Techniques

2009

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Abstract:The importance of land surface-atmosphere interactions, principally the effects of soil moisture, on hydrological, meteorological, and ecological processes has gained widespread recognition over recent decades. Its high spatial and temporal variability however, makes soil moisture a difficult parameter to measure and monitor effectively using traditional methods. Microwave remote sensing technology has demonstrated the potential to map and monitor relative soil moisture changes over large areas at regular intervals in time and also the opportunity of measuring, through inverse modelling, absolute soil moisture values. This ability has been demonstrated under a variety of topographic and land cover conditions using both active and passive microwave instruments. The purpose of this paper is to review the current status of soil moisture determination from active microwave remote sensing systems and to highlight the key areas of research that will have to be addressed to achieve routine use of the proposed retrieval approaches.

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Section: Soil Moisture Retrieval Using Semi-empirical Scattering Modelsmentioning

confidence: 99%

Soil Moisture Retrieval from Active Spaceborne Microwave Observations: An Evaluation of Current Techniques

2009

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“…Urban area and waterbody have very low values for this index as the concept of biomass does not relate to these features. Although numerous experiments have been carried out to investigate the response of microwave sensors to crop growth parameters [1,16,28,30], additional comprehensive studies for a variety of crops are needed to develop robust retrieval methods.…”

Section: Radar Vegetation Index (Rvi)mentioning

confidence: 99%

Cotton Crop Biophysical Parameter Study Using Hybrid/Compact Polarimetric Risat-1 Sar Data

Dave¹,

Haldar²,

Dave³

et al. 2017

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Abstract-A hybrid-polarity architecture, consisting of transmitting circular polarisation and receiving two orthogonal linear polarisation and also their relative phase, was used to calculate four Stokes parameters. Different parameters like Degree of Polarisation, Alpha angle, Entropy, Anisotropy, Radar vegetation Index and decompositions like Raney decomposition (m-δ), Freeman-2 and 3 component decompositions were derived from these hybrid data. Crop biophysical parameters viz. plant height, plant age and plant biomass of cotton crops grown under two different environments, i.e., rainfed and irrigated in Guajrat, India were studied with respect to derived polarimetric parameters. Right circular transmitted and horizontally (RH) and vertically (RV ) received backscatter values show good relation with the plant height, age and biomass. RH backscatter −13 dB to −7 dB and RV backscatter from −13 to −10 dB were observed for crop biophysical parameters. Volume component of all decomposition showed strong response to the increase in height, age and biomass of the plant. Radar Vegetation index (RVI) values have also shown significant increase from 0.6 to 0.7 with increasing age of the crop. The rate of growth was slow in the initial phase, but fast post mid-July for both early and late sown cases. The polarimetric parameters were found significantly correlated to the above plant biophysical parameters.

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“…-multi-temporal: is one of the most investigated forms of fusion in remote sensing due to the rich information content hidden in the temporal dimension. In particular, it can be applied to strictly time-related tasks, like prediction [13], change detection [27][28][29] and co-registration [30], and general-purpose tasks, like segmentation [7], despeckling [31] and feature extraction [32][33][34], which do not necessarily need a joint processing of the temporal sequence, but can benefit from it. -multi-sensor: is gaining an ever growing importance due both to the recent deployment of many new satellites and to the increasing tendency of the community to share data.…”

Section: Introductionmentioning

confidence: 99%

A CNN-Based Fusion Method for Feature Extraction from Sentinel Data

et al. 2018

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Sensitivity to weather conditions, and specially to clouds, is a severe limiting factor to the use of optical remote sensing for Earth monitoring applications. A possible alternative is to benefit from weather-insensitive synthetic aperture radar (SAR) images. In many real-world applications, critical decisions are made based on some informative optical or radar features related to items such as water, vegetation or soil. Under cloudy conditions, however, optical-based features are not available, and they are commonly reconstructed through linear interpolation between data available at temporally-close time instants. In this work, we propose to estimate missing optical features through data fusion and deep-learning. Several sources of information are taken into account-optical sequences, SAR sequences, digital elevation model-so as to exploit both temporal and cross-sensor dependencies. Based on these data and a tiny cloud-free fraction of the target image, a compact convolutional neural network (CNN) is trained to perform the desired estimation. To validate the proposed approach, we focus on the estimation of the normalized difference vegetation index (NDVI), using coupled Sentinel-1 and Sentinel-2 time-series acquired over an agricultural region of Burkina Faso from May-November 2016. Several fusion schemes are considered, causal and non-causal, single-sensor or joint-sensor, corresponding to different operating conditions. Experimental results are very promising, showing a significant gain over baseline methods according to all performance indicators.

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Analysis of Temporal Backscattering of Cotton Crops Using a Semiempirical Model

Cited by 37 publications

References 24 publications

Soil Moisture Retrieval from Active Spaceborne Microwave Observations: An Evaluation of Current Techniques

Soil Moisture Retrieval from Active Spaceborne Microwave Observations: An Evaluation of Current Techniques

Cotton Crop Biophysical Parameter Study Using Hybrid/Compact Polarimetric Risat-1 Sar Data

A CNN-Based Fusion Method for Feature Extraction from Sentinel Data

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