Application potentials of synthetic aperture radar interferometry for land-cover mapping and crop-height estimation

Synthetic aperture radar (SAR) interferometry is widely used for applications like digital elevation map generation and studies related to surface movement. However, SAR interferometry can also be exploited in many other areas. Here a few of the potential applications of SAR interferometry have been demonstrated by exploring its use in delineation and density mapping of forested areas, delineation of surface water extent under adverse weather conditions, which is useful during flood-mapping; detection of human settlement and crop-height estimation. This has been achieved by exploiting interferometric coherence, which is inversely related to the magnitude of random dislocation of scatterers between the two passes. The study indicated that interferometric coherence decreases with increase in forest density or increase in crop height. It was also observed that interferometric coherence over stable targets like settlement is quite high compared to other land-cover classes. In contrast, interferometric coherence is always low for unstable surfaces like the water surface. The study suggested that interferometric coherence is a parameter that provides valuable information, which is completely different from that of SAR backscatter. It was also observed that synergic use of SAR backscatter with InSAR coherence enhances the application potential of a SAR system as a whole towards many land-cover features

How far SAR has fulfilled its expectation for soil moisture retrieval

Microwave remote sensing is one of the most promising tools for soil moisture estimation owing to its high sensitivity to dielectric properties of the target. Many ground-based scatterometer experiments were carried out for exploring this potential. After the launch of ERS-1, expectation was generated to operationally retrieve large area soil moisture information. However, along with its strong sensitivity to soil moisture, SAR is also sensitive to other parameters like surface roughness, crop cover and soil texture. Single channel SAR was found to be inadequate to resolve the effects of these parameters. Low and high incidence angle RADARSAT-1 SAR was exploited for resolving these effects and incorporating the effects of surface roughness and crop cover in the soil moisture retrieval models. Since the moisture and roughness should remain unchanged between low and high angle SAR acquisition, the gap period between the two acquisitions should be minimum. However, for RADARSAT-1 the gap is typically of the order of 3 days. To overcome this difficulty, simultaneously acquired ENVISAT-1 ASAR HH/VV and VV/VH data was studied for operational soil moisture estimation. Cross-polarised SAR data has been exploited for its sensitivity to vegetation for crop-covered fields where as co-pol ratio has been used to incorporate surface roughness for the case of bare soil. Although there has not been any multi-frequency SAR system onboard a satellite platform, efforts have also been made to understand soil moisture sensitivity and penetration capability at different frequencies using SIR-C/X-SAR and multi-parametric Airborne SAR data. This paper describes multi-incidence angle, multi-polarised and multi-frequency SAR approaches for soil moisture retrieval over large agricultural area

Advances in Radar Remote Sensing of Agricultural Crops: A Review

There are enormous advantages of a review article in the field of emerging technology like radar remote sensing applications in agriculture. This paper aims to report select recent advancements in the field of Synthetic Aperture Radar (SAR) remote sensing of crops. In order to make the paper comprehensive and more meaningful for the readers, an attempt has also been made to include discussion on various technologies of SAR sensors used for remote sensing of agricultural crops viz. basic SAR sensor, SAR interferometry (InSAR), SAR polarimetry (PolSAR) and polarimetric interferometry SAR (PolInSAR). The paper covers all the methodologies used for various agricultural applications like empirically based models, machine learning based models and radiative transfer theorem based models. A thorough literature review of more than 100 research papers indicates that SAR polarimetry can be used effectively for crop inventory and biophysical parameters estimation such are leaf area index, plant water content, and biomass but shown less sensitivity towards plant height as compared to SAR interferometry. Polarimetric SAR Interferometry is preferable for taking advantage of both SAR polarimetry and SAR interferometry. Numerous studies based upon multi-parametric SAR indicate that optimum selection of SAR sensor parameters enhances SAR sensitivity as a whole for various agricultural applications. It has been observed that researchers are widely using three models such are empirical, machine learning and radiative transfer theorem based models. Machine learning based models are identified as a better approach for crop monitoring using radar remote sensing data. It is expected that the review article will not only generate interest amongst the readers to explore and exploit radar remote sensing for various agricultural applications but also provide a ready reference to the researchers working in this field

Multi-frequency and multi-polarized SAR response to thin vegetation and scattered trees

This communication highlights the results of a study carried out to understand the Synthetic Aperture Radar (SAR) response to thin vegetation volume at L, C and X bands as well as cross-polarizations at L and C bands, and the X band at VV polarization. The sensitivity of SAR backscatter to the vegetation volume varies with the frequency, polarization and incidence angle at which the canopy is illuminated. Multifrequency, multi-polarized SAR response of thin linear vegetation along the roadside, small thorny hedges along the boundary of the farmers' fields and scattered cluster of trees was studied for this purpose. It was observed that cross-polarized signals were able to pick up signals better from a very thin vegetation volume among the polarization responses and the L band was the most sensitive among the frequencies

Effects of nitrogen dioxide on gas exchange in phaseolus vulgaris leaves

The present investigation was undertaken to survey the general features of physiological responses of plants to NO2, and to understand the mechanism of inhibition of gas exchange by NO2. To achieve these objectives, the effects of NO2, on photosynthesis, respiration and transpiration and the rate of NO2, uptake by primary bean (Phaseolus vulgaris. L. cv. 'Pure Gold wax'), leaves were examined in various environmental conditions using an open gas flow system. Apparent photosynthesis, respiration and the evolution of CO2, into CO2- free air, were all inhibited by NO2, concentrations between 1.0 and 7.0 ppm. The degree of inhibition was increased by increasing NO2, concentration and exposure time. A 2 and 5 h exposure to 3.0 ppm NO2, inhibited the gas exchange of bean leaves at all plant ages and in all the environmental conditions examined. Photosynthesis was most inhibited in leaves of intermediate ages, at optimum temperatures, at high light intensities, at relative humidities between 45 and 80% and in leaves of plants grown without any external source of nitrogen. The inhibition was rather less affected by changing C02 or 02 concentration. Maximum inhibition of respiration was observed in the youngest leaves, at high temperatures and in the leaves of nitrogen deficient plants. In most cases, the maximum inhibition of C02 exchange coincided with the maximum control rate in the absence of NO2. The inhibition of transpiration by NO2, was generally small and in a few cases either there was no effect of NO2, on transpiration or it was slightly increased by NO2. This indicated that the primary effects of NO2, were within the leaf mesophyll and not on the stomata. The uptake of N02 was also modified by plant age and environmental conditions. The rate of NO2 uptake increased with increasing concentrations of N02 and decreased with increasing exposure time. It was highest in the leaves of intermediate ages, in the light, at higher temperatures, at low and O2 concentrations, and in nitrogen deficient plants. In most cases, the maximum rate of N02 uptake was correlated with the maximum inhibition of gas exchange, but in several cases, it was not. Although stomatal resistance influenced the rate of N02 uptake to some extent, mesophyll resistance to NO2 was mainly responsible for the regulation of its absorption. In addition to Phaseolus vulgaris L.,10 other angiosperm species were examined. All species absorbed substantial amounts of N02 from an atmosphere of 3.0 ppm NO2, and all experienced a concomitant inhibition of photosynthesis. The rates of N02 uptake and the degree of inhibition varied according to species. The average rate of N02 uptake after a 2 h exposure to 3.0 ppm N02 -2 -1 was 0.391 mg N02 dm h and the average inhibition of photosynthesis with the same dose of N02 was 14.3%. An estimation of N02 uptake on a worldwide basis indicated that a concentration of 0.1 ppm N02 in the world's atmosphere could provide as much as 11% of the total nitrogen requirement of the terrestrial plants. Furthermore, the experiments reveal that the effect of N02 on plant metabolism is not restricted to a particular pathway or process; rather it is generalized. It appears that N02 may inhibit gas exchange by disrupting the structure of cell organelles and/or by interfering with the activities of enzymes.Land and Food Systems, Faculty ofGraduat

A novel approach to use semantic segmentation based deep learning networks to classify multi-temporal SAR data

The use of SAR data for land cover mapping provides many advantages over land cover classification achieved using optical remote sensing data. However, the classification of SAR data has always been a challenging task. In this study, the feasibility of the use of semantic segmentation based deep learning networks to classify temporal SAR data has been demonstrated. It has been achieved by applying six deep learning architectures viz. Pyramid Scene Parsing, UNET, DeepLabv3+, Path Aggregation Network, Encoder-Decoder Network and Feature Pyramid Network over temporally acquired SAR datasets for three different frequencies (triannual, quarterly and bimonthly). Outputs of all six architectures have been assessed using frequency weighted IoU. It was observed that Pyramid Scene Parsing architecture when applied on bimonthly temporal SAR provides the best results

Crop Height Estimation Using RISAT-1 Hybrid-Polarized Synthetic Aperture Radar Data

The objective of this paper was to explore the potential of hybrid-polarized (RH and RV) RISAT-1 SAR data to retrieve the height of wheat crop-an important winter crop in South Asian countries including India. The images acquired over north-west India in 2015 covered critical growth stages of wheat. The field campaigns were carried out in synchronous with the SAR passes. Considering the dominant role of underlying soil cover in the total backscatter (σ total 0 ) response from a target, we propose that refining the σ total 0 by reducing the effect of underlying soil can significantly improve the retrieval accuracy of crop height (CH). To achieve this, we modified the existing water cloud model (WCM) to estimate soil-corrected vegetation backscatter (σ veg 0 ). Leaf area index and interaction factor showed great potential as the vegetation descriptors in modeling σ total 0 using WCM. A comparative analysis between the CH retrieved from σ total 0 and σ veg 0 using multilayer perceptron neural networks revealed the response of C-band backscatter to CH. CH was moderately correlated to σ total 0 , but the results improved considerably with the substitution of σ total 0 with σ veg 0 . This holds true particularly in the early growth stages of crop growth when the vegetation cover is scarce and there is a substantial effect of soil background on the remote sensing signal. Thus, the results suggest suitability of C-band hybrid-polarized data for the assessment of CH

Wheat crop biophysical parameters retrieval using hybrid-polarized RISAT-1 SAR data

The main goal of this study was to assess the potential of SAR backscatter signatures (RH and RV) retrieved from hybrid-polarized RISAT-1 SAR data in providing relevant information about the wheat growth parameters (leaf area index or LAI, plant water content or PWC, plant volume or PV and wet biomass or WB) over the entire growing season. The study was carried out over the parts of Bharatpur and Mathura districts located in Rajasthan and Uttar Pradesh (India), respectively. The three-date time series hybrid-polarized dataset was collected coincident to which a comprehensive ground truth campaign was organised. We propose that refining the total backscatter (σtotal 0) values after minimising the effect of underlying/background soil cover, would result in more accurate retrieval of plant parameters since it is the vegetation backscatter, which ultimately has a direct correlation with the crop biophysical parameters. It was achieved using a semi-empirical water cloud model (WCM) based approach. The applicability of four different combinations of canopy descriptors, i.e. leaf area index (LAI), plant water content (PWC), leaf water area index (LWAI) and interaction factor (IF that takes into consideration the moisture distribution per unit volume) was tested on the RH and RV backscatter. We found that WCM based on LAI and IF as the two canopy descriptors modelled the total backscatter with a significantly high coefficient of determination (R2=0.90 and 0.85, respectively) and RMSE of 1.18 and 1.25 dB, respectively. Subsequently, this set was used to retrieve the soil-corrected vegetation backscatter (σveg 0) values. A comparative evaluation of the retrieval accuracy between plant parameters estimated from σtotal 0 (σT_RH o, σT_RV o) and σveg 0 (σV_RH o, σV_RV o) was performed using rigorously trained multi-layer perceptron (MLP) neural networks. The findings suggest that the prediction accuracy considerably improved when the backscatter of underlying/background soil cover was eliminated. The designed networks (with σtotal 0 as input) retrieved plant water content and plant volume with the highest accuracy of 0.82 and 0.80, respectively while it increased dramatically to 0.87 and 0.89 when the inputs were substituted by σveg 0. The present study is a first step towards retrieving crop parameters from hybridpolarized data and thus possesses the potential to serve as a reference for further research initiatives

Incorporating soil texture in soil moisture estimation from extended low-1 beam mode RADARSAT-1 SAR data

Sensitivity of microwaves towards soil moisture is well understood; still, development of a practical algorithm for soil moisture estimation using microwaves is difficult. This is due to the fact that along with their strong sensitivity to soil moisture, microwave signals are also sensitive to other target properties such as soil texture, surface roughness, and crop cover. In this paper, an attempt has been made to incorporate the effect of soil texture in large area soil moisture mapping using extended low-1 beam mode RADARSAT-1 SAR data in such a way that knowledge of soil texture is not a prerequisite

Wheat leaf area index retrieval using RISAT-1 hybrid polarized SAR data

Leaf Area Index (LAI) is a key parameter to characterize the canopy–atmosphere interface, where most of the energy fluxes exchange. Space-borne satellite images have shown their relevance for various applications including LAI retrieval over large areas. Although optical data have been used for this purpose in previous studies, the constraints to acquire optical data during extreme weather conditions due to the presence of clouds, haze, smoke etc. hinders its use for uninterrupted monitoring. This study aims to analyze the relationships of C-band RISAT-1 hybrid polarized SAR data (σ˚RH and σ˚RV) with wheat LAI. The results have shown the correlation coefficient (|r|) of 0.57 and 0.73 for RH and RV backscatter, respectively, using non-linear regression approach. It is also observed that the accuracy of LAI retrieval has been significantly improved with |r| and RMSE of 0.81 and 0.54 (m2/m2), respectively, by considering both RH and RV backscatter as inputs for support vector machine-based model