Assessing Performance of Tomo-SAR and Backscattering Coefficient for Hemi-Boreal Forest Aboveground Biomass Estimation

Tomo-SAR technique has been used for hemi-boreal forest height and further forest biomass estimation through allometric equation. Backscattering coefficient especially in longer wavelength (L- or P-band) is thought as a useful parameter for hemi-boreal forest biomass retrieval. The aim of this paper is to assess the performance of vertical backscattering power and backscattering coefficient for hemi-boreal forest aboveground biomass (AGB) estimation with airborne P-band data. The test site locates in southern Sweden called Remningstorp test site, and the in-situ forest AGB ranges from 14 t/ha to 245 t/ha at stand level. Multi-baseline P-band Pol-InSAR data in repeat-path mode collected during March and May in 2007 at Remningstorp test site was used. We found that the correlation coefficient (R) between backscattering coefficient of P-band HH polarization and the in-situ forest biomass reached 0.87. The R for P-band VV backscattering power at 5 m is 0.71 and 10 m is 0.72. Backscattering coefficient in HH polarization and vertical backscattering power at 5 m and 10 m were applied to construct a model for hemi-boreal forest AGB estimation by backward step-wise regression and cross-validation approach. The results showed that the estimated forest AGB ranges from 19 to 240 t/ha, and the constructed model obtained a higher R and smaller RMSE, the value of R is 0.91, RMSE is 30.43 t/ha at Remningstorp test site.     

Vegetation water content retrieval using scatterometer data at X-band

The vegetation water content (VWC) of kidney bean crop is retrieved using ground-based multi-temporal, multi-angular and co-polarized scatterometer data at X-band. An outdoor crop-bed was prepared to observe the scatterometer response at HH- and VV-polarizations in the angular range from 20° to 70°. The trend of scattering coefficient is found to decrease for the entire angular range. The present study is carried out to investigate the retrieval for VWC of a kidney bean crop at its nine different growth stages by an empirical relation based on least square optimization method using scatterometer data. The results are found promising for the retrieval of the VWC of kidney bean crop at its several growth stages.     

Soil Moisture Estimation in a Vegetated Area Using Combination of AIRSAR and Landsat5-TM Images

High difference between dielectric constant of water (dielectric constant about 80) and dielectric constant of dried soil (dielectric constant about 2–3) makes Synthetic Aperture Radar (SAR) highly capable in soil moisture estimation. However, there are other factors which affect on radar backscattering coefficient. The most important parameters are vegetation cover, surface roughness and sensor parameters (frequency, polarization and incidence angle). In this paper, the importance of considering the effects of these parameters on SAR backscatter coefficients is shown by comparing different soil moisture estimation models. Moreover, an experimental soil moisture estimation model is developed. It is shown that this model can be used to estimate soil moisture under a variety of vegetation cover densities. The new developed model is based on combination of different indices derived from Landsat5-Thematic Mapper and AIRSAR images. The AIRSAR image is used for extraction of backscattering coefficient and incidence angle while TM image is used for calculation of Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Normalized Difference Water Index (NDWI) and Brightness Temperature. Then a soil moisture estimation model which is named as Hybrid model is developed based on integration of all of these parameters. The accuracies of this model are assessed in the NDVI ranges of 0–0.2, 0.2–0.4 and 0.4–0.7 by using SAR data in C band and L band frequencies and also in different polarizations of HH, HV, VV and TP. The results show that for instance in L band with HV polarization, R-square values of 0.728, 0.628 and 0.527 are obtained between ground measured soil moisture and estimated soil moisture values using the Hybrid model for NDVI ranges of 0–0.2, 0.2–0.4 and 0.4–0.7, respectively.     

Surface roughness signatures of summer arctic snow-covered sea ice in X-band dual-polarimetric SAR

ABSTRACT

Surface roughness of sea ice is primary information for understanding sea ice dynamics and air–ice–ocean interactions. Synthetic aperture radar (SAR) is a powerful tool for investigating sea ice surface roughness owing to the high sensitivity of its signal to surface structures. In this study, we explored the surface roughness signatures of the summer Arctic snow-covered first-year sea ice in X-band dual-polarimetric SAR in terms of the root mean square (RMS) height. Two ice campaigns were conducted for the first-year sea ice with dry snow cover in the marginal ice zone of the Chukchi Sea in August 2017 and August 2018, from which high-resolution (4 cm) digital surface models (DSMs) of the sea ice were derived with the help of a terrestrial laser scanner to obtain the in situ RMS height. X-band dual-polarimetric (HH and VV) SAR data (3 m spatial resolution) were obtained for the 2017 campaign, at a high incidence angle (49.5°) of TerraSAR-X, and for the 2018 campaign, at a mid-incidence angle (36.1°) of TanDEM-X 1–2 days after the acquisition of the DSMs. The sea ice drifted during the time between the SAR and DSM acquisitions. As it is difficult to directly co-register the DSM to SAR owing to the difference in spatial resolution, the two datasets were geometrically matched using unmanned aerial vehicle (4 cm resolution) and helicopter-borne (30 cm resolution) photographs acquired as part of the ice campaigns. A total of five dual-polarimetric SAR features―backscattering coefficients at HH and VV polarizations, co-polarization ratio, co-polarization phase difference, and co-polarization correlation coefficient ―were computed from the dual-polarimetric SAR data and compared to the RMS height of the sea ice, which showed macroscale surface roughness. All the SAR features obtained at the high incidence angle were statistically weakly correlated with the RMS height of the sea ice, possibly influenced by the low backscattering close to the noise level that is attributed to the high incidence angle. The SAR features at the mid-incidence angle showed a statistically significant correlation with the RMS height of the sea ice, with Spearman’s correlation coefficient being higher than 0.7, except for the co-polarization ratio. Among the intensity-based and polarimetry-based SAR features, HH-polarized backscattering and co-polarization phase difference were analyzed to be the most sensitive to the macroscale RMS height of the sea ice. Our results show that the X-band dual-polarimetric SAR at mid-incidence angle exhibits potential for estimation of the macroscale surface roughness of the first-year sea ice with dry snow cover in summer.     

General Regression Neural Network and Radial Basis Neural Network for the Estimation of Crop Variables of Lady Finger

Estimation of crop variables is necessary for crop type monitoring as well as crop yield forecast. At the present era artificial neural network methodology are widely used to the remote sensing domain for numerous applications like crop yield forecasting and crop type classification. In the present work, two neural network models namely general regression neural network (GRNN) and radial basis function neural network (RBFNN) are used to estimate crop variables: leaf area index (LAI), biomass (BM), plant height (PH) and soil moisture (SM) by using ground based X-band scatterometer data. The both networks are trained and tested with X-band scatterometer data. The performance of the GRNN and RBFNN networks are found that the radial basis approach is more suitable for crop variable estimation in comparison to the GRNN approach. This work presents the applicability of neural network as an estimator and method employed could be useful to estimate the crop variables of other crops.     

星载SAR水下地形和水深遥感的最佳雷达系统参数模拟

根据星载合成孔径雷达 (SAR)浅海水下地形和水深成像机理 ,建立了浅海水下地形和水深雷达后向散射截面仿真模型。该模型包括奈维 斯托克斯方程、谱作用量平衡方程和雷达后向散射模式。利用该模型仿真结果 ,探讨了不同波段 (P、L、C和X)、不同极化 (VV和HH)和不同入射角 (2 0°— 70°)的星载SAR测量浅海水下地形和水深的能力。研究结果表明 ,浅海水下地形和水深遥感的最佳波段为P波段 ,L波段次之 ,C波段比X波段要好一些。VV极化SAR的测量能力要强于HH极化。 2 0°— 40°是星载SAR测量浅海水下地形和水深的最佳入射角范围。     

Potential of ASAR-ENVISAT for Estimating Near Surface Soil Moisture in a Sloping Terrain of a Himalayan Watershed

Soil moisture estimation is considered to be one of the important parameters in hydrological studies. The extraction of information on near surface soil moisture from the synthetic aperture radar is well established. The available Advanced Synthetic Aperture Radar (ASAR) data onboard ENVISAT with multi-incidence and multi-polarization mode for soil moisture estimation on sloping terrain was investigated. Empirical models were developed to estimate near surface soil moisture in the fallow agricultural fields by incorporating the effects of surface roughness using multi-incidence angle ASAR data. Medium incidence angle (IS-4) with VV polarization of ASAR data had higher correlation coefficient to volumetric soil moisture content. The ratio of medium (IS-4) to high incidence (IS-6) angle could further reduce the effect of surface roughness. The effect of topography on the radar data is taken care by calculating local incidence angle derived from ASTER DEM data. The VV polarization in the sloping terrain provided better results in comparison to VH polarization.     

海洋目标的HY-2A卫星微波散射计在轨辐射定标研究

HY-2A卫星微波散射计主要用于测量海表面的风速和风向,为了满足其风速和风向的测量精度要求,必须对其进行在轨辐射定标。本文基于散射计在开阔大洋的观测数据,利用经过定标的seawinds散射计后向散射系数数据,分析了海洋目标定标法的定标精度,结果表明定标精度可以达到0.15 dB,且模式函数对定标结果的影响为系统偏差,说明海洋目标定标法需要选用高精度的模式函数。然后,采用相同的海洋目标定标算法,实现了HY-2A卫星微波散射计的在轨辐射定标,获得HY-2A卫星微波散射计VV极化的定标系数为-1.79 dB,HH极化的定标系数为-1.73 dB。最后,利用NDBC浮标观测数据,检验HY-2A卫星微波散射计反演的风矢量精度,证明获得的定标系数可以明显提高HY-2A卫星微波散射计反演风矢量的精度。     

Multiple incidence angle sir-b data analysis over parts of the assam plains

SIR-B Synthetic Aperture Radar System operating at L-band (HH) acquired images over parts of the Assam plains at 25.6° and 45.2°incidence angle during October 1984. The capabilities of L-band SAR for delineating various land covers using multiple incidence angle imageries have been assessed. It was observed that colour composite image generated from multiple incidence angle imagery was useful in delineating various land cover units.     

Microwave response to broad leaf vegetation (Spinach) and vegetation covered moist soil for remote sensing

Soil moisture is one of the most important parameter which controls the growth of the vegetation. For accurate data and sufficient information to increase food production, remote sensing technique is highly useful. This paper deals with the bistatic microwave response of spinach and spinach covered soil moisture at various growth stages on X-band if the frequency spectrum. The microwave response of spinach in different stages of growth have been studied in terms of scattering co-efficient (σ^°). The look angle effect on σ^° is observed for like polarization i.e. (VV-and-HH) only. A linear regression analysis has been done between the vegetation covered soil moisture and scattering co-efficient. It provides an idea that VV-polarization is more sensitive than HH-polarizalion for vegetation covered soil moisture and best suitable look angle for observing vegetation covered soil moisture is less than 40°(θ<40°).     

Estimation of winter wheat crop growth parameters using time series Sentinel-1A SAR data

In the present study, Sentinel-1A Synthetic Aperture Radar analysis of time series data at C-band was carried out to estimate the winter wheat crop growth parameters. Five different date images were acquired during January 2015–April 2015 at different growth stages from tillering to ripening in Varanasi district, India. The winter wheat crop parameters, i.e. leaf area index, vegetation water content (VWC), fresh biomass (FB), dry biomass (DB) and plant height (PH) were estimated using random forest regression (RFR), support vector regression (SVR), artificial neural network regression (ANNR) and linear regression (LR) algorithms. The Ground Range Detected products of Interferometric Wide (IW) Swath were used at VV polarization. The three different subplots of 1 m² area were taken for the measurement of crop parameters at every growth stage. In total, 73 samples were taken as the training data-sets and 39 samples were taken as testing data-sets. The highest sensitivity (adj. R^2?=?0.95579) of backscattering with VWC was found using RFR algorithm, whereas the lowest sensitivity (adj. R^2?=?0.66201) was found for the PH using LR algorithm. Overall results indicate more accurate estimation of winter wheat parameters by the RFR algorithm followed by SVR, ANNR and LR algorithms.     

环境小卫星S波段SAR监测土壤水分变化应用分析

通过IEM正演模型的模拟数据，发展了一种用S波段(3.0GHz)、VV极化数据反演土壤含水量相对变化的算法；选择典型的土壤含水量、地表粗糙度及入射角变化范围，模拟出两幅SAR图像，并把该算法应用到模拟图像中，对算法进行验证和改进； 将结果与输入值对比，结果表明，该算法能较好地提取土壤含水量时间和空间变化信息。     

Discrimination between Babul plantations and Mustard crop using Polarimetric C- and L- band data

Radar sensors can be used for large-scale vegetation mapping and monitoring using backscattering coefficients in different polarizations and wavelength bands. C-band space borne SAR is widely used for the classification of agricultural crops, but can only perform a limited discrimination of various tree species. This paper presents the results of discrimination between mustard crop and babul plantation (Prosopis sp.) using quad polarisation Radarsat 2 and ALOS PALSAR data. Study area is comprised of dense babul plantation along the canal, mustard crop on one side of the canal and Fallow land near to Ramgarh village of Jaisalmer district. Three bands of Radarsat (HH, HV and VV) acquired during peak mustard crop growth stage were integrated with four polarizations (HH, HV, VH and VV) of ALOS PALSAR acquired when crop cover was absent. Using only Radarsat data Jefferies-Matusita (JM) separability between mustard crop and babul plantation was found to be poor (710). Where as in the seven band combination the separability was observed to be high (1374). Among the different polarizations three layer combination, highest separability was observed using cross polarizations (HV and VH) of L-band with any one of the Radarsat Polarisation (HH/HV/VV). This combination of C- and L-band resulted in easy separation of mustard and babul plantation which was otherwise difficult using only Radarsat data.     

Crop Evapotranspiration Estimation for Wheat (<Emphasis Type="Italic">Triticum aestivum</Emphasis> L.) Using Remote Sensing Data in Semi-Arid Region of Maharashtra

Penman–Monteith method adapted to satellite data was used for the estimation of wheat crop evapotranspiration during the entire growth period using satellite data together with ground meteorological measurements. The IRS-1D/IRS-P6 LISS-III sensor data at 23.5 m spatial resolution for path 096 and row 059 covering the study area were used to derive, albedo, normalized difference vegetation index, leaf area index and crop height and then to estimate wheat crop evapotranspiration referred to as actual evapotranspiration (ETact). The ETact varied from 0.86 to 3.41 mm/day during the crop growth period. These values are on an average 16.40 % lower than wheat crop potential evapotranspiration (ETc) estimated as product of reference crop evapotranspiration estimated by Penman–Monteith method and lysimetric crop coefficient (Kc). The deviation of ETact from ETc is significant, when both the values were compared with t test for paired two sample means. Though the observations on ETact were taken from well maintained unstressed experimental plot of 120 × 120 m size, there was significant deviation. This deviation could be attributed to, the satellite images representing the actual crop evapotranspiration as function crop canopy biophysical parameters, condition of the crop stand, climatic and soil conditions and the microclimate variation over area of one hectare. However, Penman–Monteith method represents a flat rate of specific growth stage of the crop.     

Comprehensive evaluation of soil moisture retrieval models under different crop cover types using C-band synthetic aperture radar data

In the present study, random forest regression (RFR), support vector regression (SVR) and artificial neural network regression (ANNR) models were evaluated for the retrieval of soil moisture covered by winter wheat, barley and corn crops. SVR with radial basis function kernel was provided the highest adj. R² (0.95) value for soil moisture retrieval covered by the wheat crop at VV polarization. However, RFR provided the adj. R² (0.94) value for soil moisture retrieval covered by barley crop at VV polarization using Sentinel-1A satellite data. The adj. R² (0.94) values were found for the soil moisture covered by corn crop at VV polarization using RFR, SVR linear and radial basis function kernels. The least performance was reported using ANNR model for almost all the crops under investigation. The soil moisture retrieval outcomes were found better at VV polarization in comparison to VH polarization using three different models.     

利用ESAR极化数据的复杂地形区森林地上生物量估算

利用机载E-SAR传感器获取的P-波段全极化SAR数据与实测林分样地数据,分析不同极化方式后向散射系数在地形起伏区与森林地上生物量（AGB）的响应关系,以改进的水云模型为基础,建立了融入地形因子的分析性模型。采用遗传算法确定模型的最优参数,并对模型在不同坡度情况下的可靠性、稳定性进行分析,同时通过与常用模型相对比,确定水云分析模型在复杂地形区估算AGB的优势。结果表明：在森林AGB处于较低值的情况下,后向散射系数（HH、HV、VV）变化趋势与AGB变化趋势保持一致,但随着AGB值的增大,这种一致性仅在HV极化方式下继续保持,因此相比之下,HV极化方式更适用于复杂地形区生物量的估算。地形对森林AGB的估算具有极大的影响,后向散射系数与AGB的相关性随着地形坡度的增加而减小。5种模型估算森林AGB的能力大小排序为：水云分析模型 > 二次模型 > 对数模型 > 指数模型 > 线性模型。地形起伏较小的地区估算稳定性排序为：水云分析模型 > 二次模型 > 对数模型 > 指数模型>线性模型。地形起伏较大的地区估算稳定性排序为。水云分析模型 > 二次模型 > 线性模型 > 指数模型 > 对数模型。利用水云分析模型对研究区AGB估算,其实测AGB与模型估算的生物量值决定系数为0.597,RMSE为30.876 t/hm²,拟合精度为77.40%。     

Evaluation of RADARSAT Standard Beam data for identification of potato and rice crops in India

The Canadian satellite RADARSAT launched in November 1995 acquires C-band HH polarisation Synthetic Aperture Radar (SAR) data in various incident angles and spatial resolutions. In this study, the Standard Beam S7 SAR data with 45°–49° incidence angle has been used to discriminate rice and potato crops grown in the Gangetic plains of West Bengal state. Four-date data acquired in the 24-day repeat cycle between January 2 and March 15, 1997 was used to study the temporal backscatter characteristics of these crops in relation to the growth stages. Two, three and four-date data were used to classify the crops. The results show that the backscatter was the lowest during puddling of rice fields and increased as the crop growth progressed. The backscatter during this period changed from −18 dB to −8 dB. This temporal behaviour was similar to that observed in case of ERS-SAR data. The classification accuracy of rice areas was 94% using four-date data. Two-date data, one corresponding to pre-field preparation and the other corresponding to transplantation stage, resulted in 92% accuracy. The last observation is of particular interest as one may estimate the crop area as early as within 20–30 days of transplantation. Such an early estimate is not feasible using optical remote sensing data or ERS-SAR data. The backscatter of potato crop varied from −9 dB to −6 dB during the growth phase and showed large variations during early vegetative stage. Two-date data, one acquired during 40–45 days of planting and another at maturing stage, resulted in 93% classification accuracy for potato. All other combinations of two-date data resulted in less than 90% classification accuracy for potato.     

Estimation of Above Ground Biomass for Central Indian Deciduous Forests Using ALOS PALSAR L-Band Data

Reliable and accurate estimates of tropical forest above ground biomass (AGB) are important to reduce uncertainties in carbon budgeting. In the present study we estimated AGB of central Indian deciduous forests of Madhya Pradesh (M.P.) state, India, using Advanced Land Observing Satellite – Phased Array type L-band Synthetic Aperture Radar (ALOS-PALSAR) L-band data of year 2010 in conjunction with field based AGB estimates using empirical models. Digital numbers of gridded 1?×?1° dual polarization (HH & HV) PALSAR mosaics for the study area were converted to normalized radar cross section (sigma naught - σ⁰). A total of 415 sampling plots (0.1 ha) data collected over the study area during 2009–10 was used in the present study. Plot-level AGB estimates using volume equations representative to the study area were computed using field inventory data. The plot-level AGB estimates were empirically modeled with the PALSAR backscatter information in HH, HV and their ratios from different forest types of the study area. The HV backscatter information showed better relation with field based AGB estimates with a coefficient of determination (R²) of 0.509 which was used to estimate spatial AGB of the study area. Results suggested a total AGB of 367.4 Mt for forests of M.P. state. Further, validation of the model was carried out using observed vs. predicted AGB estimates, which suggested a root mean square error (RMSE) of ±19.32 t/ha. The model reported robust and defensible relation for observed vs. predicted AGB values of the study area.     

Application of ENVISAT ASAR Data in Mapping Rice Crop Growth in Southern China

This research letter presents preliminary results of mapping rice crop growth using ENVISAT advanced synthetic aperture radar (ASAR) alternating polarization HH/HV data. Four ASAR HH/HV images were collected in the early rice-growth cycle in the test site in 2006, and the temporal response of ASAR data to the rice field was analyzed. The height and biomass of rice were measured during acquisition of ASAR data, and empirical relationships were established between the backscattering coefficient and these two parameters. Based on the temporal variation of the radar response, a method for mapping a rice growth area was developed using the combination of ASAR HH and HV polarization data between two acquisition dates. The results confirm that C-band SAR data have great potential in the development of an operational system for monitoring rice crop growth in Southern China.     

Crop polarisation behavior derived from Multi-Temporal adeos-polder data

This paper reports a study on multi-temporal polarized response of wheat crop from spaceborne ADEOS-POLDER sensor over a homogeneous wheat region of Punjab, India. Both the polarized as well as total reflectance of wheat were observed at different scattering angles for two spectral bands i.e. 670 nm and 865 nm during crop growth from November to April in rabi 1996-97 season. Results show that sun-target-viewing geometry plays an important role in polarization property. The top of atmosphere (TOA) polarized reflectance is found to decrease exponentially with increasing scattering angle. Polarized reflectance of crop was found to be an order of magnitude smaller in comparison to the total reflectance. An attempt was also made to model the observed polarized behavior over an agricultural area using a theoretical simplified crop reflectance model and accounting for atmospheric molecular (Rayleigh) contribution in the single scattering approximation. It was found that there was a decrease in the polarized reflectance at the grain filling (heading) stage of wheat crop. This is in accordance with ground- based observations and can be due to the reduction in the specular component of the reflected light during post-heading stage of the crop.