Microwave Backscattering Coefficient Research Articles

Peatland wetness as an indicator of fire occurrence in forest and land fires (FLFs)

Background: Peatland ecosystems play an important role in the hydrological cycle and carbon cycling. In Indonesia, peatlands store about 28.6 gigatonnes of carbon which is equivalent to 10 years of global fossil fuel emissions. Peatlands act as a water storage during wet seasons and slowly release water during dry seasons to maintain river discharges and hydrological balance. However, climate change induced prolong drought has increased peatland dryness in recent decades which elevate the risks of unwanted peatland fires. During El Nino-induced drought in 2015, over 2.6 million hectares of forest and land burned, emitting 0.81–1.4 gigatonnes of greenhouse gases. The extreme fires damaged biodiversity, degraded water quality and displaced thousands of locals. This study aimed to analyze peatland wetness as an indicator of fire occurrences in forest and land fires (FLFs) in Riau, Indonesia by examining the relationship between degree of peatland wetness derived from satellite imagery and hotspots data. Methods: Peatland wetness was estimated from microwave backscattering coefficients at several RadarSat synthetic aperture radar (SAR) wavelengths and cross validated with water table depth measurements from 120 monitoring wells. Hotspots data between 2015-2020 were obtained from NASA's MODIS active fire product. Finding: Preliminary results showed significant negative correlations between peatland wetness and numbers of hotspots in peatlands, with more hotspots occurring in drier peatlands compared to wetter ones. This implies that maintaining peatland hydrological functions through continuoussaturation is pivotal to prevent severe peatland wildfires under future climate change. Conclusion: Conservation efforts to restore hydrological balance in degraded peatlands through re-wetting strategies are recommended. Further research utilizing machine learning algorithms to produce high-resolution peatland wetness maps can improve fire risk monitoring in peatlands. Novelty/Originality of this Study: This study introduces the novel concept of utilizing peatland wetness as a key indicator for predicting and mitigating forest and land fires in Indonesia, particularly in Riau Province. By combining peatland moisture and temperature data, the research establishes threshold values to better predict fire risks and guide timely mitigation efforts, thereby enhancing the efficiency and effectiveness of FLF response activities.

C- and L-band SAR signatures of Arctic sea ice during freeze-up

Identifying sea ice types in the early stages of development from L-band SAR imagery remains an active research area during the Arctic freeze-up period. We used ScanSAR C- and L-band imagery from RADARSAT-2, ALOS PALSAR and ALOS-2 PALSAR-2, to identify ice types in the North Water Polynya (NOW) and Victoria Strait (VS) region of the Canadian Arctic. We investigated the HH-polarized microwave backscatter coefficient (σHH0) and its GLCM texture parameters for six ice classes and open water. We found very low σHH0 for nilas at both C- and L-band. Although similar σHH0 found for grey ice at both frequencies, σHH0 decrease with increasing ice thickness at L-band from grey ice, whereas, at C-band, σHH0 increases from grey to grey-white ice and then decreases as the ice grows. GLCM texture parameters show lower values for L-band than C-band; however, separability among classes was found only for a few selected parameters. We used the support vector machine (SVM) algorithm for ice type classification from SAR scenes using σHH0 and GLCM texture statistics. Due to overlapping σHH0 signatures at C-band, early-stage ice classes were substantially misclassified. L-band identified early-stage ice classes with higher accuracy compared to C-band but misclassified thicker ice types and open water. L-band alone provided very good classification results (~80% accuracy) and combining L- and C-band (i.e., dual-frequency approach) further increased accuracy to >90%. C-band alone resulted in the lowest accuracy of <60%. We acknowledge that developing a universal ice classification is still a challenge and requires some manual supervision to adopt variable ice conditions into the classification method. However, a dual-frequency approach can achieve higher classification accuracy than conventionally used single-frequency approaches. This research highlights the value of upcoming L-Band SAR missions to improve sea ice classification in regions where a variety of ice types exist, including many thinner types, which are now dominating an increasingly warming Arctic.

Design and Actual Data Analysis of Chang’E-5 Landing Region Microwave Backscatter Coefficient

Abstract Chang’E-3 landed at the north part of the Mare Imbrium on 14 December 2013, Chang’E-4 landed at South Pole’s Aitken Basin on 3 January 2019 and Chang’E-5 landed at Rümker region in northern Oceanus Procellarum on 1 December 2020. The microwave range and velocity sensor (MRVS) on the lander provided the range and velocity information relative to the lunar surface during the powered descent phase, ensuring landing softly. In this paper, we use the lunar surface backscatter coefficient of Ka-band which was analyzed from the telemetry of MRVS equipped on Chang’E-3 and Chang’E-4, as for the important design basis of Chang’E-5 MRVS; at the same time, actual data is analyzed from the telemetry of Chang’E-5 sensor, and the accumulated analysis of lunar surface backscatter coefficient as the important reference will be used in the future MRVS design and lunar detection.

Desertification Extraction Based on a Microwave Backscattering Contribution Decomposition Model at the Dry Bottom of the Aral Sea

The fine particles produced during the desertification process provide a rich material source for sand and dust activities. Accurately locating the desertified areas is a prerequisite for human intervention in sand and dust activities. In arid and semi-arid regions, due to very sparse vegetation coverage, the microwave surface scattering model is very suitable for describing the variation of topsoil property during the process of desertification. However, the microwave backscattering coefficient (MBC) trend of the soil during the desertification process is still unclear now. Moreover, the MBC of a resolution unit usually involves the contribution of soil and vegetation. These problems seriously limit the application of microwave remote sensing technology in desertification identification. In this paper, we studied the soil MBC change trend during the desertification process and proposed a microwave backscattering contribution decomposition (MBCD) model to estimate the soil MBC of a resolution unit. Furthermore, a simple microwave backscattering threshold (SMSBT) model was established to describe the severity of desertification. The MBCD and SMSBT models were verified qualitatively through landscape photos of sampling points from a field survey in November 2018. The results showed that the MBC would gradually decline with the deepening degree of desertification. The MBCD model and the corresponding least squares method can be used to estimate the soil MBC accurately, and the SMSBT model can accurately distinguish different degrees of desertification. The results of desertification classification showed that more than 68% of the dry bottom of the Aral Sea is suffering from different degrees of desertification.

Effects of Plant Crown Shape on Microwave Backscattering Coefficients of Vegetation Canopy.

A microwave scattering model is a powerful tool for determining relationships between vegetation parameters and backscattering characteristics. The crown shape of the vegetation canopy is an important parameter in forestry and affects the microwave scattering modeling results. However, there are few numerical models or methods to describe the relationships between crown shapes and backscattering features. Using the Modified Tor Vergata Model (MTVM), a microwave scattering model based on the Matrix Doubling Algorithm (MDA), we quantitatively characterized the effects of crown shape on the microwave backscattering coefficients of the vegetation canopy. FEKO was also used as a computational electromagnetic method to make a complement and comparison with MTVM. In a preliminary experiment, the backscattering coefficients of two ideal vegetation canopies with four representative crown shapes (cylinder, cone, inverted cone and ellipsoid) were simulated: MTVM simulations were performed for the L (1.2 GHz), C (5.3 GHz) and X (9.6 GHz) bands in fully polarimetric mode, and FEKO simulations were carried out for the C (5.3 GHz) band at VV and VH polarization. The simulation results show that, for specific input parameters, the mean relative differences in backscattering coefficients due to variations in crown shape are as high as 127%, which demonstrates that the crown shape has a non-negligible influence on microwave backscattering coefficients of the vegetation canopy. In turn, this also suggests that investigation on effects of plant crown shape on microwave backscattering coefficients may have the potential to improve the accuracy of vegetation microwave scattering models, especially in canopies where volume scattering is the predominant mechanism.

A modified TSM for better prediction of hh polarized microwave backscattering coefficient from sea surface

ABSTRACT ABSTRACT: The two-scale Model (TSM) is broadly employed for studying EM scattering from rough sea surface due to its advantage of simple and practical. However, it suffers from a drawback in the prediction of microwave scattering from sea surface. That is the TSM cannot provide accurate predictions for hh polarized scattering coefficients. To overcome this problem, a modified TSM (MTSM) is proposed in this paper. Through analysing the validity conditions of the KA (Kirchhoff approximation) and SPM (small perturbation method), the concept of ‘transition spectral region’ is proposed for the first time. In this region, both the validity conditions of the KA and SPM are satisfied, indicating both the specular scattering and Bragg scattering contributed to the scattered energy in this region. Therefore, in the MTSM, two cut-off parameters are used to divide sea surface roughness into ‘specular scattering region’ and ‘Bragg scattering region’. Additionally, in the MTSM, the KA-SP rather than the commonly used KA-GO is employed for simulating the specular scattering component. Numerical simulations confirm that the MTSM performs better than the TSM and the SSA-1, especially in the prediction of hh polarized scattering coefficient.

On the Potential of Sentinel-1 for High Resolution Monitoring of Water Table Dynamics in Grasslands on Organic Soils

For soils with shallow groundwater and high organic carbon content, water table depth (WTD) is a key parameter to describe their hydrologic state and to estimate greenhouse gas emissions (GHG). Since the microwave backscatter coefficient (σ0) is sensitive to soil moisture, the application of Sentinel-1 satellite data might support the monitoring of these climate-relevant soils at high spatial resolution (~100 m) by detecting spatial and temporal changes in local field and water management. Despite the low penetration depth of the C-band, σ0 is influenced by shallow WTD fluctuations via the soil hydraulic connection between the water table and surface soil. Here, we analyzed σ0 at 60 monitoring wells in a drained temperate peatland with degraded organic soils used as extensive grassland. We evaluated temporal Spearman correlation coefficients between σ0 and WTD considering the soil and vegetation information. To account for the effects of seasonal vegetation changes, we used the cross-over (incidence) angle method. Climatologies of the slope of the incidence angle dependency derived from two years of Sentinel-1 data and their application to the cross-over angle method did improve correlations, though the effect was minor. Overall, averaged over all sites, a temporal Spearman correlation coefficient of 0.45 (±0.17) was obtained. The loss of correlation during summer (higher vegetation, deeper WTD) and the effects of cuts and grazing are discussed. The site-specific general wetness level, described by the mean WTD of each site was shown to be a major factor controlling the strength of the correlation. Mean WTD deeper than about −0.60 m lowered the correlations across sites, which might indicate an important limit of the application.

An Improved Approach for Soil Moisture Estimation in Gully Fields of the Loess Plateau Using Sentinel-1A Radar Images

As an essential ecological parameter, soil moisture is important for understanding the water exchange between the land surface and the atmosphere, especially in the Loess Plateau (China). Although Synthetic Aperture Radar (SAR) images can be used for soil moisture retrieval, it is still a challenge to mitigate the impacts of complex terrain over hilly areas. Therefore, the objective of this paper is to propose an improved approach for soil moisture estimation in gully fields based on the joint use of the Advanced Integral Equation Model (AIEM) and the Incidence Angle Correction Model (IACM) from Sentinel-1A observations. AIEM is utilized to build a simulation database of microwave backscattering coefficients from various radar parameters and surface parameters, which is the data basis for the retrieval modeling. IACM is proposed to correct the deviation between the local incidence angle at the scatterer and the radar viewing angle. The study area is located in the Loess Plateau of China, where the main land cover is mostly bare land and the terrain is complex. The Sentinel-1A SAR data in C-band with dual polarization acquired on October 19th, 2017 was adopted to extract the VV&amp;VH polarimetric backscattering coefficients. The in situ measurements of soil moisture were collected on the same day of the SAR acquisition, for evaluating the accuracy of the SAR-derived soil moisture. The results showed that, firstly, the estimated soil moisture with volumetric content between 0% and 20% was in the majority. Subsequently, both the RMSE of estimation values (0.963%) and the standard deviation of absolute errors (0.957%) demonstrated a good accuracy of the improved approach. Moreover, the evaluation of IACM confirmed that the improved approach coupling IACM and AIEM was more efficient than employing AIEM solely. In conclusion, the proposed approach has a strong ability to estimate the soil moisture in the gully fields of the Loess Plateau from Sentinel-1A data.

Incidence Angle Dependence of HH-Polarized C- and L-Band Wintertime Backscatter Over Arctic Sea Ice

Synthetic aperture radar (SAR) incidence angle has a significant effect on the microwave backscatter from sea ice. This paper investigates the incidence angle dependence of C- and L-band HH-polarized microwave backscatter coefficient over Arctic first-year sea ice (FYI) and multiyear sea ice (MYI) in winter. Advanced Land Observation Satellite Phased Array type L-band SAR (L-band) and RADARSAT-2 (C-band) images are used to derive ice type-specific incidence angle dependencies calculated using linear regression models. For L-band, mean ice type-specific incidence angle dependencies for FYI and MYI are −0.21 and −0.30 dB/1°, respectively; and for C-band, they are −0.22 and −0.16 dB/1°, respectively. To validate our results, we calculated root-mean-square deviation (RMSD) by comparing the ice type-specific dependence from 2010 with individual dependencies from 2009 based on ice types and frequencies. The RMSD is found to be smaller than the standard deviation of ice type-specific dependencies for both frequencies. The RMSD values for the L-band incidence angle dependencies are 0.03 and 0.04 dB/1° for FYI and MYI, respectively. For C-band, the RMSD values for the FYI and MYI dependencies are 0.03 and 0.01 dB/1°, respectively. Subsequently, we demonstrate that after applying incidence angle normalization, the variability of C- and L-band SAR backscatter reduces and separability of ice types increase substantially.

Soil moisture retrieval in farmland using C-band SAR and optical data

Soil moisture retrieval in vegetation-covered area with space borne synthetic aperture radar is a challenging process due to the impact of vegetation on the multiple scattering of electromagnetic wave. In this paper, a semi-empirical method is proposed to estimate farmland soil moisture by active microwave remote sensing and optical remote sensing. By integrating the vegetation water content estimated from optical data and thermal infrared data into a coupling model based on a water–cloud model, the influence of vegetation on microwave backscattering coefficient was eliminated and thus soil moisture in vegetation-covered area was accurately retrieved. The experiment of soil moisture retrieval was carried out using Radarsat-2 and Landsat 8 datasets in western Great Khingan Mountains, Inner Mongolia, China. The research showed that the accuracy of the coupling model is high and the R 2 is up to 0.69 using HH polarization. Moreover, the effects of crop types on soil moisture retrieval, particularly barley, could also be distinguished using the coupling model.

Estimating Soil Moisture Distributions across Small Farm Fields with ALOS/PALSAR

The ALOS (advanced land observing satellite) has an active microwave sensor, PALSAR (phased array L-band synthetic aperture radar), which has a fine resolution of 6.5 m. Because of the fine resolution, PALSAR provides the possibility of estimating soil moisture distributions in small farmlands. Making such small-scale estimates has not been available with traditional satellite remote sensing techniques. In this study, the relationship between microwave backscattering coefficient (σ) measured with PALSAR and ground-based soil moisture was determined to investigate the performance of PALSAR for estimating soil moisture distribution in a small-scale farmland. On the ground at a cabbage field in Japan in 2008, the soil moisture distribution of multiple soil layers was measured using time domain reflectometry when the ALOS flew over the field. Soil moisture in the 0–20 cm soil layer showed the largest correlation coefficient with σ (r = 0.403). The σ values also showed a strong correlation with the ground surface coverage ratio by cabbage plants. Our results suggested that PALSAR could estimate soil moisture distribution of the 0–20 cm soil layer across a bare field and a crop coverage ratio when crops were planted.

Estimation of kidney bean crop variables using ground-based scatterometer data at 9.89 GHz

Crop variables were estimated using ground-based multi-temporal, multi-angle and co-polarized scatterometer X-band data at 9.89 GHz. An outdoor crop-bed was prepared to observe the scatterometer response at VV and HH polarizations in the incidence angle range from 20° to 70°. Soil moisture (SM) and crop variables, including leaf area index (LAI), leaf water area index (LWAI) and plant height (PH), of a kidney bean crop at different stages of growth were measured. The aim of this study was to estimate the crop variables and to determine how the variables interact with microwaves. We found that LAI correlated better than LWAI with the microwave backscattering coefficient σ0 (at 9.89 GHz) and the correlation with PH was lower, at the most suitable incidence angle of 40°. The value of σ0 increased with an increase in LAI at both polarizations. The crop variables retrieved by inversion of the cloud model and a polarization-based model were compared with another model based on least squares optimization. The results are promising for the estimation of crop variables, particularly LAI.

Multilayer soil model for improvement of soil moisture estimation using the small perturbation method

A multilayer soil model is presented for improved estimation of soil moisture content using the first-order small perturbation method (SPM) applied to measurements of radar backscattering coefficient. The total reflection coefficient of the natural bare soil including volume scattering contribution is obtained using the multilayer model. The surface reflection terms in SPM model are replaced by the total reflection coefficient from the multilayer soil surface in estimating soil moisture. The difference between the modified SPM model and the original SPM surface model is that the modified SPM model includes both the surface scattering and the volumetric scattering of the natural bare soil. Both the modified SPM model and the original SPM model are tested in soil moisture retrievals using experimental microwave backscattering coefficient data in the literature. Results show that the mean square errors between the measured data and the values estimated by the modified SPM model from all samples are 5.2%, while errors from the original SPM model are 8.4%. This indicates that the capability of estimating soil moisture by the SPM model is improved when the surface reflection terms are replaced by the total reflection coefficients of multilayer soil model over bare or very sparsely vegetation covered fields.

THE ESTIMATION OF TROPICAL PLANTATION FOREST LEAF AREA INDEX BASED ON RADARSAT SAR DATA

The relationship between the microwave backscattering coefficient and the leaf area index(LAI) in tropical plantation forest area was studied,and a method for estimating leaf area index retrieved from Radarsat-1 SAR based on the modified WATER-CLOUD Model was presented.The method was applied over Leizhou site in Guangdong province,with the correlation of the main species(R2) being approximately 0.5.Based on characteristics of forest and radar images,the method can effectively estimate plantation forest LAI in the cloud-prone and raining area.

Relationship between Microwave Backscattering Coefficient of Ground Covered with Dry Snow and Snow Water Equivalent

Relationship between Microwave Backscattering Coefficient of Ground Covered with Dry Snow and Snow Water Equivalent

Analysis of Temporal Backscattering of Cotton Crops Using a Semiempirical Model

To develop an operational methodology for estimating soil moisture and crop biophysical parameters and to generate a crop cover map, backscattering signatures of vegetation canopies are investigated using multitemporal Radarsat synthetic aperture radar (SAR) data over a predominantly cotton-growing area in India during low to peak crop growth stage. A simple parameterization of the water-cloud model with volumetric soil moisture content (m/sub v/) and leaf area idex (LAI) is used to simulate the microwave backscattering coefficient (/spl sigma//sup 0/), as it is found to be a good candidate for operational purposes as demonstrated by several workers in past. The influence of crop height (H), LAI, and m/sub v/ on /spl sigma//sup 0/ is investigated during peak crop growth stage. A linear relationship between LAI and crop height is derived semiempirically, and a linear zone is chosen for analysis during the peak crop-growing stage. Estimation of average volume fraction of leaves (V~/sub l/) and attenuation factor (L) by two different approaches is discussed: 1) using linear relationship between LAI versus crop height and 2) from the water-cloud model parameter (/spl kappa/) estimation by iterative minimum least square error approach. It is observed that model-estimated parameters agree well with the measured values within an acceptable error limit. At lower soil moisture, m/sub v//spl cong/0.02(cm/sup 3//spl middot/cm/sup -3/), the dynamic range of /spl sigma//sup 0/ is found to be about +5 dB for 0-70 cm of crop height but monotonously decreases to null at a transition point, having m/sub v//spl ap/0.38(cm/sup 3//spl middot/cm/sup -3/). A positive correlation is found between backscattering coefficient and crop height till this transition point but shows a negative correlation beyond that, signifying the predominant attenuation by vegetation over soil. Differential moisture sensitivity (d/spl sigma//sup 0//dm/sub v/) of the backscattering coefficient decreases by half from 20.55 dB/(cm/sup 3//spl middot/cm/sup -3/) for dry and bare-field conditions to 10.68 dB/(cm/sup 3//spl middot/cm/sup -3/) for wet and crop-covered fields (m/sub v/=0.38cm/sup 3//spl middot/cm/sup -3/, H=70cm), whereas differential crop height sensitivity (d/spl sigma//sup 0//dH) varies from 0.22-0.03 dB/cm for bare-field conditions to crop-covered fields with crop height 70 cm. It is found that the percentage of relative error is smallest (2.27%) for LAI and attenuation factor estimation using the value of V~/sub l/, from LAI models, whereas it is 4.25% when estimating from the attenuation coefficient (/spl kappa/) from the model.

>Synergy of Remote Sensing and Modeling for Estimating Ecophysiological Processes in Plant Production

Information on the ecological and physiological status of crops is essential for growth diagnostics and yield prediction. Within-field or between-field spatial information is required, especially with the recent trend toward precision agriculture, which seeks the efficient use of agrochemicals, water, and energy. The study of carbon and nitrogen cycles as well as environmental management on local and regional scales requires assessment of the spatial variability of biophysical and ecophysiological variables, scaling up of which is also needed for scientific and decision -making purposes. Remote sensing has great potential for these applications because it enables wide-area, non¬destructive, and real-time acquisition of information about plant ecophysiological conditions. With recent advances in sensor technology, a variety of electromagnetic signatures, such as hyperspectral reflectance, thermal-infrared tempera¬ture, and microwave backscattering coefficients, can be acquired for both plants and ecosystems using ground-based, airborne, and satellite platforms. Their spatial and temporal resolutions have both recently been improved. This article reviews the state of the art in the remote sensing of plant ecophysiological data, with special emphasis on the synergy between remote sensing signatures and biophysical and ecophysiological process models. Several case studies for the optical, thermal, and microwave domains have demonstrated the potential of this synergistic linkage. Remote sensing and process modeling methods complement each other when combined synergistically. Further research on this approach is needed for a wide range of ecophysiological and ecosystem studies, as well as for practical crop management.

Development of a modified two‐scale electromagnetic model simulating both active and passive microwave measurements: Comparison to data remotely sensed over the ocean

Spaceborne microwave remote sensing allows the determination of oceanic and atmospheric parameters. Operational payloads such as ERS‐1 and ERS‐2 and TOPEX/Poseidon as well as missions such as Jason (from NASA–Centre National d'Etudes) or Envisat (from the European Space Agency), have contained or contain paired microwave instruments looking at the nadir direction. This combination consists of microwave radiometers and a radar‐altimeter. For the frequencies chosen in oceanographic satellite payloads, the active mode signal is mostly dependent on the surface state through its reflectivity and thus used for the near‐surface wind speed retrieval. The active mode can also be attenuated by the atmosphere. On the other hand, the passive mode is related to the surface emissivity and the atmospheric radiation through the radiative transfer equation. Until now, the oceanic and atmospheric parameters have been retrieved separately, the latter being used to correct radar measurements. However, the reflectivity and the emissivity of a target are not independent quantities; hence the synergistic use of these two kinds of microwave measurements should allow one to improve the retrieval quality of the sea and atmosphere parameters. For this purpose, a unified model has been developed for the simulation of both the microwave backscattering coefficient σ° (active measurement) and the microwave emissivity, an important factor for the brightness temperature TB simulation, for every configuration (incidence angles, frequency, polarizations), taking into account the fact that the reflectivity and the emissivity are complementary to unity. The atmospheric absorption is computed following a widely used model from the literature. This paper gives a description and a first attempt of validation of this approach through a comparison with real data. The performance of the model is assessed by comparing the simulations to both brightness temperatures and backscattering coefficients from ERS‐1 and TOPEX/Poseidon's instruments during the SEMAPHORE experiment, over a two‐month period.

Season-long daily measurements of multifrequency (Ka, Ku, X, C, and L) and full-polarization backscatter signatures over paddy rice field and their relationship with biological variables

The objective of this study was to investigate the interaction between microwave backscatter signatures and rice canopy growth variables, as well as to provide definitive insight into the interaction between backscatter and vegetation based on a comprehensive data set collected under the unique crop conditions of paddy rice (background is water surface). Our unique data consisted of daily microwave backscattering coefficients at all combinations of five frequencies (Ka, Ku, X, C, and L), all polarizations (HH, VH, HV, and VV), and four incident angles (25°, 35°, 45°, and 55°) for the entire rice crop period, from before transplantation until postharvest cultivation. A wide range of plant variables, such as leaf area index (LAI), and biomass of the whole plant and plant parts were measured periodically throughout the season. Analyses based on statistical correlation and a simple backscatter process model (the water cloud model) showed that LAI was best correlated with HH- and cross-polarization of the C-band, while fresh biomass was best correlated with HH- and cross-polarization of the L-band. Contrarily, the higher frequency bands (Ka, Ku, and X) were poorly correlated with LAI and biomass. Interestingly, the weight of heads (ultimately the grain yield) was highly correlated with the backscattering coefficient of the Ka- and Ku-bands, while the others were poorly correlated. The simple scattering process model may be applicable for C- and L-bands in rice canopies, while it may not be suitable for Ka- and Ku-bands. In the model, LAI was a better canopy descriptor for the C-band, while total fresh biomass was a better canopy descriptor for the L-band.

Simulation and Analysis for the Microwave Backscattering Coefficient of Rice

Simulation and Analysis for the Microwave Backscattering Coefficient of Rice