Use of Satellite Radar Bistatic Measurements for Crop Monitoring: A Simulation Study on Corn Fields

This paper presents a theoretical study of microwave remote sensing of vegetated surfaces. The purpose of this study is to find out if satellite bistatic radar systems can provide a performance, in terms of sensitivity to vegetation geophysical parameters, equal to or greater than the performance of monostatic systems. Up to now, no suitable bistatic data collected over land surfaces are available from satellite, so that the electromagnetic model developed at Tor Vergata University has been used to perform simulations of the scattering coefficient of corn, over a wide range of observation angles at L- and C-band. According to the electromagnetic model, the most promising configuration is the one which measures the VV or HH bistatic scattering coefficient on the plane that lies at the azimuth angle orthogonal with respect to the incidence plane. At this scattering angle, the soil contribution is minimized, and the effects of vegetation growth are highlighted

Apport de la polarimétrie radar en bande C pour l’estimation de l’humidité du sol en zone agricole

La télédétection possède plusieurs applications potentielles pour le suivi de l’humidité de surface du sol (0 à 5 cm de profondeur). Un suivi de l’humidité du sol à période régulière permettrait de nombreuses applications en hydrologie, climatologie, suivi d’événements météorologiques et agriculture de précision. Le signal radar à synthèse d’ouverture (RSO) en bande C tel que celui de RADARSAT-2 est sensible aux variations des paramètres du sol et de la végétation selon certaines conditions. L’inversion de modèles de rétrodiffusion linéaire a permis l’estimation de l’humidité du sol en zone agricole, mais pour des domaines de validité très restreints. Diverses missions satellitaires en cours ou futures permettent l’acquisition d’images radars polarimétriques. Les variables cohérentes déduites de ces images permettent de mieux décrire les cibles observées et elles ont permis l’estimation de l’humidité du sol pour un sol nu. Toutefois, le potentiel d’utilisation de la polarimétrie pour des cibles couvertes de végétation est encore mal connu. L’objectif de ce projet est d’évaluer le potentiel de la polarimétrie pour l’inversion de l’humidité du sol en zone agricole à partir d’images RSO en bande C. La campagne SMAPVEX12 menée à l’été 2012 au Manitoba a permis l’acquisition simultanée d’images polarimétriques RADARSAT-2, ainsi que des conditions du sol et de la végétation pour des champs de blé. La rétrodiffusion radar, en polarisation linéaire ou circulaire, est très sensible à l’humidité du sol avant l’épiaison du blé. Après, la végétation domine le signal. La calibration du modèle semi-empirique des canaux linéaires de rétrodiffusion, développé par Gherboudj et al. (2011) n’a pu correctement représenter les relations de la rétrodiffusion avec les caractéristiques agricoles observées. L’information de phase conservée par le capteur de RADARSAT-2 permet l’extraction de variables polarimétriques telles que la différence de phase HH-VV et la hauteur de socle, l’anisotropie A et l’entropie H issues de la décomposition de Cloude-Pottier dont la sensibilité à l’humidité du sol sera étudiée. Des modèles empiriques simples, calibrés par régression linéaire multiple de termes utilisant de 2 à 6 variables polarimétriques, ont été développés et ont permis d’estimer l’humidité du sol sur 5 champs de blé pour toute leur période de croissance avec une erreur RMSD de 0,074 m³/m³ en expliquant plus de 53.5% (R2) de la variance des valeurs d’humidité du sol observées, contre une erreur de 0.098 m³/m³ et une variance expliquée de 19.0% pour un modèle empirique basé que sur les variables incohérentes.Abstract: Remote sensing has been widely researched toward estimation of soil conditions over agricultural fields. Monitoring of surface soil moisture mv would benefit many applications in hydrology, climatology, precision agriculture and risk reduction applied to meteorological events. C-band synthetic aperture radar (SAR) signal’s, such as that of RADARSAT-2, is sensitive to soil and vegetation characteristics. Backscattering coefficients obtained from those sensors allowed the estimation of mv by inverting empirical or semi-empirical models, under very strict conditions that limit their applicability. Many on-going or future missions provides polarimetric SAR images. However, the potential of polarimetric SAR sensors operated in c-band is not yet fully understood for soil moisture estimation over vegetated fields. This paper study the effects of soil and vegetation characteristics on polarimetric RADARSAT-2 images and proposes a simple empirical model based on polarimetric parameters extracted from RADARSAT-2 imagery to retrieve surface soil moisture (0-5 cm) over agricultural fields. The data used in this study was obtained during the SMAPVEX12 campaign, which occurred on the summer of 2012 between june 6th and july 17th in Manitoba, Canada. Fully polarimetric RADARSAT-2 images were acquired over 13 wheat fields over their whole growth cycle while their soil and vegetation conditions were monitored. Linear backscattering showed significant correlations for all polarizations before crops flowering. Sensitivity analysis of the extracted polarimetric variables to soil moisture demonstrated distinct correlations before and after the beginning of the crops flowering stage. The calibrated semi-empirical model proposed by Gherboudj et al. (2011) showed poor representation of the observed relationships between linear backscattering channels and crop conditions. The phase information, obtained by the RADARSAT-2 sensor, allowed extraction of polarimetric variables. Among those, phase difference HH-VV, the pedestal height and both the anisotropy H and entropy H obtained from Cloude-Pottier decomposition, showed significant correlations to soil moisture. A simple empirical model, calibrated with multiple linear regression from 2 to 6 polarimetric variables, allowed to retrieve soil moisture with a RMSD of 0,074 m³/m³ while it explained more than de 53.5% (R2) of observed soil moisture variability, while a simple linear model based only on incoherent variables could only estimate soil moisture with a RMSD of 0.098 m³/m³ and a R2 value of 19.0%

Observing and modeling multifrequency scattering of maize during the whole growth cycle

The objective of this paper is to carry out a systematic investigation about the sensitivity of radar to maize crop growth and soil moisture by considering a wide range of frequencies and angles and all linear polarizations. We show the results of a correlation study carried out on the data collected on a maize field at Suberg, in the Swiss region named Central Plain, by the multifrequency RAdio ScAtteroMeter (RASAM). This agricultural field was monitored over a long period of time at a wide range of frequencies and observation angles so that the correlation between the backscattering and crop height and the biomass and soil moisture was studied under several plant and observation conditions. Moreover, we describe some recent refinements applied to the vegetation scattering model developed at Tor Vergata University, Rome, Italy, and we evaluate the accuracy of extended comparisons between model outputs and RASAM signatures. The Tor Vergata model is finally applied to give a theoretical basis to the experimental correlation findings

Application Of Polarimetric SAR For Surface Parameter Inversion And Land Cover Mapping Over Agricultural Areas

In this thesis, novel methodology is developed to extract surface parameters under vegetation cover and to map crop types, from the polarimetric Synthetic Aperture Radar (PolSAR) images over agricultural areas. The extracted surface parameters provide crucial information for monitoring crop growth, nutrient release efficiency, water capacity, and crop production. To estimate surface parameters, it is essential to remove the volume scattering caused by the crop canopy, which makes developing an efficient volume scattering model very critical. In this thesis, a simplified adaptive volume scattering model (SAVSM) is developed to describe the vegetation scattering as crop changes over time through considering the probability density function of the crop orientation. The SAVSM achieved the best performance in fields of wheat, soybean and corn at various growth stages being in convert with the crop phenological development compared with current models that are mostly suitable for forest canopy. To remove the volume scattering component, in this thesis, an adaptive two-component model-based decomposition (ATCD) was developed, in which the surface scattering is a X-Bragg scattering, whereas the volume scattering is the SAVSM. The volumetric soil moisture derived from the ATCD is more consistent with the verifiable ground conditions compared with other model-based decomposition methods with its RMSE improved significantly decreasing from 19 [vol.%] to 7 [vol.%]. However, the estimation by the ATCD is biased when the measured soil moisture is greater than 30 [vol.%]. To overcome this issue, in this thesis, an integrated surface parameter inversion scheme (ISPIS) is proposed, in which a calibrated Integral Equation Model together with the SAVSM is employed. The derived soil moisture and surface roughness are more consistent with verifiable observations with the overall RMSE of 6.12 [vol.%] and 0.48, respectively

Determination of Soil Moisture and Vegetation Parameters from Spaceborne C-Band SAR on Agricultural Areas

Soil moisture is an important factor influencing hydrological and meteorological exchange processes at the land surface. As ground measurements of soil moisture cannot provide spatial-ly distributed information, remote sensing of soil moisture using Synthetic Aperture Radar (SAR) offers an alternative. To derive soil moisture from vegetated areas with SAR, the influ-ence of vegetation parameters on SAR backscatter must be considered, though. The first part of the study analyses the potential to use a qualitative soil moisture index from ERS-SAR with high spatial resolution that can be used without ground truth soil moisture and vegetation data. The index ranges from low to high soil moisture instead of giving absolute soil moisture values. The method is applied to agricultural areas in the catchment of the river Rur in Germany. The soil moisture index represents wetting and drying tendencies well when compared to precipitation records and behaves like in-situ soil moisture regarding its variabil-ity. The analysis of spatial patterns from the soil moisture index by using semivariograms re-veals that differences in management that result for example in differences in evapotranspira-tion from one to the next agricultural field, are the only influence on spatial patterns of soil moisture in the Rur catchment. This study confirms the applicability of a high-resolution soil moisture index for monitoring soil moisture changes and to analyze spatial soil moisture pat-terns. The soil moisture index could be used as input to hydrological models and could substi-tute antecedent precipitation, which needs precipitation stations, as a proxy to soil moisture. The second part of the study examines the capability of dual-polarimetric C-Band SAR data with high incidence angles from the Sentinel-1 satellites to derive soil moisture and vegetation parameters quantitatively. A processing scheme for Sentinel-1 Level-1 data is presented to produce images of different SAR observables that are compared to extensive ground meas-urements of soil moisture and vegetation parameters. It shows that soil moisture retrieval is feasible from bare soil and maize with an RMSE of 7 Vol%. From other land use types, dif-ferent vegetation parameters could be retrieved with an error of around 25 % of their range, in median. Neither soil moisture nor vegetation parameters could be derived from grassland and triticale due to the influence of the thatch layer and the missing of a clear row structure. Both grassland and triticale are in contrast to the other crops not sown in rows on our research fields. The analysis has shown that the incidence angle is of main importance for the capability of C-band SAR to derive soil moisture and that the availability of at least one co- and cross-polarized channel is important for the quantitative retrieval of land surface parameters. The dual-pol H2α parameters were not meaningful for soil moisture and vegetation parameter re-trieval in this study

Satellite remote sensing priorities for better assimilation in crop growth models : winter wheat LAI and grassland mowing dates case studies

In a context of markets globalization, early, reliable and timely estimations of crop yields at regional to global scale are clearly needed for managing large agricultural lands, determining food pricing and trading policies but also for early warning of harvest shortfalls. Crop growth models are often used to estimate yields within the growing season. The uncertainties associated with these models contribute to the uncertainty of crop yield estimations and forecasts. Satellite remote sensing, through its ability to provide synoptic information on growth conditions over large geographic extents and in near real-time, is a key data source used to reduce uncertainties in biophysical models through data assimilation methods. This thesis aims at assessing possible improvements for the assimilation of remotely-sensed biophysical variables in crop growth models and to estimate their related errors reduction on modelled yield estimates. Assimilated observations are winter wheat leaf area index (LAI) and grassland mowing dates derived respectively from optical (MODIS) and microwave (ERS-2) data. These observations have been assimilated in WOFOST and LINGRA growth models. Observing System Simulation Experiments (OSSE) allowed assessing errors reduction on yield estimates after assimilation for different situations of accuracy and frequency of remotely-sensed estimates and for different assimilation strategies, indicating expected improvements with the currently available and forthcoming sensors; it supports also guidelines for future satellite missions. A main finding is the fact that yield estimate improvements are only possible for assimilation strategies able to correct the possible phenological discrepancies between the remotely-sensed and the simulated data. These phenological shifts arise mainly from uncertainties on the parameters and initial states driving the phenological stages in the models but are also due, in some situations, to lack of pixel purity because of the medium resolution of sensors such as MODIS. This thesis also identifies some of the main sources of uncertainties and assesses their impact on assimilation performances. The impact of water content and biomass on SAR backscattering of grasslands is specifically assessed. The backscattering of grasslands increases with the increases of water content and decreases with the biomass in dry conditions. Based on these results, methodologies to classify grasslands according to land use (mowing or grazing) and to detect mowings are designed and demonstrated. A good classification accuracy is observed (overall accuracy around 80%). Results for mowings detection are a bit lower as half of the mowings are correctly identified but the methodology can be considered as promising in particular in the perspective of very dense SAR time series as currently acquired operationally by Sentinel-1.(AGRO - Sciences agronomiques et ingénierie biologique) -- UCL, 201