Sensitivity Analysis of Microwave Sensors to Various Soil Types and Their Soil Moisture Content

Understanding soil moisture content is extremely important to model areas for suitability analysis of different plantations and to predict natural disasters like landslides, floods, etc. It also influences hydrological and ecological processes. In the present work, a microwave sensing system with transmitter and receiver antennas in the C band is experimented with. The sensitivity of the sensor system is compared with the changes in soil moisture levels for different soil types available in the North Eastern Region of India using the laboratory-based setup. Suitable frequency determination for such a microwave-range soil moisture sensor is directly related to remote sensing satellite applications. The sensor system is found to be highly sensitive to soil moisture changes in various soil types. However, the determination of the exact frequency range that is suitable to detect soil moisture changes in different soil types is required for satellite remote sensing applications in the microwave range. Especially for the typical alluvial soil types of the Brahmaputra valley, a detailed sensitivity study using the microwave sensor is done in the current study. Thus, this paper presents the study results for determining the most suitable C-band frequency for soil moisture monitoring using active microwave sensors

Evaluating Consistency of Snow Water Equivalent Retrievals from Passive Microwave Sensors over the North Central U. S.: SSM/I vs. SSMIS and AMSR-E vs. AMSR2

For four decades, satellite-based passive microwave sensors have provided valuable snow water equivalent (SWE) monitoring at a global scale. Before continuous long-term SWE records can be used for scientific or applied purposes, consistency of SWE measurements among different sensors is required. SWE retrievals from two passive sensors currently operating, the Special Sensor Microwave Imager Sounder (SSMIS) and the Advanced Microwave Scanning Radiometer 2 (AMSR2), have not been fully evaluated in comparison to each other and previous instruments. Here, we evaluated consistency between the Special Sensor Microwave/Imager (SSM/I) onboard the F13 Defense Meteorological Satellite Program (DMSP) and SSMIS onboard the F17 DMSP, from November 2002 to April 2011 using the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) for continuity. Likewise, we evaluated consistency between AMSR-E and AMSR2 SWE retrievals from November 2007 to April 2016, using SSMIS for continuity. The analysis is conducted for 1176 watersheds in the North Central U.S. with consideration of difference among three snow classifications (Warm forest, Prairie, and Maritime). There are notable SWE differences between the SSM/I and SSMIS sensors in the Warm forest class, likely due to the different interpolation methods for brightness temperature (Tb) between the F13 SSM/I and F17 SSMIS sensors. The SWE differences between AMSR2 and AMSR-E are generally smaller than the differences between SSM/I and SSMIS SWE, based on time series comparisons and yearly mean bias. Finally, the spatial bias patterns between AMSR-E and AMSR2 versus SSMIS indicate sufficient spatial consistency to treat the AMSR-E and AMSR2 datasets as one continuous record. Our results provide useful information on systematic differences between recent satellite-based SWE retrievals and suggest subsequent studies to ensure reconciliation between different sensors in long-term SWE records

A Multi-temporal Analysis of AMSR-E Data for Flood and Discharge Monitoring during the 2008 Flood in Iowa

The objective of this work is to demonstrate the potential of using passive microwave data to monitor flood and discharge conditions and to infer watershed hydraulic and hydrologic parameters. The case study is the major flood in Iowa in summer 2008. A new Polarisation Ratio Variation Index (PRVI) was developed based on a multi-temporal analysis of 37 GHz satellite imagery from the Advanced Microwave Scanning Radiometer (AMSR-E) to calculate and detect anomalies in soil moisture and/or inundated areas. The Robust Satellite Technique (RST) which is a change detection approach based on the analysis of historical satellite records was adopted. A rating curve has been developed to assess the relationship between PRVI values and discharge observations downstream. A time-lag term has been introduced and adjusted to account for the changing delay between PRVI and streamflow. Moreover, the Kalman filter has been used to update the rating curve parameters in near real time. The temporal variability of the b exponent in the rating curve formula shows that it converges toward a constant value. A consistent 21-day time lag, very close to an estimate of the time of concentration, was obtained. The agreement between observed discharge downstream and estimated discharge with and without parameters adjustment was 65 and 95%, respectively. This demonstrates the interesting role that passive microwave can play in monitoring flooding and wetness conditions and estimating key hydrologic parameters

Development of soil moisture profiles through coupled microwave–thermal infrared observations in the southeastern United States

The principle of maximum entropy (POME) can be used to develop vertical soil moisture (SM) profiles. The minimal inputs required by the POME model make it an excellent choice for remote sensing applications. Two of the major input requirements of the POME model are the surface boundary condition and profile-mean moisture content. Microwave-based SM estimates from the Advanced Microwave Scanning Radiometer (AMSR-E) can supply the surface boundary condition whereas thermal infrared-based moisture estimated from the Atmospheric Land EXchange Inverse (ALEXI) surface energy balance model can provide the mean moisture condition. A disaggregation approach was followed to downscale coarse-resolution ( ∼ 25&thinsp;km) microwave SM estimates to match the finer resolution ( ∼ 5&thinsp;km) thermal data. The study was conducted over multiple years (2006–2010) in the southeastern US. Disaggregated soil moisture estimates along with the developed profiles were compared with the Noah land surface model (LSM), as well as in situ measurements from 10 Natural Resource Conservation Services (NRCS) Soil Climate Analysis Network (SCAN) sites spatially distributed within the study region. The overall disaggregation results at the SCAN sites indicated that in most cases disaggregation improved the temporal correlations with unbiased root mean square differences (ubRMSD) in the range of 0.01–0.09&thinsp;m3&thinsp;m−3. The profile results at SCAN sites showed a mean bias of 0.03 and 0.05 (m3&thinsp;m−3); ubRMSD of 0.05 and 0.06 (m3&thinsp;m−3); and correlation coefficient of 0.44 and 0.48 against SCAN observations and Noah LSM, respectively. Correlations were generally highest in agricultural areas where values in the 0.6–0.7 range were achieved.</p

Soil moisture and water stage estimation using precipitation radar

In south-western United States, soil moisture data is important for drought studies in the region which is experiencing a drought for many years, whereas in South Florida, water stage data is required by hydrologists to monitor the hydrological flow in wetlands. Soil moisture data and water stage data are not sufficiently available due to sparse monitoring stations. Installation of dense measuring stations over an extended area is costly and labor intensive. Therefore, there is a need to develop an alternative method of measuring soil moisture and water stage. Microwave remote sensing has proven to be a useful tool in the measurement of various surface variables from space. This research explores the capability of microwave remote sensing to measure soil moisture and water stage on the earth from space. Tropical Rainfall Measuring Mission Precipitation Radar (TRMMPR) provides the Ku -band backscatter measurements that are used to measure soil moisture and water stage. Models that relate soil moisture and water stage to TRMMPR backscatter (σ°) are developed. The dependence of σ° on the dielectrical and physical characteristics of the land surface is used as the basis of this research. The soil moisture content affects σ° by changing the dielectric constant of the surface whereas the vegetation density affects σ° by changing the physical characteristics of the surface. Vegetation density in the model is represented by Normalized Difference Vegetation Index (NDVI). Dependence of σ° on partial submergence of vegetation in inundated areas is used to measure water stage in wetlands of South Florida. The effects of the exposed vegetation above the water surface on the model are assessed by comparing two cases of model run3 (a) that includes NDVI in the model, and (b) that does not include NDVI in the model. Eleven years of data is used in this research where 75% of the data is used for calibration of the model and 25% of the data is used for validation. The estimated values of soil moisture and water stage are compared to the observed values and the performance of the models is assessed by calculating correlation coefficients, calculating root mean square errors, and plotting non-exceedance probability plots for the absolute error between observed and modeled values. The soil moisture and water stage models work reasonably well and are able to estimate soil moisture and water stage with low errors. The soil moisture model works better in low vegetated areas because low vegetation allows the incident radiation to penetrate through the canopy cover and provide measurements from underlying surfaces. The water stage model works better in shrublands where there are no tree trunks and the model has an immediate impact from the vegetation canopy. This research provides an alternate way of measurement of soil moisture and water stage using remote sensing

Estimation dynamique d'un indice d'humidité sur le bassin de la rivière La Grande avec l'aide de données de micro-ondes passives

Afin d'évaluer les variations d'humidité du sol dans les grands bassins, ce mémoire fait une validation de la méthode du Basin Wetness Index (Basist et al., 1998) sur le bassin La Grande en y adaptant le caractère dynamique innové par Temimi et al. (2007). Il en résulte un indice semi-empirique relié au pourcentage d'eau liquide contenu à la surface du sol, qui évolue selon les variations temporelles et hétérogénéités spatiales. Les micro-ondes passives de l'instrument SSM/I sont utilisées car elles sont sensibles aux variations de la constante diélectrique d'un sol mouillé et que la grande banque de données SSM/I permet un meilleur calage. Les résultats qualitatifs ainsi obtenus démontrent la sensibilité du BWI aux fluctuations hydrologiques. La réponse du BWI aux évènements pluvieux varie entre 5 à 10 jours. Toutefois, le BWI est dominé par un cycle saisonnier, suggérant l'influence dominante de la croissance de la végétation. L'approfondissement de ce lien pourrait parfaire le développement d'un indice du contenu en eau du sol

Besoin en eau et rendements des céréales en Méditerranée du Sud : observation, prévision saisonnière et impact du changement climatique

Le secteur agricole est l'un des piliers de l'économie marocaine. En plus de contribuer à 15% au Produit Intérieur Brut (PIB) et de fournir 35% des opportunités d'emploi, il a un impact sur les taux de croissance. Ces dernières sont affectées négativement ou positivement par les conditions climatiques et la pluviométrie en particulier. Lors des années de sécheresse, caractérisées par une baisse de la production agricole, en particulier celle des céréales, la croissance de l'économie marocaine a été sévèrement affectée et les importations alimentaires du royaume ont augmenté de manière significative. Dans ce contexte, il est important d'évaluer l'impact de la sécheresse agricole sur les rendements céréaliers et de développer des modèles de prévision précoce des rendements, ainsi que de déterminer l'impact futur du changement climatique sur le rendement du blé et leurs besoins en eau. Le but de ce travail est, premièrement, d'approfondir la compréhension de la relation entre le rendement des céréales et la sécheresse agricole au Maroc. Afin de détecter la sécheresse, nous avons utilisé des indices de sécheresse agricole provenant de différentes données satellitaires. En outre, nous avons utilisé les sorties du système d'assimilation des données terrestres (LDAS). Deuxièmement, nous avons développé des modèles empiriques de la prévision précoce des rendements des céréales à l'échelle provinciale. Pour atteindre cet objectif, nous avons construit des modèles de prévision en utilisant des données multi-sources comme prédicteurs, y compris des indices basés sur la télédétection, des données météorologiques et des indices climatiques régionaux. Pour construire ces modèles, nous nous sommes appuyés sur des algorithmes de machine learning tels que : Multiple Linear Regression (MLR), Support Vector Machine (SVM), Random Forest (RF) et eXtreme Gradient Boost (XGBoost). Enfin, nous avons évalué l'impact du changement climatique sur le rendement du blé et ses besoins en eau. Pour ce faire, nous nous sommes appuyés sur cinq modèles climatiques régionaux disponibles dans la base de données Med-CORDEX sous deux scénarios RCP4.5 et RCP8.5, ainsi que sur le modèle AquaCrop et nous nous sommes basés sur trois périodes, la période de référence 1991-2010, la deuxième période 2041-2060 et la troisième période 2081-2100. Les résultats ont montré qu'il y a une corrélation étroite entre le rendement des céréales et les indices de sécheresse liés à l'état de végétation pendant le stade d'épiaison (mars et avril) et qui sont liés à la température de surface pendant le stade de développement en janvier-février, et qui sont liés à l'humidité du sol pendant le stade d'émergence en novembre-décembre. Les résultats ont également montré que les sorties du LDAS sont capables de suivre avec précision la sécheresse agricole. En ce qui concerne la prévision du rendement, les résultats ont montré que la combinaison des données provenant de sources multiples a donné des meilleurs résultats que les modèles basés sur une seule source. Dans ce contexte, le modèle XGBoost a été capable de prévoir le rendement des céréales dès le mois de janvier (environ quatre mois avant la récolte) avec des métriques statistiques satisfaisants (R² = 0.88 et RMSE = 0.22 t. ha^-1). En ce qui concerne l'impact du changement climatique sur le rendement et les besoins en eau du blé, les résultats ont montré que l'augmentation de la température de l'air entraînera un raccourcissement du cycle de croissance du blé d'environ 50 jours. Les résultats ont également montré une diminution du rendement du blé jusqu'à 30% si l'augmentation du CO2 n'est pas prise en compte. Cependant, l'effet de la fertilisation au CO2 peut compenser les pertes du rendement, et ce dernier peut augmenter jusqu'à 27%. Finalement, les besoins en eau devraient diminuer de 13 à 42%, et cette diminution est associée à une modification de calendrier d'irrigation, le pic des besoins arrivant deux mois plus tôt que dans les conditions actuelles.The agricultural sector is one of the pillars of the Moroccan economy. In addition to contributing 15% in GDP and providing 35% of employment opportunities, it has an impact on growth rates that are negatively or positively affected by climatic conditions and rainfall in particular. During drought years characterized by a decline in agricultural production and in particular cereal production, the growth of the Moroccan economy was severely affected and the kingdom's food imports increased significantly. In this context, it's important to assess the impact of agricultural drought on cereal yields and to develop early yield prediction models, as well as to determine the future impact of climate change on wheat yield and water requirements. The aim of this work is, firstly to further understand the linkage between cereal yield and agricultural drought in Morocco. In order to identify this drought, we used agricultural drought indices from remotely sensed satellite data. In addition, we used the outputs of Land Data Assimilation System (LDAS). Secondly, to develop empirical models for early prediction of cereal yields at provincial scale. To achieve this goal, we built forecasting models using multi-source data as predictors, including remote sensing-based indices, weather data and regional climate indices. And to build these models, we relied on machine learning algorithms such as Multiple Linear Regression (MLR), Support Vector Machine (SVM), Random Forest (RF) and eXtreme Gradient Boost (XGBoost). Finally, to evaluate the impact of climate change on the wheat yield its water requirements. To do this, we relied on five regional climate models available in the Med-CORDEX database under two scenarios RCP4.5 and RCP8.5, as well as the AquaCrop model and we based on three periods, the reference period 1991-2010, the second period 2041-2060 and the third period 2081-2100. The results showed that there is a close correlation between cereals yield and drought indices related to canopy condition during the heading stage (March and April) and which are related to surface temperature during the development stage in January -February, and which are related to soil moisture during the emergence stage in November -December. The results also showed that the outputs of LDAS are able to accurately monitor agricultural drought. Concerning, cereal yield forecasting, the results showed that combining data from multiple sources outperformed models based on one data set only. In this context, the XGBoost was able to predict cereal yield as early as January (about four months before harvest) with satisfactory statistical metrics (R² = 0.88 and RMSE = 0.22 t. ha^-1). Regarding the impact of climate change on wheat yield and water requirements, the results showed that the increase in air temperature will result in a shortening of the wheat growth cycle by about 50 days. The results also showed a decrease in wheat yield up to 30% if the rising in CO2 was not taken into account. The effect of fertilizing of CO2 can offset the yield losses, and yield can increase up to 27 %. Finally, water requirements are expected to decrease by 13 to 42%, and this decrease is associated with a change in temporal patterns, with the requirement peak coming two months earlier than under current conditions