Soil moisture is a crucial parameter in integrated soil sciences, particularly in agriculture. Recognizing its significance, missions such as the NASA SMAP are dedicated to periodically provide global coverage of SM and brightness temperature information. However, the efficacy of satellite measurements hinges on rigorous testing across diverse biomes and conditions.With the objective of creating a SM database that can be used as a reference, in Central Valleys, Oaxaca, monitoring campaigns have been carried out in agricultural areas characterized by their semi-arid climate from 2021 to 2023. The comparison of information is done through a retrieval algorithm known as the τ-ω model that simulates brightness temperature with in-situ information. The study demonstrates how the heterogeneity of the study area and its climate makes the task difficult and causes the need for an adjustment in the in-put parameters.

The assessment of satellite products over agricultural areas has several challenges that get more complex in developing countries, particularly when exploiting active observation. The use of free-open access satellite images such as Sentinel-1 (S-1) and Sentinel-2 (S-2) images as data sources is considered a great alternative to minimize the high cost and time consumption of the field measurements. The composition of the rainfed agricultural zones depends on the vegetation and soil layers, in that sense, the ability to detect changes in surface parameters is related to the adequate representation of these components. The selection of vegetation descriptors to represent the vegetation changes is one of the main steps for accurate simulations over vegetated areas. Therefore, it is essential to assess the impact of the different vegetation indices (VIs) derived from optical information when included in active models. In this study, the Normalized Difference Vegetation Index (NDVI) and Specific Leaf Area Vegetation Index (SLAVI) were compared with the Vegetation Water Content calculated based on NDVI values (VWC-ndvi). In this paper, the Water Cloud Model (WCM) was used based on in-situ information collected during a field experiment conducted in a rain-fed agricultural area located in Central Mexico (THEXMEX-19). It was concluded that the use of the VW C-ndvi obtained 0.97, -0.11 dB, and 0.49 dB for correlation coefficient (r), Bias, and root mean squared difference (RMSD), respectively. Based on the results, this work highly recommends the use of the WCM and Oh model when evaluating different combinations of parameters for operational use, especially for agricultural purposes.

The monitoring of agricultural areas is challenging because the fast phenological changes. Satellite sensors are a powerful tool to monitor agricultural lands because of their capacity of cover large areas. Microwave satellite observations, particularly those acquired at frequencies < 10 GHz, are sensitive enough to capture changes in vegetation. The ESA Sentinel-1 mission, operating at C-band, has shown its ability to monitor agricultural areas because of its revisit time. In order to understand satellite C-band backscatter observations, there is still a need of insights about the selection of the modeling approach that accounts for the different effects involved in the backscatter from a scene. This study presents the comparison of two scattering models, the Water Cloud Model and a Coherent Scattering Model, over corn fields to understand the backscatter signal.

Satellite and airborne radars currently monitor agricultural regions on Earth. Corn is a globally important crop that may benefit from radar observations for estimating soil moisture (SM) and other quantities. Estimates of SM could be used to enhance crop yield and aid in weather prediction. A scattering model is needed, however, to accurately estimate these quantities. Historically, corn is a difficult crop to model at microwave frequencies, and only approximate models for it exist. Novel models based on full-wave electromagnetic solvers can be more accurate by accounting for multiple scattering among plant constituents, other adjacent plants, and the underlying soil surface. Such a model is computationally expensive, but the increased availability of computing resources may make it more feasible. This article presents a model for corn at L-band based on finite element method (FEM) simulations in conjunction with Monte Carlo methods to estimate polarimetric backscattering coefficients. The FEM simulation uses periodic boundary conditions to limit its size. The physical representation of the corn plants comes from data-based 3-D plant models. The results of simulations are validated with synthetic aperture radar (SAR) data obtained during the SM active passive validation experiment of 2012 (SMAPVEX12) experimental campaign. The estimated backscattering coefficients of the SAR data are within ±2 dB for all polarization channels. Validation is performed for two days within the experimental campaign. Good agreement is observed between the simulated and measured values. This result indicates that the model can give novel insights into the scattering characteristics of corn. Future work remains to build an invertible model for estimating SM from backscatter measurements.

An dequate resource management allows for better crop yields. Observing system simulations experiments (OSSE) have recently emerged as a useful tool to evaluate new observing systems before building or implementing them. Once the OSSE is validated, it allows characterizing the scenario and forecasting the behavior of physical variables for specific conditions such as agricultural areas. Satellite observations have shown greater sensitivity to vegetation and soil change according to different studies. Therefore, applying the OSSE methodology to validate satellite observations would be beneficial for our country.The application of this methodology proposes implement an OSSE methodology to monitor agricultural areas in the State of Tlaxcala, Mexico, using active-passive microwave sensors covering different spatial resolutions and different frequencies over an agricultural area in central Mexico. The coupled models used were the Land Surface Process (LSP), τ-ω model and the Water Cloud Model (WCM).When applying this methodology, differences of less than 15 K were observed in the case of the L-band simulations and less than 9 dB in the case of the C-band observations. However, the OSSE methodology showed a good approximation to the behavior of the SMAP and Sentinel-1 observations.

This study aims to discuss the use of L-band radar backscatter and phase information to analyze soil moisture (SM) and crop conditions, focusing on corn vegetation. While previous research has primarily utilized total backscatter magnitude to assess SM and crop conditions, this work explores the potential of phase information, which may be more sensitive to crop structure and suitable for monitoring crop dynamics. The methodology involves radar measurements using the University of Florida L-band Automated Radar System (UF-LARS), destructive vegetation sampling, and calibration techniques. Preliminary results demonstrate significant differences in CPD between bare soil and vegetated conditions, with phase variations associated with different growth stages of corn. This ongoing project indicates the potential of phase information to characterize vegetation growth stages for corn and similar crops.

Recently, the efforts to obtain Soil Moisture (SM) global measurements, at high spatial resolution, have been increasing several years ago. As a result, there are many techniques to downscale SM from remote observations of different sensors. However, we have focused on the algorithm which was developed in 2018 by Chakrabarti, using spatio-temporal correlations of high-resolution remote sensing products, bagged regression trees (BRT), and in-situ SM measurements. We computed the algorithm to downscale SMAP Level 2 SM products (L2_SM_P_E) at 9 km to 1 km over agricultural fields in Mexico using optical observations such as Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), and Landcover (LC), and precipitation measurements (PPT). We found that the algorithm correctly downscales soil moisture at 1 km over our study area, especially over the corn fields the RMSD is 0.037 m3/m3. Despite the limitations that we encountered when using optical ancillary products due to adverse weather conditions and the difference of spatial and temporal resolutions between each space-borne mission.

Soil moisture is a critical parameter that is relevant to several activities such as agriculture and disaster management (e.g., drought, floods, water stress periods, etc.) [1]. Thus, it has a considerable impact on life on Earth. Recently, researchers are counting on the advanced development of remote sensing (RS) technologies for soil moisture retrieval. RS techniques provided amounts of free high, spatial, and time series images that serve soil moisture retrievals at the agricultural field scale such as Synthetic Aperture Radar (SAR). Therefore, this work aims at evaluating the impact of different formulations to represent the soil contribution using synergistically both Sentinel-1 and Sentinel-2 images. This evaluation will reveal the impact of the different soil formulations to represent the backscatter from the ground within the Water Cloud Model (WCM). The formulations representing the soil contribution in this work are the linear equation and the Oh model. The evaluation experiments are set based on in-situ data collected from different fields in Huamantla, Central Mexico. The statistical evaluation of the different formulations of the soil contribution in the total backscatter from the WCM is obtained by comparing the simulations with satellite observations over the complete period. The best results of the evaluated formulations are recorded by the Oh model in the Alvaro site with bias and root mean squared difference (RMSD) values equal -0.0002 dB and $ - {1.49_{x{{10}^{ - 6}}}}$ dB for VV polarization and, 0.0011dB, and ${8.1452_{x{{10}^{ - 6}}}}$ dB for VH polarization. In contrast, the best results obtained by the linear equation are obtained in the Macario site with bias and RMSD values equal to 0.6622 dB and -0.724dB, for VV polarization and 1.2990 dB, and 1.2994 dB for VH polarizations. On the other hand, the total backscatter from the WCM combined with the Oh model outlined the highest accuracy during the vegetated period. In the Palafox-2 site, the combined model obtained a correlation coefficient (r), bias, and RMSD up to 0.965 dB and, -0.107 dB and 0.494 dB for VV polarization, respectively, and 0.916 dB, 0.572 dB, and 1.655 dB for VH polarization, respectively. The slight differences between the two surface scattering models within the WCM suggest that both formulations could be suitable to be implemented in a retrieval process, depending upon the available ancillary information.

The National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) satellite aims at estimating surface soil moisture at global scales. In order to validate the soil moisture retrieval algorithms, they need to be tested over different conditions worldwide. In response to this need, the Terrestrial Hydrology Experiment 2021 and 2022 in Mexico (THEXMEX-21 and -22) was conducted in Central Valleys, Oaxaca, Mexico over a twelve-month period. Ground crews collected soil moisture values, crop description, and biomass samples in support of these campaigns. The objective of these field experiments was to create a soil moisture network over a semi-arid agricultural area in Mexico and compare the SMAP soil moisture estimates with soil moisture measurements. The comparison between in-situ values and SMAP retrievals of soil moisture demonstrated that absolute soil moisture values can be delivered by satellite observations. SMAP soil moisture estimates followed the general trend observed during the field experiment.

One of the main socio-environmental problems of the current era is climate change, a problem that has arisen due to anthropogenic actions, which in most cases do not allow ecosystems to recover by leveling the excesses of pollutants. The causes of climate change will be different depending on the natural conditions and society in each region [1] [2]. This work arises from an existing project in both communities where soil moisture and precipitation are monitored during a certain period, with a measurement interval of 20 minutes, so direct measurement equipment is located in the community. Two field visits were made to both communities. The sensitization was carried out first by narrating a story of a small caterpillar that did not make correct use of its available resources.

A phenology-based coherent scattering model was used to estimate terrain backscatter at the C-band for growing corn. The scattering model accounted for combined effects from row structure and phenological changes in the plant. The study was done in central Mexico in the region of Huamantla, Tlaxcala using the dataset collected during the THEXMEX19 campaigns. Model simulations showed an RMSD of 4.75 dB when compared with Sentinel-1 observations at VV- polarization and, for RADARSAT-2 observations, an RMSD of 5.76 dB at VV-pol and 7.01 dB at HH-pol.

The need to remotely obtain Soil Moisture (SM) measurements has been a challenge in recent years. Worldwide, this generates a scientific interest on remote sensing field. As a result, there are many techniques to retrieve SM from remote observations of different sensors, for example, mission SMOS can retrieve soil moisture from Brightness Temperature (TB) measurements at coarse spatial resolution. However, these resolutions for agricultural purposes are not suitable, for this reason, we are focused on the assessment of SM retrievals from the improvement of TB products over agricultural fields with similar weather conditions and crop field characteristics. Initially, we obtained TB at 9km and 3km in H and V polarization from NSIDC over agricultural fields in Mexico. Later, we computed retrieval model to retrieve SM. Finally, we obtained RMSD equal to 0.1 and low Pearson correlation coefficients, such as 0.068 in 2018 at 3 km (the lowest value) and 0.5711 in 2019 at 9 km (the highest value). In addition, BIAS and ubRMSD are similar to each other. Nevertheless, they showed poor performance in the Huamantla crop fields. But this does not limit the application to other regions of Latin America.

Currently, for developing countries, the estimation of conditions in agricultural areas is provided with high uncertainty due to the lack of information. The implementation of new technologies allows the improvement in the management of agricultural resources. Advances in technology have made satellite remote sensing a powerful tool for agricultural monitoring due to its ability to observe changes in vegetation. However, access to accurate estimates of surface parameters from satellite information at a global level is reduced primarily because satellite platforms of space agencies such as NASA and ESA have be located at latitudes higher than 30°.en mainly using information from particular study sites. Therefore, these are only accurate in areas with characteristics similar to those for which the satellite mission was calibrated/validated. For this reason, this work proposes to calibrate and validate electromagnetic microwave models in an area of rainfed fields in central Mexico. The models used in this work were the τ-ω model (passive model) and the Water Cloud Model (active model). Both models were calibrated using in situ data collected over an agricultural area. Additionally, the characteristics of the SMAP and Sentinel-1 satellite missions were considered to calibrate the models. Comparison between observations and simulations will allow finding a difference of 10 K for the passive case and less than 1 db for the active case.

Agricultural drought periods are a phenomenon that affect crops production and health of ecosystems thereby economies suffer continuous imbalances. For this reason, scientific communities have been focused on accuracy global prediction and mitigation risk models. These models are fed with increasingly enhanced inputs such as TB, therefore the need arises to evaluate inputs. This work presents the relationship between Microwave Polarization Difference Index (MPDI) and Soil Water Deficit Index (SWDI), using TB at 9km of SMAP mission to identify drought periods over a rainfed agricultural area in Huamantla (Tlaxcala State in Central Mexico).

In this study, a data assimilation framework based on the Ensemble Kalman Filter was implemented including a soil-vegetation-atmosphere energy transfer (SVAT) model. The SVAT model has been calibrated with in-situ data in the central region of Mexico, with temperate subhumid climate. The soil moisture information from ten locations was scaled within a 36km satellite pixel. Both synthetic observations and SMAP SM retrieval were assimilated and they improved by 29% compared to open-loop simulations. Particularly, the assimilated soil moisture allows us to have a better characterization of periods of water stress for corn cultivation.

Satellite remote sensing provides information on near-surface soil moisture at a global scale, thus allowing for a broad range of studies, such as, the forecasting and monitoring of drought. Accurate knowledge of soil moisture (SM) is crucial in hydrology, micrometeorology, and agriculture, as it allows for the estimation of energy and moisture fluxes within the land surface. In accordance with the National Aeronautics and Space Administration, during April of 2021, 85% of the Mexican territory was affected by drought. In Mexico, the main tool for monitoring drought is the Drought Monitoring in Mexico, which uses an intensity scale to describe the intensity of drought within a given region. The scale is defined as follows: the level (D0) abnormally dry, (D1) moderate drought, (D2) severe drought, (D3) extreme drought, and (D4) exceptional drought. Domestic corn production for self-consumption and the agroindustry in Mexico are under threat because of the effects caused by the increase of droughts in terms of frequency, duration, and intensity. For instance, in 2020, corn production in the central state of Tlaxcala dropped by 40%, due to a long drought period from April to August. The local agricultural drought is an important condition to monitor, therefore, the European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) satellite was launched in 2009. This satellite has shown its potential in different applications, for instance, the calculation of indices to determine the vegetation's health that can also be used as drought indicators. In this paper, we present a Soil Water Deficit Index (SWDI) from the data obtained on the THEX-MEX'18 experiment.

The latest developments in radar mission concepts suggest that subdaily synthetic aperture radar will become available in the next decades. The goal of this study was to demonstrate the potential value of subdaily spaceborne radar for monitoring vegetation water dynamics, which is essential to understand the role of vegetation in the climate system. In particular, we aimed to quantify fluctuations of internal and surface canopy water (SCW) and understand their effect on subdaily patterns of L-band backscatter. An intensive field campaign was conducted in north-central Florida, USA, in 2018. A truck-mounted polarimetric L-band scatterometer was used to scan a sweet corn field multiple times per day, from sowing to harvest. SCW (dew, interception), soil moisture, and plant and soil hydraulics were monitored every 15 min. In addition, regular destructive sampling was conducted to measure seasonal and diurnal variations of internal vegetation water content. The results showed that backscatter was sensitive to both transient rainfall interception events, and slower daily cycles of internal canopy water and dew. On late-season days without rainfall, maximum diurnal backscatter variations of >2 dB due to internal and SCW were observed in all polarizations. These results demonstrate a potentially valuable application for the next generation of spaceborne radar missions.

The proper management of resources allows better yields from crops. Within the field of remote sensing, one of the sectors benefited is the agricultural sector because changes in biodiversity can be quantified through the observation and temporary evaluation of satellite images. For example, different studies have shown the potential of using information from the ESA Sentinel-1 satellite to monitor the growing crops. The backscatter observations obtained from Sentinel 1A and Sentinel 1B satellites showed greater sensitivity to the change in vegetation according to the correlation study, it is shown that the correlation between vegetation parameters and Sentinel-1 observations is greater than 0.8. The application of this methodology allows the understanding of the temporal variability over corn fields in the central zone of Mexico and allows seeing the potential of the Sentinel-1 constellation for the monitoring of natural resources. The application of this methodology allows the understanding of the temporal variability over corn fields in the central zone of Mexico and allows seeing the potential of the Sentinel-1 constellation for the monitoring of natural resources.

Since 2018, the Terrestrial Hydrology Experiment in Mexico (THExMEX-18) is being part of SMAP algorithm validation through soil moisture (SM), crop measurements, and biomass samples that were collected over a rainfed agricultural region of Huamantla, Tlaxcala. As a second part of this experiment, THExMEX-19 was carried out in whole crop season from March to December 2019. In order to help to validate to SMAP algorithm applying NASA's protocols, both joint efforts of National Polytechnic Institute and Center for Remote Sensing of the University of Florida have obtained these results in 2018 and 2019. In-Situ soil moisture measurements are compared with the enhanced soil moisture SMAP level 2 passive observation (SMAP L2_SM_P & SMAP L2_SM_P_E) and soil moisture SMAP level 2 and Sentinel-1 passive observation (SMAP L2_SM_SP). SMAP mission meets the requirement of SM retrievals with an unbiased root-mean-square difference (ubRMSD)3m-3) compared to in-situ measurements over agricultural fields. The L-band passive SM retrievals and in-situ measurements were evaluated in terms of four statistical metrics: root-mean-square difference (RMSD), bias, ubRMSD, and correlation coefficient (r).

Integrate information of soil moisture obtained through available satellite observations with low implementation cost can help to guarantee food security and sovereignty in Mexican production. With few reliable databases of the behavior of soil moisture during the growth of corn and other crops, the validation of satellite retrievals with Mexican field campaigns it is necessary. In this study, we present the calibration of a SVAT-LSP model during a complete growing season over a corn region in Central Mexico. In-situ soil moisture values and SVAT-LSP estimates of soil moisture were also compared with the SMAP L2SM product. The calibration of the SVAT-LSP model was carried out using the Monte Carlo methodology. The surface soil moisture from the calibrated SVAT-LSP model show an RMSE of 0.0258 m3/m3when compared with in-situ values. In contrast, an RMSE of 0.1331 m3/m3was obtained between in-situ values and SMAP L2SM retrievals.

Accurate knowledge of soil and crop conditions such as water content, biomass, and phenology is crucial in agriculture for estimating growth and productivity. Remote sensing observations at microwave frequencies are sensitive to various soil and crop characteristics. For example, both the active (radar) and passive (radiometer) microwave sensors measure radiation quantities that are functions of soil and vegetation dielectric constant and exhibit sensitivities to soil moisture (SM) and vegetation water content (VWC). In addition to SM, the quantities are also influenced by other land surface parameters such temperature, soil surface roughness, vegetation geometry. Brightness temperature (TB) is more sensitive to SM and less sensitive to surface roughness and vegetation geometry compared to radar backscattering ( σ0). Satellite passive observations have been widely used because of their high temporal resolutions (frequency of every 3 days), but these observations are available at coarse resolutions of about 25-40km. In contrast, satellite active observations from synthetic aperture radar (SAR) provide finer resolution (lower than 3 km), but low temporal resolution. However, with the recent availabilities of both Radarsat-2 and Sentinel-1, we have almost weekly observations combining both observations. In addition, upcoming launch of NISAR mission in 2021 will provide unprecedented opportunities to harness multifrequency observations at L-, S-, and C-bands. In this study, we investigate sensitivities of such multifrequency observations to soil and vegetation water content using current C-band SAR data and ground-based L-band data (in preparation for NISAR) obtained from field experiments. Active L-band observations are the most sensitive to SM variations even when the biomass in agriculture fields such as corn is high; in contrast, active C-band observations are more sensitive to vegetation.

Soil moisture (SM) is an important land surface variable for understanding the water cycle, ecosystem productivity, and linkages between water-carbon cycles. For agricultural applications, SM information is needed at higher resolutions (about 1km). In this study, coarse-scale remotely sensed SM at 36 km from NASA-SMAP was disaggregated to 1 km using high resolution auxiliary information such as land cover, precipitation, land surface temperature, NDVI for a growing season of corn in 2018 in Central Mexico (CM). The main objective is to evaluate a machine-learning based downscaling algorithm over an agricultural area with very limited in-situ observations of SM obtained during THExMEX-18. We found that overall, the downscaled moisture captured the dynamics during the growing season observed by the in-situ measurements.

The National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) satellite was launched in January 2015. In order to validate the soil moisture retrieval algorithms that fully exploit the unique capabilities of SMAP, the algorithms need to be tested over different conditions worldwide. In response to this need, the Terrestrial Hydrology Experiment 2018 in Mexico (THExMEX-18) was conducted in a rainfed agricultural region of Huamantla, Tlaxcala, Mexico over a six-month growing season. During the experiment, soil moisture, crop measurements, and biomass samples were collected. The objective of THExMEX-18 was to create a soil moisture network over an agricultural area in Mexico, understand the spatial distribution of soil moisture, and compare the SMAP soil moisture retrievals with in-situ measurements. This work details the field data collection as well as data calibration and analysis. A first comparison between in-situ data and SMAP retrievals of soil moisture is presented. It is demonstrated that absolute soil moisture values can be retrieved by satellite observations. SMAP soil moisture estimates closely follow dry down and wetting events observed during the field experiment.

Continuous monitoring of physical parameters such as soil moisture (SM) is crucial to improve food sustainability and risks mitigation. Because of the spatio-temporal variations in SM, satellite observations are an excellent tool to monitor its dynamic. Among different operational satellite missions, the NASA SMAP mission offers a unique opportunity to monitor SM worldwide thanks to its frequency band of operation (L-band). The main goal of this mission was the disaggregation of brightness temperature (TB) at 36 km to 9 km using backscatter images at 3 km. Although the SMAP mission was designed to collect simultaneously radar/radiometer observations, due to a failure in the radar in July 2015, only the radiometer continues working. Among different options to compensate the lack of information from the L-band radar, the exploitation of C-band active information with L-band passive observations to disaggregate TBand produce SM at fine resolution has been proposed. Recently, the NASA SMAP team delivered a new SM product using this approach. However, this new product has not been validated over forested areas yet. In this work, we present the results of implementing a disaggregation algorithm based on the baseline SMAP downscaling approach over a tropical forest located in Southern Mexico, using information from the SMAP radiometer and Sentinel-1 images.

The monitoring of tropical ecosystems is complex due to the type of terrain and adverse weather conditions present during most of the year. This paper proposes the development and implementation of a methodology based on RADARSAT-2 images and a semi-empirical model to obtain surface parameters in a tropical forest. The model takes into consideration the sensor configuration, and vegetation and soil parameters to represent the behavior of the wave in the scene. To retrieve the surface parameters, the model is implemented in an optimization framework using all polarizations (HH, HV, VH and VV). Finally, as outputs of the optimization process, we obtain the trunk diameter (Dt), the crow height (hc), and the optical penetration depth (τ). By using τ, the vegetation water content (VWC) is estimated. The accuracy of this methodology is about 83% when compared to ground data.

Applying various mathematical and modeling methods, a tsunami hazard map was made for the coasts of the municipality of Manzanillo in Colima, Mexico; This map is intended to cover the risk of a tsunami produced by hypothetical earthquakes with magnitudes of Mw 6-Mw 9.5 with epicenter at 19.0192 N-104.4504 W. This area has been chosen as it stands out as one of the most historically affected areas by this type of disasters. puts at risk its tourist and habitable zone, which has positioned itself as one of the most important economic zones of the Mexican Pacific.

Soil moisture (SM) is an important land surface variable for understanding the water cycle, ecosystem productivity, and linkages between water-carbon cycles. For SM studies, observations at microwave frequencies <; 10 GHz are more desirable due to larger penetration depths. The NASA Soil Moisture Active/Passive (SMAP) mission provides global observations of SM using passive sensor at L-band. Current active C-band systems such as Sentinel 1 have shown potentialities to estimate SM and improve spatial resolution of SM data from passive sensors. In addition to the SM sensitivity, radar backscatter is highly sensitive to roughness of soil surface and scattering within the vegetation. Despite much progress in the development of backscattering models, there is still a gap in validating such models under dynamic vegetation conditions such agricultural crops. The goal of this study is to calibrate coherent and incoherent backscattering models for corn and soybean using Sentinel 1 and Radarsat-2 observations.

In this paper, we developed and evaluated a phenology-based coherent scattering model to estimate terrain backscatter at the L-band for growing corn. The scattering model accounted for combined effects from periodicity in soil and vegetation, and changes in plant structure and phenology. The model estimates were compared with observations during the two growing seasons in North Central Florida. The unbiased average root-mean-square (rms) differences between the model and observations decreased from 5 to 1.31 dB when these combined effects were included. During the early stage, direct scattering from soil was the primary scattering mechanism, and as the vegetation increased, the interactions between stems and soil became the dominant scattering mechanism. The most sensitive soil parameters were moisture content and rms height, and vegetation parameters were the widths of stems, leaves, and ears, and the stem water content. This paper demonstrates that it is necessary to consider periodicity and plant structural effects in algorithms to retrieve realistic soil moisture in agricultural terrain.

Soil moisture (SM) is an important land surface variable for understanding the water cycle, ecosystem productivity, and linkages between water-carbon cycles. For SM studies, observations at L-band frequencies are more desirable due to larger penetration depths. The NASA Soil Moisture Active/Passive (SMAP) mission includes active and passive sensors at L-band to provide global observations of SM. The active observations are available from April-July 2015. In addition to the SM sensitivity, radar backscatter is highly sensitive to roughness of soil surface and scattering within the vegetation. Despite much progress in the development of backscattering models, there is still a gap in validating such models under dynamic vegetation conditions such agricultural crops. The goal of this study is to validate an incoherent model using season-long active observations over a soybean field at high temporal resolution during the SMAPVEX16-MicroWEX experiment.

Soil moisture (SM) is an important land surface variable for understanding the water cycle, ecosystem productivity, and linkages between water-carbon cycles. For SM studies, observations at L-band frequencies are more desirable due to larger penetration depths. The NASA Soil Moisture Active/Passive (SMAP) mission includes active and passive sensors at L-band to provide global observations of SM. The active observations are available from April-July 2015. In addition to the SM sensitivity, radar backscatter is highly sensitive to roughness of soil surface and scattering within the vegetation. Despite much progress in the development of backscattering models, there is still a gap in validating such models under dynamic vegetation conditions such agricultural crops. The goal of this study is to improve a coherent model and evaluate it using season-long active observations at high temporal resolution during the SMAPVEX16-MicroWEX experiment.

The volcanic products are deposited in the atmosphere as a result of volcanic eruptions. These materials are dispersed by the wind and can be deposited on the surface in airport facilities and the finest fraction can remain long affecting airspace. Using tools such as wind patterns studio in height and track volcanic clouds with satellite images can identify areas of high probability of being contaminated with ash depending on the season and the intensity of the eruption. In this work two patterns of ash dispersion by wind are identifiedNovember to May transporting the ash has a higher probability NE to the ESE direction. During the months of July to September transporting ash would displace mainly towards the SW to W, with the months of May and October transition.

Forested areas have been the ecosystem more impacted by anthropogenic activities as agriculture, livestock, etc. Particularly, tropical forests host a large diversity of flora and fauna. Southern Mexico, Guatemala, and Belize own the second larger area worldwide of tropical forests after the Amazonas. Despite the importance of this ecosystem, there is still a lack of information, and then, an understanding of the local dynamics. This paper aims at reporting on two field campaigns conducted at Calakmul, Southern, Mexico, to characterize soil and vegetation during rainy and dry seasons. Simultaneously to the field campaigns, Radarsat-2 images were acquired. Significant changes are observed between the rainy and dry seasons; primarily, because of the loss of water content during the dry season for both soil and vegetation.

In this paper, bias correction approaches are investigated to understand their impact on assimilating active and/or passive microwave observations on near-surface soil moisture (SM) estimates. Synthetic and field observations were assimilated in a soil-vegetation-atmosphere transfer model linked with an integrated active-passive model at L-band for bare soil. The two bias correction methods included in this study are the online bias correction with feedback (BCWF) with extended implementation with nonlinear observation operators and the simultaneous state parameter (SSP) update. New equations for BCWF were derived for the case of nonlinear observation operators because current versions of this approach were not applicable for improving SM by assimilating microwave observations. In SSP, the bias is compensated by tunning the values of the parameters. The two approaches resulted in similar accuracy for improving SM estimates compared with the uncorrected estimates. SSP showed the highest certainty for both synthetic and field observations. Using the bias correction methods, the mean estimates of SM improved by up to 88%, 87%, and 94%, when passive, active, and active-passive synthetic observations were assimilated, respectively, compared with the open-loop estimates. In contrast, when assimilating field observations from the Eleventh Microwave Water Energy Balance Experiment, the mean estimates of SM improved by up to 44%, 18%, and 48%, when passive, active, and active-passive observations were assimilated, respectively, compared with open-loop estimates. The decrement in improving the SM estimates suggests sources of uncertainty other than those from model parameters and forcings.

Volcano monitoring is developed mainly with remote sensing techniques. In the case of SO2, it has been strongly used both surface techniques, such as COSPEC, and satellite techniques, such as MODIS. The two techniques differ in their measurement principle presenting advantages and disadvantages to each other. The purpose of this work is to show the synergy of both remote sensing methods in order to utilize information derived from these two techniques for continuous volcano monitoring. In this communication, we propose a methodology to correct any lag between COSPEC measurements and MODIS images at either time or space based on the wind speed and difference of the exact time of acquisition between the two techniques. The difference observed between COSPEC data and MODIS estimates is removed by using an exponential regression function derived from simultaneous observations using both sensors.

In this paper, we show the application of a coherent model on a tropical forest for studying the backscattering coefficient. The study site is located at southern Mexico. The radar configuration considered is C-band, full polarization and the range of incidence angle is the 20°-50°. As from result obtained we applied the linear regression for a build a simplified model.

Oil industry represents one of the main incomes for many countries. However, this industry is not exempt of accidents such as oil spills. Satellite remote sensing is a useful tool in delineating polluted areas due to its characteristic to cover large areas. This paper proposes a methodology combining information from both optical (Landsat) and radar (Envisat-ASAR) images to delineate polluted soils by hydrocarbons in the coast of Paraiso, Tabasco, Mexico. Landsat images identified polluted areas over the beach (bare soil); nevertheless, they did not reach the soil under vegetation conditions. In contrast, radar images did not indentify polluted areas in the beach mainly because of the extreme dry conditions in the soil. But, in vegetated areas, the radar penetrated the vegetation cover and identified the polluted soils. Both optical and radar information showed complementarities to delineate polluted areas under heterogeneous conditions.

In this paper, L-band microwave observations were assimilated using the ensemble Kalman filter to improve root-zone soil moisture (RZSM) estimates from a coupled soil vegetation atmosphere transfer (SVAT)-vegetation model linked to a forward microwave model. Simultaneous state-parameter updates were performed by assimilating both synthetic and field observations during a growing season of sweet corn every three days, matching the temporal coverage of observations from the Soil Moisture and Ocean Salinity and Soil Moisture Active Passive missions. The sensitivities of parameters to the states were investigated using the information-theoretic measure of conditional entropy. Among the soil parameters, the pore-size index (λ) was the most sensitive to brightness temperatures (TB) during the early and midgrowth stages, while porosity (φ) was the most sensitive toTBduring the reproductive stage. In the microwave model, the soil roughness parameters, root mean square (RMS) height (r), and correlation length (l) were the most sensitive during the early and mid stages, while the vegetation regression parameter (b) was the most sensitive during the reproductive stage. In the synthetic experiment, assimilation ofTBprovided RMS error reductions in RZSM estimates of 70% compared to open loop estimates. Minimal variations in performance were observed across different stages of the season during the synthetic experiment. However, when field observations ofTBwere assimilated, significant differences in RZSM estimates were observed during different growth stages. Maximum RMS difference (RMSD) reductions in RZSM estimates of 33.3% were observed compared to open loop estimates during the early stages, while improvements of 4.8% and 16.7% were observed in the mid- and reproductive stages, respectively. Further analyses of assimilation with field observations also suggest some improvements in the SVAT model are needed for moisture transport immediately following the precipitation/irrigation events. In the microwave model, the linear vegetation formulation for estimating canopy opacity, parameterized byb, was inadequate in capturing the complexities inTBduring stages of high vegetative and reproductive growth rates.

To achieve a soil moisture estimation (ms) in an accurate way is crucial to understand the water cycle response and avoid traditional runoff estimations where the msis assumed as a constant value. The aim of this study is the implementation of optical-radar images into a model to estimate msin the Zapotes Lagoon System in Tabasco, Mexico. The satellite images used were Landsat TM and ETM+ sensors, Envisat and also in situ measurements and MDT were available. The field measurements at the soil profile showed a clear pattern of the water movement into the basin corresponding to the lower parts of the system (lagoons). The land use analysis obtained with the optical images indicated a strong change in the floodplain due to the construction of protect barriers around the Villahermosa city losing its hydrological capacity. This allows the identification of three main covers (soil, vegetation and air) that were monitored and feed to the model (MIMICS) in order to estimate iris using the Envisat image. Results provided an empirical equation that relates the m, with the backscattering coefficient.

The impact of incoherent and coherent formulations on estimates of terrain backscatter (σterrain0) at L-band for a growing season of corn is examined. The average root mean square difference (RMSD) between the two formulations over the growing season ranged between 3-4 dB, with higher RMSDs at HH polarization (pol), indicating the presence of coherent effects. In the incoherent model, the direct scattering from stems was the primary mechanism, while in the coherent formulation, the interactions between the stems and soil were the primary mechanisms due to the coherent effects. Both incoherent and coherent formulations estimated equally high sensitivities of σterrain0to soil moisture (SM) during early stage under low vegetation conditions. During the early and mid stages, the σterrain0estimated by both formulations exhibited higher sensitivities during dry conditions than wet conditions. In contrast, during the reproductive stage, the σterrain0by the incoherent formulation was more sensitive to the SM at wet conditions than at dry conditions. Based upon the ALOS/SMAP accuracy for σterrain0, the incoherent formulation exhibited the highest sensitivity during the early stage with detection of SM changes as low as 2 vol% for dry condition, whereas the coherent formulation exhibited the highest sensitivity during the mid stage with detection of SM changes as low as 2.5 vol%. The results of this study suggest that the coherent effects should be considered for defining accuracy of SM estimation algorithms for corn at L-band.

A coherent scattering formulation is developed for radar remote sensing of Sahelian grassland. This African vegetation is mainly composed of annual grass and shrubs. In the proposed procedure, first, atemporalmodel for generation of grass and shrub structures, which includes important realistic botanical information, is implemented. Because we develop a coherent scattering model, preserving the relative position of plant elements in a statistical manner as accurately as possible is very important. Shrubs are reproduced using cylindrical elements which represent trunks, branches, and thin green stems that function as leaves for these shrubs. Their crown shape is highly irregular, but for the most part can be encompassed in an ellipsoidal or cylindrical volume; on the other hand, the grass is represented as a set of cylindrical stalks and blade leaves. The scattered power from each grass element is added because multiple scattering among adjacent elements can be neglected at microwave frequencies. We calculate the soil scattering using the Integral Equation Method and neglect the soil volume scattering which may become significant for dry soil condition at high incidence angles. Backscatter statistics are acquired via a Monte Carlo simulation over a large number of realizations. The accuracy of the model is verified using measured data acquired by the C-band environmental satellite advanced synthetic aperture radar instrument at different incident angles.

In this paper, the applicability of a procedure for retrieval of vegetation parameters using a coherent scattering model that considers the botanical properties of Sahelian grassland and a stochastic optimization algorithm is studied. This African vegetation is mainly composed of shrubs and grass. Since the coherent scattering model is computationally time-consuming, a simplified empirical model is constructed by fitting of simulation results obtained by the scattering model. Inputs to the empirical model are the sensitive parameters that, for the studied class of vegetation, are the soil moisture content, grass density, and grass moisture content. The model outputs are the polarimetric backscattering coefficients as a function of the incidence angle. Employing the empirical model and a genetic algorithm, a search routine is implemented to estimate the biophysical parameters of the African vegetation from a data set of backscattering coefficients. The estimation of Sahelian-grassland parameters using the set of C-band HH-polarized measured data shows that this procedure achieves good agreement with the ground-truth data.

In recent years a special emphasis has been placed on the retrieval of physical parameters from polarimetric radar at microwave frequencies in many research programs. In this paper, we adapted a technique based on an empirical model and a genetic algorithm, and verify its applicability for a complex class of vegetation within a wide temporal interval. This complex class of vegetation is Sahelian grassland which is mainly composed of annual grass and shrubs. The proposed retrieval algorithm is conformed of 3 main steps: (1) Identification of sensitive parameters, (2) Development of the empirical model, and (3) Implementation of a genetic algorithm for the inverse process. For this class of vegetation the sensitive parameters are: the soil moisture content ms, the grass density D, and the grass moisture content mv. When applying the retrieval algorithm to simulated radar responses, a great agreement (an error of 6% when estimating the soil moisture content, 13% for the grass density, and 18% for the grass moisture content in the adult-plant stage) is observed between input parameters and estimated ones.

A coherent scattering formulation is developed for radar remote sensing of Sahelian grassland. This African vegetation is composed of shrubs and annual grass. The proposed model includes a vegetation generator tool in order to create vegetation structure with realistic architectures and botanical information. This is important in the development of the coherent scattering model, since the relative position of plant elements needs be preserved as accurately as possible. To correctly account for the coherent attenuation through the crown layer, the crown shape of shrubs must be considered. The crown shape is highly irregular, but for the most part can be encompassed in an ellipsoidal or cylindrical volume depending on the ground truth data. Thus, the extinction of the coherent wave is then calculated only when traveling within the crown volume. On the other hand, the grass generator models the grass as a set of cylindrical stalks and blade leaves arranged in a semi- deterministic fashion. Since grass blades are thin, multiple scattering among adjacent elements can be neglected at microwave frequencies. Backscatter statistics are acquired via a Monte Carlo simulation over a large number of realizations. Depending on the season, it is shown that contribution from soil and grass are the dominant components of the overall backscatter.

The validity of a coherent Sahelian-grassland scattering model is determined by comparing the model predictions with satellite measurements of a representative site. This model considers the realistic botanical structure of grassland. The site Agoufou, located in the Northern Mali, was selected as the test target. This site is governed by a semi-arid tropical climate. Its vegetation is mainly composed of shrubs and annual grass. HH polarization backscattering data was collected over an entire growing season at different incidence angles by means of the ENVISAT ASAR. Simulations provided by the coherent model show a good agreement with measured data having a correlation coefficient equal to 0.92. Model predictions show that the HH polarization component is higher than the W polarization component during all growing season. Significant parameters are shown to be the grass density, the soil moisture content and the grass moisture content. The most sensitive parameter is the ground soil moisture content. Moreover, it is observed that the variation of the backscattering coefficient for all parameters can be represented by a linear regression function.