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.

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.

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.

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.

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.

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.

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.

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.

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.