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.

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.

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.