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.

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.

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.