Mauro Holzman

Subsurface Soil Moisture Estimation by VI–LST Method

In this letter, the relationship between temperature vegetation dryness index (TVDI) from the Moderate Resolution Imaging Spectroradiometer and subsurface soil moisture (SM) over crop and native grassland of the Argentine Pampas is analyzed. High correlation (R2 > 0.69) between TVDI and SM measurements was found at different soil depths. In addition, we found that the potential of this index to reflect subsurface soil wetness fluctuations depends on root system depth, root distribution in the soil, and physical soil characteristics. Results indicate that thermal and reflectance data combination could be used to monitor subsurface SM below vegetated areas.

SMOS Level-2 Soil Moisture Product Evaluation in Rain-Fed Croplands of the Pampean Region of Argentina

A field campaign was carried out to evaluate the Soil Moisture (SM) MIR_SMUDP2 product (v5.51) generated from the data of the Microwave Imaging Radiometer using Aperture Synthesis (MIRAS) aboard the Soil Moisture and Ocean Salinity (SMOS) mission. The study area was the Pampean Region of Argentina, which was selected because it is a vast area of flatlands containing quite homogeneous rain-fed croplands, which are considered SMOS nominal land uses and hardly affected by radio-frequency interference contamination. Transects of ground handheld SM measurements were performed using ThetaProbe ML2x probes within four Icosahedral Snyder Equal Area Earth (ISEA) grid nodes, where permanent SM stations are located. The campaign results showed a negative bias of -0.02 m3m-3 between concurrent SMOS data and ground SM measurements, which means a slight SMOS underestimation, and a standard deviation of ±0.06 m3m-3. Additionally, a good correlation was obtained between the handheld SM measurements taken during the campaign and the permanent SM station data within a node, which pointed out that the station data could be used as reference data to evaluate the SMOS product over a longer temporal period. SMOS-retrieved data were also compared with station mean SM values from 2012 to 2014. A general SMOS underestimation of -0.05 m3m-3 was observed, with a standard deviation of ±0.04 m3m-3, which yields an uncertainty of ±0.07 m3m-3 for the SMOS product. Although the random error meets the SMOS missions goal of ±0.04 m3m-3, the product overall uncertainty is higher than that due to the significant dry bias, which is also found in other regions of the world.

Early Maize Yield Forecasting From Remotely Sensed Temperature/Vegetation Index Measurements

High and low soil moisture availability is one of the main limiting factors-affecting crops productivity. Thus, determination of the relationship between them is crucial for food security and support importing- exporting strategies. The aim of this work was to analyze the aptitude of temperature vegetation dryness index (TVDI) to forecast maize yield. MODIS/AQUA enhanced vegetation index and land surface temperature (LST) at 1 km were used to calculate TVDI and maize yield over a large agricultural area of Argentine Pampas. The comparison between TVDI and official yield statistics was carried out to derive regression models in two agro-climatic zones, obtaining linear and quadratic adjustments. The models account for between 73% and 83% of yield variability, with the best prediction in the humid zone. The RMSE values ranged from 14% to 19% of average yield. The bias showed a slightly higher difference between predicted and observed yield data in semi-arid zone. The models showed aptitude to estimate yield with reasonable accuracy 8-12 weeks before harvest. In addition, the TVDI-maize yield relationship and the impact of submonthly water stress were evaluated at field scale using yield measurements to ensure the analysis on maize. The highest

A method for soil moisture probes calibration and validation of satellite estimates

\text{R}^{2}

Estimating soil moisture and the relationship with crop yield using surface temperature and vegetation index

(0.61) was obtained using monthly values suggesting that the entire critical stage should be taken into account for yield forecasting. Although these results would not be directly extrapolated to other agricultural regions in the world, the proposed model is promising for forecasting spatial yield in other regions with poor data coverage several weeks before harvest.

Early assessment of crop yield from remotely sensed water stress and solar radiation data

Graphical abstract