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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 R 2 (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.

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As the collection of soil moisture data is often costly, it is essential to implement dataworth analysis in advance to obtain a cost-effective data collection scheme. In previous data-worth analysis, the model structural error is often neglected. In this paper, we propose a robust data-worth analysis framework based on a hybrid data assimilation method. By constructing Gaussian process (GP) error model, this study attempts to alleviate biased data-worth assessments caused by unknown model structural errors, and to excavate complementary values of multisource data without resorting to multiple governing equations. The results demonstrated that this proposed framework effectively identified and compensated for complex model structural errors. By training prior data, more accurate potential observations were obtained and data-worth estimation accuracy was improved. The scenario diversity played a crucial role in establishing an effective GP training system. The integration of soil temperature into GP training unraveled new information and improved the data-worth estimation. Instead of traditional evapotranspiration calculations, the direct inclusion of easy-toobtain meteorological data into GP training yielded better data-worth assessment.