Indicators based on evapotranspiration (ET) provide useful information about surface water status, response of vegetation to drought stress, and potential growth limitations. The capability of ET-based indicators, including actual ET and the evaporative stress index (ESI), to predict crop yields of spring barley and winter wheat was analyzed for 33 districts of the Czech Republic. In this study, the ET-based indicators were computed using two different approaches: (i) a prognostic model, SoilClim, which computes the water balance based on ground weather observations and information about soil and land cover; (ii) the diagnostic Atmosphere–Land Exchange Inverse (ALEXI) model based primarily on remotely sensed land surface temperature data. The capability of both sets of indicators to predict yields of spring barley and winter wheat was tested using artificial neural networks (ANNs) applied to the adjusting number and timeframe of inputs during the growing season. Yield predictions based on ANNs were computed for both crops for all districts together, as well as for individual districts. The root mean square error (RMSE) and coefficient of determination (R²) between observed and predicted yields varied with date within the growing season and with the number of ANN inputs used for yield prediction. The period with the highest predictive capability started from early-June to mid-June. This optimal period for yield prediction was identifiable already at the lower number of ANN inputs, nevertheless, the accuracy of the prediction improved as more inputs were included within ANNs.The RMSE values for individual districts varied between 0.4 and 0.7 t ha^–1 while R² reached values of 0.5–0.8 during the optimal period. Results of the study demonstrated that ET-based indicators can be used for yield prediction in real time during the growing season and therefore have great potential for decision making at regional and district levels.

基于蒸散量 (ET) 的指标可提供有关地表水状况、植被对干旱胁迫的响应以及潜在的生长限制的有用信息。分析了基于 ET 的指标（包括实际 ET 和蒸发胁迫指数 (ESI)）对捷克共和国 33 个地区春大麦和冬小麦作物产量的预测能力。在这项研究中，基于 ET 的指标是使用两种不同的方法计算的：(i) 预测模型 SoilClim，它根据地面天气观测和土壤和土地覆盖信息计算水平衡；(ii) 主要基于遥感地表温度数据的诊断性大气-土地交换逆 (ALEXI) 模型。使用人工神经网络 (ANN) 测试了两组指标预测春大麦和冬小麦产量的能力，该网络应用于生长季节期间输入的调整数量和时间范围。对所有地区以及个别地区的两种作物都计算了基于 ANN 的产量预测。均方根误差 (RMSE) 和决定系数 (R² ) 观察到的和预测的产量之间随着生长季节内的日期和用于产量预测的人工神经网络输入的数量而变化。预测能力最强的时期是从6月初到6月中旬。这个产量预测的最佳时期已经可以在较少数量的 ANN 输入中识别出来，然而，随着更多的输入包含在 ANN 中，预测的准确性提高了。个别地区的 RMSE 值在 0.4 和 0.7 t ha ^–1之间变化，而 R ²在最佳时期达到 0.5-0.8 的值。研究结果表明，基于 ET 的指标可用于在生长季节实时预测产量，因此在区域和地区层面的决策中具有巨大潜力。