Description: Agriculture monitoring and yield estimation are important for food security, both regionally and globally.Understanding the year-to-year variability in crop yield and its relationship to meteorological conditions isparticularly important for several regions where yield is highly dependent on changing environmental factors.For example, wheat yields in India have been steadily increasing since the 1960s and 1970s due to benefits ofthe Green Revolution, but in recent years the wheat yield has been unstable as major crop yield losses wereattributed to unfavorable meteorological conditions. Modeling the effects of various environmental variables canbe challenging, as their impact on the final yield is complex and varies depending on their intensity and thecrop growth stage at which they occur (e.g., moderate rainfall is beneficial for crops, but extensive and untimelyrainfalls can lead to huge yield losses).In this work, we exploit interconnections between meteorological conditions and satellite data on vegeta-tion during the whole growing season, and their simultaneous impact on wheat yield in the Wheat Belt inIndia. We use GLDAS 2.1 data as the meteorological input and MODIS data for the vegetation remote sensinginput. Adding satellite information on crop is crucial for yield estimation, as it carries information on both cropphenology, as well as the crop response to the meteorological conditions. We apply machine learning algorithms(e.g., convolutional neural networks, CNNs) that can model non-linear processes and can extract importantfeatures in the multivariate time series automatically from data, without prior knowledge or human effort in featuredesign. By doing so, we do not force assumptions on which time is the most important for the final crop yieldand we can include in the analysis the whole time series of multiple input variables at a high temporal resolution.Furthermore, we analyze the CNNs in terms of important features and crucial time windows for yield estimation,which shows that they the vary across space and time. By combining meteorological and satellite vegetation datawith CNNs this work may help to disentangle the complex interactions between the features in the time series ofthe input data and the wheat yield.

Description: Forecasting crop yields is becoming increasingly important under the current context in which food security needs to be ensured despite the challenges brought by climate change, an expanding world population accompanied by rising incomes, increasing soil erosion, and decreasing water resources. Temperature, radiation, water availability and other environmental conditions influence crop growth, development, and final grain yield in a complex non-linear manner. Machine learning (ML) techniques, and deep learning (DL) methods in particular, can account for such non-linear relations between yield and its covariates. However, they typically lack transparency and interpretability, since the way the predictions are derived is not directly evident. Yet, in the context of yield forecasting, understanding which are the underlying factors behind both a predicted loss or gain is of great use. Here, we explore how to benefit from the increased predictive performance of DL methods while maintaining the ability to interpret how the models achieve their results. To do so, we applied a deep neural network to multivariate time series of vegetation and meteorological data to estimate the wheat yield in the Indian Wheat Belt. Then, we visualized and analyzed the features and yield drivers learned by the model with the use of regression activation maps. The DL model outperformed other tested models (ridge regression and random forests) and facilitated the interpretation of variables and processes that lead to yield variability. The learned features were mostly related to the length of the growing season, temperature, and light conditions during the growing season. For example, our results showed that high yields in 2012 were associated with low temperatures accompanied by sunny conditions during the growing period. The proposed methodology can be used for other crops and regions in order to facilitate application of DL models in agriculture.

Description: Satellite sun-induced chlorophyll fluorescence (SIF) has emerged as a promising tool for monitoring growing conditions and productivity of vegetation. However, it still remains unclear the ability of satellite SIF data to predict crop yields at the regional scale, comparing to widely used satellite vegetation index (VI), such as the Enhanced Vegetation Index (EVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS). Additionally, few attempts have been made to verify if SIF products from the new Orbiting Carbon Observatory-2 (OCO-2) satellite could be applied for regional corn and soybean yield estimates. With the deep neural networks (DNN) approach, this study investigated the ability of OCO-2 SIF, MODIS EVI, and climate data to estimate county-level corn and soybean yields in the U.S. Corn Belt. Monthly mean and maximum SIF and MODIS EVI during the peak growing season showed similar correlations with corn and soybean yields. The DNNs with SIF as predictors were able to estimate corn and soybean yields well but performed poorer than MODIS EVI and climate variables-based DNNs. The performance of SIF and MODIS EVI-based DNNs varied with the areal dominance of crops while that of climate-based DNNs exhibited less spatial variability. SIF data could provide useful supplementary information to MODIS EVI and climatic variables for improving estimates of crop yields. MODIS EVI and climate predictors (e.g., VPD and temperature) during the peak growing season (from June to August) played important roles in predicting yields of corn and soybean in the Midwestern 12 states in the U.S. The results highlighted the benefit of combining data from both satellite and climate sources in crop yield estimation. Additionally, this study showed the potential of adding SIF in crop yield prediction despite the small improvement of model performances, which might result from the limitation of current available SIF products. The framework of this study could be applied to different regions and other types of crops to employ deep learning for crop yield forecasting by combining different types of remote sensing data (such as OCO-2 SIF and MODIS EVI) and climate data.