Evaluating a Global Soil Moisture dataset from a Multitask Model (GSM3 v1.0) for current and emerging threats to crops
Evaluating a Global Soil Moisture dataset from a Multitask Model (GSM3 v1.0) for current and emerging threats to crops
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评估多任务模型 (GSM3 v1.0) 的全球土壤湿度数据集，了解当前和新出现的农作物威胁
DOI：
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Chaopeng Shen
中科院分区：
文献类型：
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作者：
Jiangtao Liu;David Hughes;F. Rahmani;K. Lawson;Chaopeng Shen
。气候变化威胁着我们为不断增长的人口种植粮食的能力。全球范围内同时发生干旱和洪水，其中非洲受到的影响最大。在非洲等监测不足的地区需要高质量的土壤湿度预测。然而，目前尚不清楚全球土壤湿度过程是否足够相似，足以让我们的模型在未监测的区域保持准确性。我们提出了一种多任务长短期记忆 (LSTM) 模型，可同时从全球卫星和现场土壤 15 湿度数据中学习。该模型在随机空间抵抗模式和大陆抵抗模式下进行评估（在某些大陆上进行训练，在不同的大陆上进行测试）。该模型优于当前的地表模型、卫星产品和候选机器学习模型，随机空间保留测试的全局中值相关性达到 0.792。它在非洲和澳大利亚的表现出人意料地好，即使我们将它们的站点排除在训练集中，也显示出很高的相关性，但在发生快速变化的阿拉斯加 20 表现相对较差。在除一个大陆（亚洲）之外的所有大陆中，多任务模型在最坏情况场景测试中的表现优于土壤湿度主动被动 (SMAP) 9 公里产品。因子分析表明，LSTM 模型的准确性受地形影响，导致干燥和朝南斜坡或潮湿和朝北斜坡的性能较低。这些知识有助于我们应用该模型，同时了解其局限性。该模型正在被整合到一个可操作的农业援助应用程序中，该应用程序目前 25 为 1300 万非洲农民提供信息。像非洲这样监测不足的地区需要高质量的土壤湿度预测来帮助粮食生产，但尚不清楚世界各地的土壤湿度过程是否足够相似，足以让数据驱动的模型保持 30 的精度。我们提出了一种基于深度学习的土壤湿度模型，该模型可以从现场数据和卫星数据中学习，并且在全球范围内表现优于卫星产品。这些结果帮助我们在全球范围内应用我们的模型，同时更好地理解其局限性。结果
. Climate change threatens our ability to grow food for a growing population. There are concurrent droughts and floods happening globally, with the greatest impacts felt in Africa. There is a need for high-quality soil moisture predictions in under-monitored regions like Africa. Yet it is unclear if soil moisture processes are globally similar enough to allow our models to maintain accuracy in unmonitored regions. We present a multitask long short-term memory (LSTM) model that learns simultaneously from global satellite-based and in-situ soil 15 moisture data. This model is evaluated in both random spatial holdout mode and continental holdout mode (trained on some continents, tested on a different one). The model compared favorably to current land surface models, satellite products, and a candidate machine learning model, reaching a global median correlation of 0.792 for the random spatial holdout test. It behaved surprisingly well in Africa and Australia, showing high correlation even when we excluded their sites from the training set, but performed relatively poorly in Alaska where rapid changes 20 are occurring. In all but one continent (Asia), the multitask model in the worst-case scenario test performed better than the soil moisture active passive (SMAP) 9-km product. Factorial analysis shows that the LSTM model’s accuracy with representing impacts of terrain aspect, resulting in lower performance for dry and south-facing slopes or wet and north-facing slopes. This knowledge helps us apply the model while understanding its limitations. This model is being integrated into an operational agricultural assistance application which currently 25 provides information to 13 million African farmers. Under-monitored regions like Africa need high-quality soil moisture predictions to help with food production, but it is not clear if soil moisture processes are similar enough around the world for data-driven models to maintain 30 accuracy. We present a deep-learning-based soil moisture model that learns from both in-situ data and satellite data and performs better than satellite products at the global scale. These results help us apply our model globally while better understanding its limitations. results
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影响因子：
5.4
作者：
D. Feng;Jiangtao Liu;K. Lawson;Chaopeng Shen
通讯作者：
D. Feng;Jiangtao Liu;K. Lawson;Chaopeng Shen
影响因子：
6.7
作者：
Rahmani, Farshid;Lawson, Kathryn;Shen, Chaopeng
通讯作者：
Shen, Chaopeng
DOI：
10.1109/tgrs.2018.2872131
发表时间：
2019-04-01
影响因子：
8.2
作者：
Fang, Kuai;Pan, Ming;Shen, Chaopeng
通讯作者：
Shen, Chaopeng
影响因子：
5.4
作者：
Feng, Dapeng;Fang, Kuai;Shen, Chaopeng
通讯作者：
Shen, Chaopeng
影响因子：
3.2
作者：
F. Rahmani;Chaopeng Shen;S. Oliver;K. Lawson;A. Appling
通讯作者：
F. Rahmani;Chaopeng Shen;S. Oliver;K. Lawson;A. Appling