Exploring the potential role of environmental and multi-source satellite data in crop yield prediction across Northeast China

2022 ◽  
pp. 152880
Author(s):  
Zhenwang Li ◽  
Lei Ding ◽  
Dawei Xu
Keyword(s):  
Crop Yield ◽  
Satellite Data ◽  
Northeast China ◽  
Potential Role ◽  
Yield Prediction

scholarly journals Combining Optical, Fluorescence, Thermal Satellite, and Environmental Data to Predict County-Level Maize Yield in China Using Machine Learning Approaches

2019 ◽  
Vol 12 (1) ◽  
pp. 21 ◽  
Author(s):  
Liangliang Zhang ◽  
Zhao Zhang ◽  
Yuchuan Luo ◽  
Juan Cao ◽  
Fulu Tao
Keyword(s):  
Machine Learning ◽  
Crop Yield ◽  
Satellite Data ◽  
Maize Yield ◽  
Environmental Data ◽  
Yield Prediction ◽  
County Level ◽  
Climate Data ◽  
Source Data ◽  
Optical Fluorescence

Maize is an extremely important grain crop, and the demand has increased sharply throughout the world. China contributes nearly one-fifth of the total production alone with its decreasing arable land. Timely and accurate prediction of maize yield in China is critical for ensuring global food security. Previous studies primarily used either visible or near-infrared (NIR) based vegetation indices (VIs), or climate data, or both to predict crop yield. However, other satellite data from different spectral bands have been underutilized, which contain unique information on crop growth and yield. In addition, although a joint application of multi-source data significantly improves crop yield prediction, the combinations of input variables that could achieve the best results have not been well investigated. Here we integrated optical, fluorescence, thermal satellite, and environmental data to predict county-level maize yield across four agro-ecological zones (AEZs) in China using a regression-based method (LASSO), two machine learning (ML) methods (RF and XGBoost), and deep learning (DL) network (LSTM). The results showed that combining multi-source data explained more than 75% of yield variation. Satellite data at the silking stage contributed more information than other variables, and solar-induced chlorophyll fluorescence (SIF) had an almost equivalent performance with the enhanced vegetation index (EVI) largely due to the low signal to noise ratio and coarse spatial resolution. The extremely high temperature and vapor pressure deficit during the reproductive period were the most important climate variables affecting maize production in China. Soil properties and management factors contained extra information on crop growth conditions that cannot be fully captured by satellite and climate data. We found that ML and DL approaches definitely outperformed regression-based methods, and ML had more computational efficiency and easier generalizations relative to DL. Our study is an important effort to combine multi-source remote sensed and environmental data for large-scale yield prediction. The proposed methodology provides a paradigm for other crop yield predictions and in other regions.

scholarly journals Predicting Maize Yield at the Plot Scale of Different Fertilizer Systems by Multi-Source Data and Machine Learning Methods

2021 ◽  
Vol 13 (18) ◽  
pp. 3760
Author(s):  
Linghua Meng ◽  
Huanjun Liu ◽  
Susan L. Ustin ◽  
Xinle Zhang
Keyword(s):  
Machine Learning ◽  
Crop Yield ◽  
Satellite Data ◽  
Maize Yield ◽  
Environmental Data ◽  
Yield Prediction ◽  
Climate Data ◽  
Multiple Sources ◽  
Adaptive Boosting ◽  
Source Data

Timely and reliable maize yield prediction is essential for the agricultural supply chain and food security. Previous studies using either climate or satellite data or both to build empirical or statistical models have prevailed for decades. However, to what extent climate and satellite data can improve yield prediction is still unknown. In addition, fertilizer information may also improve crop yield prediction, especially in regions with different fertilizer systems, such as cover crop, mineral fertilizer, or compost. Machine learning (ML) has been widely and successfully applied in crop yield prediction. Here, we attempted to predict maize yield from 1994 to 2007 at the plot scale by integrating multi-source data, including monthly climate data, satellite data (i.e., vegetation indices (VIs)), fertilizer data, and soil data to explore the accuracy of different inputs to yield prediction. The results show that incorporating all of the datasets using random forests (RF) and AB (adaptive boosting) can achieve better performances in yield prediction (R2: 0.85~0.98). In addition, the combination of VIs, climate data, and soil data (VCS) can predict maize yield more effectively than other combinations (e.g., combinations of all data and combinations of VIs and soil data). Furthermore, we also found that including different fertilizer systems had different prediction accuracies. This paper aggregates data from multiple sources and distinguishes the effects of different fertilization scenarios on crop yield predictions. In addition, the effects of different data on crop yield were analyzed in this study. Our study provides a paradigm that can be used to improve yield predictions for other crops and is an important effort that combines multi-source remotely sensed and environmental data for maize yield prediction at the plot scale and develops timely and robust methods for maize yield prediction grown under different fertilizing systems.

scholarly journals Seed Priming with Phytohormones: An Effective Approach for the Mitigation of Abiotic Stress

Plants ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 37
Author(s):  
Mohammad Saidur Rhaman ◽  
Shahin Imran ◽  
Farjana Rauf ◽  
Mousumi Khatun ◽  
Carol C. Baskin ◽  
...  
Keyword(s):  
Abiotic Stress ◽  
Crop Yield ◽  
Abiotic Stresses ◽  
Crop Production ◽  
Potential Role ◽  
Seed Priming ◽  
Plant Growth And Development ◽  
Biotic And Abiotic Stresses ◽  
Harmful Effects

Plants are often exposed to abiotic stresses such as drought, salinity, heat, cold, and heavy metals that induce complex responses, which result in reduced growth as well as crop yield. Phytohormones are well known for their regulatory role in plant growth and development, and they serve as important chemical messengers, allowing plants to function during exposure to various stresses. Seed priming is a physiological technique involving seed hydration and drying to improve metabolic processes prior to germination, thereby increasing the percentage and rate of germination and improving seedling growth and crop yield under normal and various biotic and abiotic stresses. Seed priming allows plants to obtain an enhanced capacity for rapidly and effectively combating different stresses. Thus, seed priming with phytohormones has emerged as an important tool for mitigating the effects of abiotic stress. Therefore, this review discusses the potential role of priming with phytohormones to mitigate the harmful effects of abiotic stresses, possible mechanisms for how mitigation is accomplished, and roles of priming on the enhancement of crop production.

scholarly journals Application study of crop yield prediction based on AquaCrop model in black soil region of Northeast China

2020 ◽  
Vol 47 (1) ◽  
pp. 159-168
Author(s):  
Ying CUI ◽  
Hong-Hong LIN ◽  
Yun XIE ◽  
Su-Hong LIU
Keyword(s):  
Crop Yield ◽  
Northeast China ◽  
Black Soil ◽  
Yield Prediction ◽  
Application Study ◽  
Black Soil Region ◽  
Aquacrop Model

Field-scale crop yield prediction using multi-temporal WorldView-3 and PlanetScope satellite data and deep learning

2021 ◽  
Vol 174 ◽  
pp. 265-281
Author(s):  
Vasit Sagan ◽  
Maitiniyazi Maimaitijiang ◽  
Sourav Bhadra ◽  
Matthew Maimaitiyiming ◽  
Davis R. Brown ◽  
...  
Keyword(s):  
Deep Learning ◽  
Crop Yield ◽  
Satellite Data ◽  
Yield Prediction ◽  
Field Scale ◽  
Multi Temporal

Potential role of urban forest in removing PM2.5: A case study in Seoul by deep learning with satellite data

Urban Climate ◽  
2021 ◽  
Vol 36 ◽  
pp. 100795
Author(s):  
Ahreum Lee ◽  
Sujong Jeong ◽  
Jaewon Joo ◽  
Chan-Ryul Park ◽  
Jhoon Kim ◽  
...  
Keyword(s):  
Deep Learning ◽  
Satellite Data ◽  
Potential Role ◽  
Urban Forest

POPDC proteins and cardiac function

2019 ◽  
Vol 47 (5) ◽  
pp. 1393-1404 ◽  
Author(s):  
Thomas Brand
Keyword(s):  
Potential Role ◽  
Striated Muscle ◽  
Short Review ◽  
Effector Proteins ◽  
Muscle Disease ◽  
Disease Genes ◽  
Protein Protein Interaction ◽  
Interaction Partners ◽  
And Function

Abstract The Popeye domain-containing gene family encodes a novel class of cAMP effector proteins in striated muscle tissue. In this short review, we first introduce the protein family and discuss their structure and function with an emphasis on their role in cyclic AMP signalling. Another focus of this review is the recently discovered role of POPDC genes as striated muscle disease genes, which have been associated with cardiac arrhythmia and muscular dystrophy. The pathological phenotypes observed in patients will be compared with phenotypes present in null and knockin mutations in zebrafish and mouse. A number of protein–protein interaction partners have been discovered and the potential role of POPDC proteins to control the subcellular localization and function of these interacting proteins will be discussed. Finally, we outline several areas, where research is urgently needed.

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