Field-scale mapping of evaporative stress indicators of crop yield: An application over Mead, NE, USA

Yang, Yang; Anderson, Martha C.; Gao, Feng; Wardlow, Brian D.; Hain, Christopher; Otkin, Jason A.; Alfieri, Joseph G.; Yang, Yun; Sun, Ling; Dulaney, W.

doi:10.1016/j.rse.2018.02.020

Cited by 84 publications

(43 citation statements)

References 80 publications

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“…The ESI is represented by anomalous values in most studies, whereby a value below −2 is indicative of drought and a value above 2 is indicative of wet conditions [33]. Values between 2 and −2 are not defined by an exact drought depth.…”

Section: Evaporative Stress Index (Esi)mentioning

confidence: 99%

Agricultural Drought Assessment in East Asia Using Satellite-Based Indices

et al. 2020

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Drought is the meteorological phenomenon with the greatest impact on agriculture. Accordingly, drought forecasting is vital in lessening its associated negative impacts. Utilizing remote exploration in the agricultural sector allows for the collection of large amounts of quantitative data across a wide range of areas. In this study, we confirmed the applicability of drought assessment using the evaporative stress index (ESI) in major East Asian countries. The ESI is an indicator of agricultural drought that describes anomalies in actual/reference evapotranspiration (ET) ratios that are retrieved using remotely sensed inputs of land surface temperature (LST) and leaf area index (LAI). The ESI is available through SERVIR Global, a joint venture between the National Aeronautics and Space Administration (NASA) and the United States Agency for International Development (USAID). This study evaluated the performance of ESI in assessing drought events in South Korea. The evaluation of ESI is possible because of the availability of good statistical data. Comparing drought trends identified by ESI data from this study to actual drought conditions showed similar trends. Additionally, ESI reacted to the drought more quickly and with greater sensitivity than other drought indices. Our results confirmed that the ESI is advantageous for short and medium-term drought assessment compared to vegetation indices alone.

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Section: Evaporative Stress Index (Esi)mentioning

confidence: 99%

Agricultural Drought Assessment in East Asia Using Satellite-Based Indices

et al. 2020

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“…Besides weather variables, satellitederived vegetation indices (VIs) significantly improve the skill of yield predictions in many studies (Becker-Reshef et al 2010, Johnson 2014, Guan et al 2017, Peng et al 2018. In addition, a few studies show that satellite-based evapotranspiration (ET) and soil moisture observations possess unique signatures in quantifying environmental stress and might provide additional information than conventional opticalbased VIs (Anderson et al 2016a(Anderson et al , 2016bGuan et al 2017, Mladenova et al 2017, Yang et al 2018.…”

Section: Introductionmentioning

confidence: 99%

Comparative assessment of environmental variables and machine learning algorithms for maize yield prediction in the US Midwest

Kang

Özdoğan

Zhu

et al. 2020

Environ. Res. Lett.

132

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Crop yield estimates over large areas are conventionally made using weather observations, but a comprehensive understanding of the effects of various environmental indicators, observation frequency, and the choice of prediction algorithm remains elusive. Here we present a thorough assessment of county-level maize yield prediction in U.S. Midwest using six statistical/machine learning algorithms (Lasso, Support Vector Regressor, Random Forest, XGBoost, Long-short term memory (LSTM), and Convolutional Neural Network (CNN)) and an extensive set of environmental variables derived from satellite observations, weather data, land surface model results, soil maps, and crop progress reports. Results show that seasonal crop yield forecasting benefits from both more advanced algorithms and a large composite of information associated with crop canopy, environmental stress, phenology, and soil properties (i.e. hundreds of features). The XGBoost algorithm outperforms other algorithms both in accuracy and stability, while deep neural networks such as LSTM and CNN are not advantageous. The compositing interval (8-day, 16-day or monthly) of time series variable does not have significant effects on the prediction. Combining the best algorithm and inputs improves the prediction accuracy by 5% when compared to a baseline statistical model (Lasso) using only basic climatic and satellite observations. Reasonable county-level yield foresting is achievable from early June, almost four months prior to harvest. At the national level, early-season (June and July) prediction from the best model outperforms that of the United States Department of Agriculture (USDA) World Agricultural Supply and Demand Estimates (WASDE). This study provides insights into practical crop yield forecasting and the understanding of yield response to climatic and environmental conditions.

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“…In addition, microwave L/C bands are less affected by cloud and can detect soil moisture values at 0–5 cm depths for strong penetration performance. Future work will be focused on maximizing the potential of multisource data, including visible, NIR, TIR, and microwave remote sensing data to generate spatially consistent and temporally continuous SSM estimates at the field scale over heterogeneous agricultural land, which would be of more value for ET and crop yield estimation (Y. Yang et al, ).…”

Section: Discussionmentioning

confidence: 99%

Estimation of Surface Soil Moisture With Downscaled Land Surface Temperatures Using a Data Fusion Approach for Heterogeneous Agricultural Land

Bai

Long

Yan

2019

Water Resources Research

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Field-scale surface soil moisture (SSM, 0-10 cm), which is closely linked with land surface temperature (LST), is particularly important to agricultural water resource management. Active and passive microwave remote sensing-based SSM retrievals on the order of kilometer squared resolutions are difficult to apply to heterogeneous agricultural land surfaces that may need SSM data at a resolution of 30 m. In this study, the High-resolution Urban Thermal Sharpener and Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model were applied to downscale optical and thermal remote sensing data simultaneously by blending Landsat and MODIS red-near infrared-LST data, with the ultimate goal to generate field-scale SSM values from the trapezoidal approach. To evaluate the performance of the downscaled LST E (based on the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model method) and SSM, an irrigation district (Area 1) in Inner Mongolia and an irrigation district in the North China Plain (Area 2) with varying spatial heterogeneity were selected as the testbeds. Results indicated that the downscaled LST E was highly consistent with synchronous Landsat LST H and in situ LST measurements in Area 1, with the root-mean-square error ranging from 0.73 to 2.75 K. Compared with the MODIS SSM, the average root-mean-square error of the downscaled SSM improved from 0.048 to 0.038 cm 3 /cm 3 for both areas. The downscaled LST E and SSM developed in this study enhance the spatiotemporal resolutions of the SSM estimates, maximizing the potential of remotely sensed information for agricultural water resource management.Plain Language Summary Field-scale (30 m) surface soil moisture (SSM), closely linked with land surface temperature (LST), is particularly important for agricultural water resource management, such as for assessment of agricultural droughts, optimization of irrigation schedules and improvement of water use efficiency, particularly in the heterogeneous agricultural land. Here, the High resolution Urban Thermal Sharpener (HUTS) and Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) were jointly adopted to downscale optical and thermal remote sensing data simultaneously by blending Landsat and MODIS Red-Near infrared-LST data, with the ultimate goal to generate field-scale SSM values from the downscaled Red-Near infrared-LST remote sensing data using the theoretical trapezoidal approach. The field-scale LST and SSM developed in this study maximize the potential of remotely sensed information, improve both the spatial and temporal resolutions of SSM, and provide more valuable information on heterogeneous land surfaces for agricultural water resource management. Key Points: • Downscaled LSTE (30 m), closely linked with SSM, can be acquired jointly using HUTS and ESTARFM methods • Downscaled field-scale SSM (30 m) well represents the actual soil moisture than MODIS SSM in heterogeneous land • Downscaled Red-Near infrared-LSTE improves both the spatial and temporal resolution of SSM

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Field-scale mapping of evaporative stress indicators of crop yield: An application over Mead, NE, USA

Cited by 84 publications

References 80 publications

Agricultural Drought Assessment in East Asia Using Satellite-Based Indices

Agricultural Drought Assessment in East Asia Using Satellite-Based Indices

Comparative assessment of environmental variables and machine learning algorithms for maize yield prediction in the US Midwest

Estimation of Surface Soil Moisture With Downscaled Land Surface Temperatures Using a Data Fusion Approach for Heterogeneous Agricultural Land

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