Assessment of agricultural drought indicators impact on soybean crop yield: A case study in Iowa, USA

Huang, Youxin; Liu, Xiuguo; Shen, You-Gen; Jin, JiangFeng

doi:10.1109/agro-geoinformatics.2014.6910573

Cited by 7 publications

(5 citation statements)

References 11 publications

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“…A traditional drought generally corresponds to a period of abnormally dry weather that lasts long enough to produce a serious imbalance in the water cycle (Huschke, ). A large amount of research has been conducted for the traditional drought assessment from various perspectives, including climate (Palmer, ; Vicente‐Serrano et al, ), hydrology (Shafer and Dezman, ; Shukla and Wood, ), and agriculture (Huang et al, ). Recently, a short‐term concurrent hot and dry extreme (SCHDE) event, usually known as “heat wave flash drought,” occurs more frequently during crop growing seasons, characterized by the increase in actual evapotranspiration (ET) and decrease in soil moisture (SM) due to anomalously high surface temperature ( T air ) (Mo and Lettenmaier, ).…”

Section: Introductionmentioning

confidence: 99%

Recent intensification of short‐term concurrent hot and dry extremes over the Pearl River basin, China

Zhang

2019

Intl Journal of Climatology

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Ongoing climate warming is largely affecting the frequency and magnitude of short‐term climatic extremes in many regions of the world, causing significant damage to the environment and society. Here, based on the long‐term (1980–2010) meteorological observations and reanalysis products, we investigated the spatio‐temporal evolutions of short‐term concurrent hot and dry extreme (SCHDE) events over the Pearl River basin, a typical humid and subtropical basin in southern China. We also explored the physical mechanisms of SCHDE and its relationship with a series of hydrometeorological variables and large‐scale climate indices. The results indicate an intensification of SCHDE over the study basin during the study period, especially for the period 1997–2010. SCHDE occurs mostly in the northeastern and western regions and shows a statistically significant increasing trend mainly in the midwestern and southeast regions, which is mainly associated with an increased temperature (Tair), drying trend in soil moisture (SM), and a sharp increasing trend in evapotranspiration (ET). The contribution of Tair variability to the SCHDE variability is largest (57.27%), followed by SM (28.43%) and ET (14.30%). In addition to Tair, SM deficit before the onset also plays an important role in the SCHDE occurrence. Furthermore, the SCHDE variability tends to be more sensitive to the SM deficits in the high‐altitude areas than in the low‐altitude areas. Besides, wavelet transform analysis results indicate that the cold phase of ENSO tends to trigger a higher risk of SCHDE events, while El Niño Modoki events have different influences on the variations of SCHDE during different warm/cold phases over the Pearl River basin.

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Section: Introductionmentioning

confidence: 99%

Recent intensification of short‐term concurrent hot and dry extremes over the Pearl River basin, China

Zhang

2019

Intl Journal of Climatology

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“…Spectral vegetation indices (SVIs) derived from satellite imagery collected from various platforms have been used to predict crop yields across a range of spatial scales. The platforms have included Advanced Very High Resolution Radiometer (AVHRR), the Moderate Resolution Imaging Spectroradiometer (MODIS) [23][24][25], Landsat Thematic Mapper (TM) [23,26,27], Satellite Pour l'observation de la Terre (SPOT) [28,29], IKONOS [30,31], WorldView 2, and RapidEye imagery. For an exhaustive list of satellites and other remote sensing platforms, refer to Konecny [32].…”

Section: Introductionmentioning

confidence: 99%

Mapping Intra-Field Yield Variation Using High Resolution Satellite Imagery to Integrate Bioenergy and Environmental Stewardship in an Agricultural Watershed

2015

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Biofuels are important alternatives for meeting our future energy needs. Successful bioenergy crop production requires maintaining environmental sustainability and minimum impacts on current net annual food, feed, and fiber production. The objectives of this study were to: (1) determine under-productive areas within an agricultural field in a watershed using a single date; high resolution remote sensing and (2) examine impacts of growing bioenergy crops in the under-productive areas using hydrologic modeling in order to facilitate sustainable landscape design. Normalized difference indices (NDIs) were computed based on the ratio of all possible two-band combinations using the RapidEye and the National Agricultural Imagery Program images collected in summer 2011. A multiple regression analysis was performed using 10 NDIs and five RapidEye spectral bands. The regression analysis suggested that the red and near infrared bands and NDI using red-edge and near infrared that is known as the red-edge normalized difference vegetation index (RENDVI) had the highest correlation (R 2 = 0.524) with the reference yield. Although predictive yield map showed striking similarity to the reference yield map, the model had modest correlation; thus, further research is needed to improve predictive capability for absolute yields. Forecasted impact using the Soil and Water Assessment Tool model of growing switchgrass (Panicum virgatum) on under-productive areas based on corn yield thresholds of 3.1, 4.7, and 6.3 Mg· ha −1 showed reduction of tile NO3-N and sediment

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“…This method has been studied by Mohsen et al [4] for drought assessment in the Middle East. Drought in the US soybean fields was also detectable using this technique as reported by Huang et al [5]. Despite some intensive uses, VSWI has some drawbacks.…”

Section: Introductionmentioning

confidence: 63%

Constraints of VSWI in the Estimation of Drought Extent Using Landsat Data: A Case of Tuban, Indonesia

Trisasongko

Panuju

Shiddiq

et al. 2015

Procedia Environmental Sciences

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Existing drought-impacted data are generally rooted from individual reports, which under-represent spatial information. To improve the report, some meteorological satellites have been employed. Nonetheless, due to lacking of spatial resolution, the scale of the data is often excessively coarse. With the availability of long-term Landsat data, estimated extent of drought has been studied. One of the latest methods for this purpose is Vegetation Supply Water Index (VSWI). VSWI is defined as a ratio between vegetation index (in this case NDVI) and land surface temperature (LST, presented in Kelvin). Both data can be derived from remote sensing data containing multispectral reflectance and thermal data, which are available in Landsat data after calibration procedure. In this research, Landsat 7 sensor was applied considering its temporal span. Landsat data were atmospherically corrected to avoid misinterpretation of the results. We found that VSWI can accommodate various state of drought in agricultural fields. Severely affected fields are represented in dark tone, illustrating the absence of vegetation cover when surface temperature rises. Nonetheless, shortcomings of the technique are visually observable. Based on two kinds of rice field (irrigated and rainfed) coupled with two states of field condition (wet and dry), we conclude that dried and waterlogged irrigated rice fields are inseparable due similar value of NDVI. In contrast, vegetated rice field has fairly high VSWI value. The results indicate that further analysis incorporating water index can improve discrimination process.

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Assessment of agricultural drought indicators impact on soybean crop yield: A case study in Iowa, USA

Cited by 7 publications

References 11 publications

Recent intensification of short‐term concurrent hot and dry extremes over the Pearl River basin, China

Recent intensification of short‐term concurrent hot and dry extremes over the Pearl River basin, China

Mapping Intra-Field Yield Variation Using High Resolution Satellite Imagery to Integrate Bioenergy and Environmental Stewardship in an Agricultural Watershed

Constraints of VSWI in the Estimation of Drought Extent Using Landsat Data: A Case of Tuban, Indonesia

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