Study on Summer Maize Yield Responses to Remote Sensing Drought Indices in Henan Province with GWR Model

2019 ◽  
Author(s):  
Yan Wang ◽  
Hongshuo Wang ◽  
Weizhong Yang ◽  
Yuan Li
Keyword(s):  
Remote Sensing ◽  
Maize Yield ◽  
Henan Province ◽  
Drought Indices ◽  
Summer Maize ◽  
Yield Responses ◽  
Gwr Model

scholarly journals Key factors affecting the spatial variation of summer maize yield in Henan Province, China

资源科学 ◽  
2019 ◽  
Vol 41 (10) ◽  
pp. 1935-1948
Author(s):  
Zhenfu WU ◽  
Yanfeng ZHAO ◽  
Daoquan CHENG ◽  
Jie CHEN ◽  
Keyword(s):  
Spatial Variation ◽  
Maize Yield ◽  
Henan Province ◽  
Key Factors ◽  
Summer Maize ◽  
Factors Affecting

scholarly journals Remote Sensing-Based Quantification of the Summer Maize Yield Gap Induced by Suboptimum Sowing Dates over North China Plain

2021 ◽  
Vol 13 (18) ◽  
pp. 3582 ◽  
Author(s):  
Sha Zhang ◽  
Yun Bai ◽  
Jiahua Zhang
Keyword(s):  
Remote Sensing ◽  
Crop Yield ◽  
North China Plain ◽  
North China ◽  
Regional Scale ◽  
Maize Yield ◽  
Yield Potential ◽  
Management Information ◽  
Summer Maize ◽  
Yield Gap

Estimating yield potential (Yp) and quantifying the contribution of suboptimum field managements to the yield gap (Yg) of crops are important for improving crop yield effectively. However, achieving this goal on a regional scale remains difficult because of challenges in collecting field management information. In this study, we retrieved crop management information (i.e., emerging stage information and a surrogate of sowing date (SDT)) from a remote sensing (RS) vegetation index time series. Then, we developed a new approach to quantify maize Yp, total Yg, and the suboptimum SDT-induced Yg (Yg0) using a process-based RS-driven crop yield model for maize (PRYM–Maize), which was developed in our previous study. PRYM–Maize and the newly developed method were used over the North China Plain (NCP) to estimate Ya, Yp, Yg, and Yg0 of summer maize. Results showed that PRYM–Maize outputs reasonable estimates for maize yield over the NCP, with correlations and root mean standard deviation of 0.49 ± 0.24 and 0.88 ± 0.14 t hm−2, respectively, for modeled annual maize yields versus the reference value for each year over the period 2010 to 2015 on a city level. Yp estimated using our new method can reasonably capture the spatial variations in site-level estimates from crop growth models in previous literature. The mean annual regional Yp of 2010–2015 was estimated to be 11.99 t hm−2, and a Yg value of 5.4 t hm−2 was found between Yp and Ya on a regional scale. An estimated 29–42% of regional Yg in each year (2010–2015) was induced by suboptimum SDT. Results also show that not all Yg0 was persistent over time. Future studies using high spatial-resolution RS images to disaggregate Yg0 into persistent and non-persistent components on a small scale are required to increase maize yield over the NCP.

scholarly journals Evaluation of Drought Indices Based on Thermal Remote Sensing of Evapotranspiration over the Continental United States

2011 ◽  
Vol 24 (8) ◽  
pp. 2025-2044 ◽  
Author(s):  
Martha C. Anderson ◽  
Christopher Hain ◽  
Brian Wardlow ◽  
Agustin Pimstein ◽  
John R. Mecikalski ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Drought Index ◽  
Temporal Correlation ◽  
Meteorological Drought ◽  
Rainfall Data ◽  
Added Value ◽  
Stress Index ◽  
Drought Indices ◽  
Shallow Water Table

Abstract The reliability of standard meteorological drought indices based on measurements of precipitation is limited by the spatial distribution and quality of currently available rainfall data. Furthermore, they reflect only one component of the surface hydrologic cycle, and they cannot readily capture nonprecipitation-based moisture inputs to the land surface system (e.g., irrigation) that may temper drought impacts or variable rates of water consumption across a landscape. This study assesses the value of a new drought index based on remote sensing of evapotranspiration (ET). The evaporative stress index (ESI) quantifies anomalies in the ratio of actual to potential ET (PET), mapped using thermal band imagery from geostationary satellites. The study investigates the behavior and response time scales of the ESI through a retrospective comparison with the standardized precipitation indices and Palmer drought index suite, and with drought classifications recorded in the U.S. Drought Monitor for the 2000–09 growing seasons. Spatial and temporal correlation analyses suggest that the ESI performs similarly to short-term (up to 6 months) precipitation-based indices but can be produced at higher spatial resolution and without requiring any precipitation data. Unique behavior is observed in the ESI in regions where the evaporative flux is enhanced by moisture sources decoupled from local rainfall: for example, in areas of intense irrigation or shallow water table. Normalization by PET serves to isolate the ET signal component responding to soil moisture variability from variations due to the radiation load. This study suggests that the ESI is a useful complement to the current suite of drought indicators, with particular added value in parts of the world where rainfall data are sparse or unreliable.

scholarly journals N, P and K use efficiency and maize yield responses to fertilization modes and densities

2021 ◽  
Vol 20 (1) ◽  
pp. 78-86
Author(s):  
Guang-hao LI ◽  
Qian CHENG ◽  
Long LI ◽  
Da-lei LU ◽  
Wei-ping LU
Keyword(s):  
Maize Yield ◽  
Use Efficiency ◽  
Yield Responses

scholarly journals SPAD inversion of summer maize combined with multi-source remote sensing data

2018 ◽  
Vol 1 (1) ◽  
pp. 45-52
Author(s):  
Fangjiang Pan ◽  
Wenhua Li ◽  
Yubin Lan ◽  
Xuguang Liu ◽  
Jianchi Miao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Summer Maize ◽  
Sensing Data

High-resolution root zone soil moisture-based indices for drought monitoring in the Ebro basin

2021 ◽  
Author(s):  
Vivien-Georgiana Stefan ◽  
Maria-José Escorihuela ◽  
Pere Quintana-Seguí
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
High Resolution ◽  
Root Zone ◽  
Cost Effective ◽  
Strong Relationship ◽  
Drought Indices ◽  
Ebro Basin ◽  
Small Areas ◽  
Cost Effective Approach

<h3>Agriculture is an important factor on water resources, given the constant population growth and the strong relationship between water availability and food production. In this context, root zone soil moisture (RZSM) measurements are used by modern irrigators in order to detect the onset of crop water stress and to trigger irrigations. Unfortunately, in situ RZSM measurements are costly; combined with the fact they are available only over small areas and that they might not be representative at the field scale, remote sensing is a cost-effective approach for mapping and monitoring extended areas. A recursive formulation of an exponential filter was used in order to derive 1 km resolution RZSM estimates from SMAP (Soil Moisture Active Passive) surface soil moisture (SSM) over the Ebro basin. The SMAP SSM was disaggregated to a 1 km resolution by using the DISPATCH (DISaggregation based on a Physical And Theoretical scale CHange) algorithm. The pseudodiffusivity parameter of the exponential filter was calibrated per land cover type, by using ISBA-DIF (Interaction Soil Biosphere Atmosphere) surface and root zone soil moisture data as an intermediary step. The daily 1 km RZSM estimates were then used to derive 1 km drought indices such as soil moisture anomalies and soil moisture deficit indices (SMDI), on a weekly time-scale, covering the entire 2020 year. Results show that both drought indices are able to capture rainfall and drying events, with the weekly anomaly being more responsive to sudden events such as heavy rainfalls, while the SMDI is slower to react do the inherent inertia it has. Moreover, a quantitative comparison with drought indices derived from a model-based RZSM estimates has also been performed, with results showing a strong correspondence between the different indices. For comparison purposes, the weekly soil moisture anomalies and SMDI derived using 1 km SMAP-derived SSM were also estimated. The analysis shows that the anomalies and SMDI based on the RZSM are more representative of the hydric stress level of the plants, given that the RZSM is better suited than the SSM to describe the moisture conditions at the deeper layers, which are the ones used by plants during growth and development.</h3><h3>The study provides an insight into obtaining robust, high-resolution remote-sensing derived drought indices based on remote-sensing derived RZSM estimates. The 1 km resolution proves an improvement from other currently available drought indices, such as the European Drought Observatory’s 5 km resolution drought index, which is not able to capture as well the spatial variability present within heterogeneous areas. Moreover, the SSM-derived drought indices are currently used in a drought observatory project, covering a region in the Tarragona province of Catalonia, Spain. The project aims at offering irrigation recommendations to water agencies, and the introduction of RZSM-derived drought indices will further improve such advice.</h3>

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