scholarly journals Evaluation of Drought Monitoring Effect of Winter Wheat in Henan Province of China Based on Multi-Source Data

2020 ◽  
Vol 12 (7) ◽  
pp. 2801
Author(s):  
Yuan Li ◽  
Yi Dong ◽  
Dongqin Yin ◽  
Diyou Liu ◽  
Pengxin Wang ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Winter Wheat ◽  
Wheat Yield ◽  
Growth Period ◽  
Henan Province ◽  
Agricultural Drought ◽  
Individual Growth ◽  
Drought Monitoring ◽  
Drought Indices ◽  
Winter Wheat Yield

Monitoring agricultural drought is important to food security and the sustainable development of human society. In order to improve the accuracy of soil moisture and winter wheat yield estimation, drought monitoring effects of optical drought index data, meteorological drought data, and passive microwave soil moisture data were explored during individual and whole growth periods of winter wheat in 2003–2011, taking Henan Province of China as the research area. The model of drought indices and relative meteorological yield of winter wheat in individual and whole growth periods was constructed based on multiple linear regression. Results showed a higher correlation between Moderate-Resolution Imaging Spectroradiometer (MODIS) drought indices and 10 cm relative soil moisture (RSM10) than 20 cm (RSM20) and 50 cm (RSM50). In the whole growth period, the correlation coefficient (R) between vegetation supply water index (VSWI) and RSM10 had the highest correlation (R = −0.206), while in individual growth periods, the vegetation temperature condition index (VTCI) was superior to the vegetation health index (VHI) and VSWI. Among the meteorological drought indices, the 10-day, 20-day, and 30-day standard precipitation evapotranspiration indices (SPEI1, SPEI2, and SPEI3) were all most relevant to RSM10 during individual and whole growth periods. RSM50 and SPEI3 had a higher correlation, indicating that deep soil moisture was more related to drought on a long time scale. The relationship between Advanced Microwave Scanning Radiometer for EOS soil moisture (AMSR-E SM) and VTCI was stable and significantly positive in individual and whole growth periods, which was better compared to VHI and VSWI. Compared with the drought indices and the relative meteorological yield in the city, VHI had the best monitoring effect during individual and whole growth periods. Results also showed that drought occurring at the jointing–heading stage can reduce winter wheat yield, while a certain degree of drought occurring at the heading–milk ripening stage can increase the yield. In the whole growth period, the combination of SPEI1, SPEI2, and VHI had the best performance, with a coefficient of determination (R2) of 0.282 with the combination of drought indices as the independent variables and relative meteorological yield as the dependent variable. In the individual growth period, the model in the later growth period of winter wheat performed well, especially in the returning green–jointing stage (R2 = 0.212). Results show that the combination of multiple linear drought indices in the whole growth period and the model in the returning green–jointing period could improve the accuracy of winter wheat yield estimation. This study is helpful for effective agricultural drought monitoring of winter wheat in Henan Province.

Drought monitoring over Semi-Humid rain-fed winter wheat region in northwest China using remote sensing data from 1981 to 2010

2020 ◽  
Author(s):  
Ni Guo ◽  
Wei Wang ◽  
Lijuan Wang
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Winter Wheat ◽  
Natural Disasters ◽  
Drought Index ◽  
Growth Period ◽  
Drought Monitoring ◽  
Study Region ◽  
The World ◽  
Semi Arid

<p>Drought is a widespread climate phenomenon throughout the world, as well as one of the natural disasters that seriously impact agricultural. Losses caused by drought in China reach up to about 15 percent of the all losses caused by natural disasters every year. Therefore, to monitoring the drought real-time and effectively, to improving the level of drought monitoring and early warning capacity have important significance to defense drought effectively. Satellite remote sensing technique of drought developed rapidly and had been one of the significant methods that widely used throughout the world since 1980s. Studies have shown that remote sensing drought index, especially the Vegetation drought Index (VIs) is the most suitable one that can be used in semi-arid and semi-humid climate region. We choose semi-arid region of Longdong rain-fed agriculture area in the northwest of Gansu Province as the study area, which is the most frequency area in China that drought occurs. To estimate the drought characteristics from 1981 to 2010, monthly NDVI data, the VCI and AVI index data got from NDVI data, the Comprehensive meteorological drought Index (CI) data during this period, and soil moisture observation data in 20 cm were used. Results show that:</p><ol><li>The frequency and severity of drought in Longdong region appeared a low-high-low trend from 1981 to 2010. 1980s showed a lowest value, 1990s showed a highest value and 2000s showed a falling trend in the frequency and severity.</li> <li>AVI and VCI showed a good consistency of drought monitoring together with CI and soil moisture, but a higher volatility and lagged behind for 1 month.</li> <li>A Winter Wheat Drought Index (WWDI) was proposed through the analyses of inter-annual NDVI data during the winter wheat growth period and it represents the drought degree in the whole growth period commendably. Thus provide an efficient index to the winter wheat disaster assessment.</li> <li>The winter wheat drought degree in the study region from 1981 to 2010 was obtained using WWDI data. The most drought years got from WWDI data were 1995, 2000, 1992, 1996 and 1997, which displayed a very high consistency with the actual disaster situations.</li> </ol>

Drought monitoring in the Ebro basin: comparison of Soil Moisture and Vegetation anomalies

2020 ◽  
Author(s):  
Maria Jose Escorihuela ◽  
Pere Quintana Quintana-Seguí ◽  
Vivien Stefan ◽  
Jaime Gaona
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Prediction Models ◽  
Vegetation Indices ◽  
Standardized Precipitation Index ◽  
Agricultural Drought ◽  
Drought Monitoring ◽  
Drought Indices ◽  
Ebro Basin ◽  
Soil Moisture Data

<p>Drought is a major climatic risk resulting from complex interactions between the atmosphere, the continental surface and water resources management. Droughts have large socioeconomic impacts and recent studies show that drought is increasing in frequency and severity due to the changing climate.</p><p>Drought is a complex phenomenon and there is not a common understanding about drought definition. In fact, there is a range of definitions for drought. In increasing order of severity, we can talk about: meteorological drought is associated to a lack of precipitation, agricultural drought, hydrological drought and socio-economic drought is when some supply of some goods and services such as energy, food and drinking water are reduced or threatened by changes in meteorological and hydrological conditions. 
</p><p>A number of different indices have been developed to quantify drought, each with its own strengths and weaknesses. The most commonly used are based on precipitation such as the precipitation standardized precipitation index (SPI; McKee et al., 1993, 1995), on precipitation and temperature like the Palmer drought severity index (PDSI; Palmer 1965), others rely on vegetation status like the crop moisture index (CMI; Palmer, 1968) or the vegetation condition index (VCI; Liu and Kogan, 1996). Drought indices can also be derived from climate prediction models outputs. Drought indices base on remote sensing based have traditionally been limited to vegetation indices, notably due to the difficulty in accurately quantifying precipitation from remote sensing data. The main drawback in assessing drought through vegetation indices is that the drought is monitored when effects are already causing vegetation damage. In order to address drought in their early stages, we need to monitor it from the moment the lack of precipitation occurs.</p><p>Thanks to recent technological advances, L-band (21 cm, 1.4 GHz) radiometers are providing soil moisture fields among other key variables such as sea surface salinity or thin sea ice thickness. Three missions have been launched: the ESA’s SMOS was the first in 2009 followed by Aquarius in 2011 and SMAP in 2015.</p><p>A wealth of applications and science topics have emerged from those missions, many being of operational value (Kerr et al. 2016, Muñoz-Sabater et al. 2016, Mecklenburg et al. 2016). Those applications have been shown to be key to monitor the water and carbon cycles. Over land, soil moisture measurements have enabled to get access to root zone soil moisture, yield forecasts, fire and flood risks, drought monitoring, improvement of rainfall estimates, etc.</p><p>The advent of soil moisture dedicated missions (SMOS, SMAP) paves the way for drought monitoring based on soil moisture data. Initial assessment of a drought index based on SMOS soil moisture data has shown to be able to precede drought indices based on vegetation by 1 month (Albitar et al. 2013).</p><p>In this presentation we will be analysing different drought episodes in the Ebro basin using both soil moisture and vegetation based indices to compare their different performances and test the hypothesis that soil moisture based indices are earlier indicators of drought than vegetation ones.</p>

Risk assessment model of drought-caused winter wheat yield loss and its applications in Henan province

2006 ◽  
Author(s):  
Ronghua Liu ◽  
Shuanghe Shen ◽  
Zixi Zhu ◽  
Wenying Kang ◽  
Wensong Fang ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Winter Wheat ◽  
Yield Loss ◽  
Wheat Yield ◽  
Henan Province ◽  
Assessment Model ◽  
Risk Assessment Model ◽  
Winter Wheat Yield

scholarly journals Assimilating Soil Moisture Retrieved from Sentinel-1 and Sentinel-2 Data into WOFOST Model to Improve Winter Wheat Yield Estimation

2019 ◽  
Vol 11 (13) ◽  
pp. 1618 ◽  
Author(s):  
Wen Zhuo ◽  
Jianxi Huang ◽  
Li Li ◽  
Xiaodong Zhang ◽  
Hongyuan Ma ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Winter Wheat ◽  
Cloud Model ◽  
Wheat Yield ◽  
Remote Sensing Data ◽  
Yield Estimation ◽  
Sensing Data ◽  
Winter Wheat Yield ◽  
Sentinel 2

Crop yield estimation at a regional scale over a long period of time is of great significance to food security. In past decades, the integration of remote sensing observations and crop growth models has been recognized as a promising approach for crop growth monitoring and yield estimation. Optical remote sensing data are susceptible to cloud and rain, while synthetic aperture radar (SAR) can penetrate through clouds and has all-weather capabilities. This allows for more reliable and consistent crop monitoring and yield estimation in terms of radar sensor data. The aim of this study is to improve the accuracy for winter wheat yield estimation by assimilating time series soil moisture images, which are retrieved by a water cloud model using SAR and optical data as input, into the crop model. In this study, SAR images were acquired by C-band SAR sensors boarded on Sentinel-1 satellites and optical images were obtained from a Sentinel-2 multi-spectral instrument (MSI) for Hengshui city of Hebei province in China. Remote sensing data and ground data were all collected during the main growing season of winter wheat. Both the normalized difference vegetation index (NDVI), derived from Sentinel-2, and backscattering coefficients and polarimetric indicators, computed from Sentinel-1, were used in the water cloud model to derive time series soil moisture (SM) images. To improve the prediction of crop yields at the field scale, we incorporated remotely sensed soil moisture into the World Food Studies (WOFOST) model using the Ensemble Kalman Filter (EnKF) algorithm. In general, the trend of soil moisture inversion was consistent with the ground measurements, with the coefficient of determination (R2) equal to 0.45, 0.53, and 0.49, respectively, and RMSE was 9.16%, 7.43%, and 8.53%, respectively, for three observation dates. The winter wheat yield estimation results showed that the assimilation of remotely sensed soil moisture improved the correlation of observed and simulated yields (R2 = 0.35; RMSE =934 kg/ha) compared to the situation without data assimilation (R2 = 0.21; RMSE = 1330 kg/ha). Consequently, the results of this study demonstrated the potential and usefulness of assimilating SM retrieved from both Sentinel-1 C-band SAR and Sentinel-2 MSI optical remote sensing data into WOFOST model for winter wheat yield estimation and could also provide a reference for crop yield estimation with data assimilation for other crop types.

Comparison of three remotely sensed drought indices for assessing the impact of drought on winter wheat yield

2018 ◽  
Vol 13 (4) ◽  
pp. 504-526 ◽  
Author(s):  
Jianxi Huang ◽  
Wen Zhuo ◽  
Ying Li ◽  
Ran Huang ◽  
Fernando Sedano ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Wheat Yield ◽  
Remotely Sensed ◽  
Drought Indices ◽  
Winter Wheat Yield ◽  
The Impact

scholarly journals Prediction of Winter Wheat Yield Based on Multi-Source Data and Machine Learning in China

2020 ◽  
Vol 12 (2) ◽  
pp. 236 ◽  
Author(s):  
Jichong Han ◽  
Zhao Zhang ◽  
Juan Cao ◽  
Yuchuan Luo ◽  
Liangliang Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Winter Wheat ◽  
Crop Yield ◽  
Wheat Yield ◽  
Growth Period ◽  
Yield Prediction ◽  
Climate Data ◽  
Source Data ◽  
Winter Wheat Yield

Wheat is one of the main crops in China, and crop yield prediction is important for regional trade and national food security. There are increasing concerns with respect to how to integrate multi-source data and employ machine learning techniques to establish a simple, timely, and accurate crop yield prediction model at an administrative unit. Many previous studies were mainly focused on the whole crop growth period through expensive manual surveys, remote sensing, or climate data. However, the effect of selecting different time window on yield prediction was still unknown. Thus, we separated the whole growth period into four time windows and assessed their corresponding predictive ability by taking the major winter wheat production regions of China as an example in the study. Firstly we developed a modeling framework to integrate climate data, remote sensing data and soil data to predict winter wheat yield based on the Google Earth Engine (GEE) platform. The results show that the models can accurately predict yield 1~2 months before the harvesting dates at the county level in China with an R2 > 0.75 and yield error less than 10%. Support vector machine (SVM), Gaussian process regression (GPR), and random forest (RF) represent the top three best methods for predicting yields among the eight typical machine learning models tested in this study. In addition, we also found that different agricultural zones and temporal training settings affect prediction accuracy. The three models perform better as more winter wheat growing season information becomes available. Our findings highlight a potentially powerful tool to predict yield using multiple-source data and machine learning in other regions and for crops.

scholarly journals Influence of primary tillage methods on moisture-temperature soil regime and winter wheat yield

2021 ◽  
Author(s):  
Yu. A. Semenikhina ◽  
◽  
S. I. Kambulov ◽  
Keyword(s):  
Soil Moisture ◽  
Winter Wheat ◽  
Soil Temperature ◽  
Wheat Yield ◽  
Soil Tillage ◽  
Zero Tillage ◽  
Southern Zone ◽  
Rostov Region ◽  
Winter Wheat Yield ◽  
Tillage Methods

Purpose: to study the influence of soil cultivation methods on soil moisture-temperature regime and the winter wheat yield under conditions of insufficient and unstable moisture. Materials and methods: the study of various primary soil tillage methods was carried out under the conditions of long-term stationary experience in 2017–2019 on the isolated field of Federal State Budgetary Scientific Institution “Asovsliy Scientific Centre “Donskoy” (southern zone of Rostov region). The cultivated crop is Stanichnaya variety winter wheat, the predecessor is peas. The experimental site soil is ordinary calcareous heavy loamy chernozem. The studied tillage methods are surface, shallow, moldboard and zero (direct seeding). The method for determining the relative humidity and soil temperature in an autonomous mode was based on the use of Watch Dog 1400 Micro meteorological stations (recorders) from Spectrum Technologies, Inc., the soil moisture and temperature sensors were located at a depth of 30 cm. At the same time, the air humidity and temperature and the accumulation precipitation were monitored. Results: it was found that in the southern zone of Rostov region, zero tillage throughout the entire observation period provides high moisture conservation, preventing soil overheating, at the same time allowing to obtain a consistently high yield of winter wheat, which compares favorably with other tillage methods. Conclusions: comparison of various tillage methods with leading in all indicators zero tillage allowed to establish that, on average, with surface tillage, soil moisture is lower by 17.75 %, soil temperature is higher by 4.12 %, and yield is lower by 8.37 %. With shallow tillage, the soil moisture is 20.12 % lower, the soil temperature is 12.19 % higher, and the yield is 12.14 % lower. With the moldboard method, soil moisture is lower by 13.19 %, the soil temperature is higher by 11.48 %, and the yield is lower by 5.44 %.

Influence of global warming on winter wheat yield in Henan Province

2011 ◽  
Vol 19 (4) ◽  
pp. 854-859 ◽  
Author(s):  
Lin CHENG ◽  
Rong-Hua LIU ◽  
Zhi-Hong MA
Keyword(s):  
Global Warming ◽  
Winter Wheat ◽  
Wheat Yield ◽  
Henan Province ◽  
Winter Wheat Yield
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