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>

scholarly journals Suitability Evaluation of Typical Drought Index in Soil Moisture Retrieval and Monitoring Based on Optical Images

2020 ◽  
Vol 12 (16) ◽  
pp. 2587
Author(s):  
Yan Nie ◽  
Ying Tan ◽  
Yuqin Deng ◽  
Jing Yu
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Arid Regions ◽  
Drought Index ◽  
Dynamic Monitoring ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Rapid Acquisition ◽  
Semi Arid ◽  
Soil Moisture Inversion

As a basic agricultural parameter in the formation, transformation, and consumption of surface water resources, soil moisture has a very important influence on the vegetation growth, agricultural production, and healthy operation of regional ecosystems. The Aksu river basin is a typical semi-arid agricultural area which seasonally suffers from water shortage. Due to the lack of knowledge on soil moisture change, the water management and decision-making processes have been a difficult issue for local government. Therefore, soil moisture monitoring by remote sensing became a reasonable way to schedule crop irrigation and evaluate the irrigation efficiency. Compared to in situ measurements, the use of remote sensing for the monitoring of soil water content is convenient and can be repetitively applied over a large area. To verify the applicability of the typical drought index to the rapid acquisition of soil moisture in arid and semi-arid regions, this study simulated, compared, and validated the effectiveness of soil moisture inversion. GF-1 WFV images, Landsat 8 OLI images, and the measured soil moisture data were used to determine the Perpendicular Drought Index (PDI), the Modified Perpendicular Drought Index (MPDI), and the Vegetation Adjusted Perpendicular Drought Index (VAPDI). First, the determination coefficients of the correlation analyses on the PDI, MPDI, VAPDI, and measured soil moisture in the 0–10, 10–20, and 20–30 cm depth layers based on the GF-1 WFV and Landsat 8 OLI images were good. Notably, in the 0–10 cm depth layers, the average determination coefficient was 0.68; all models met the accuracy requirements of soil moisture inversion. Both indicated that the drought indices based on the Near Infrared (NIR)-Red spectral space derived from the optical remote sensing images are more sensitive to soil moisture near the surface layer; however, the accuracy of retrieving the soil moisture in deep layers was slightly lower in the study area. Second, in areas of vegetation coverage, MPDI and VAPDI had a higher inversion accuracy than PDI. To a certain extent, they overcame the influence of mixed pixels on the soil moisture spectral information. VAPDI modified by Perpendicular Vegetation Index (PVI) was not susceptible to vegetation saturation and, thus, had a higher inversion accuracy, which makes it performs better than MPDI’s in vegetated areas. Third, the spatial heterogeneity of the soil moisture retrieved by the GF-1 WFV and Landsat 8 OLI image were similar. However, the GF-1 WFV images were more sensitive to changes in the soil moisture, which reflected the actual soil moisture level covered by different vegetation. These results provide a practical reference for the dynamic monitoring of surface soil moisture, obtaining agricultural information and agricultural condition parameters in arid and semi-arid regions.

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.

Agricultural drought monitoring in semi-arid and arid areas using MODIS data

2011 ◽  
Vol 149 (4) ◽  
pp. 403-414 ◽  
Author(s):  
A. SHAHABFAR ◽  
J. EITZINGER
Keyword(s):  
Remote Sensing ◽  
Water Balance ◽  
Winter Wheat ◽  
Drought Index ◽  
Climatic Conditions ◽  
Agricultural Drought ◽  
Crop Evapotranspiration ◽  
Climatic Zones ◽  
Reference Crop ◽  
Semi Arid

SUMMARYThe performances of two remote sensing drought indices were evaluated at selected agricultural sites in different agro-climatic zones in Iran to detect the severity of drought phenomena related to temporal variation and different climatic conditions. The indices used were the perpendicular drought index (PDI) and the modified perpendicular drought index (MPDI), which are derived from moderate resolution imaging spectroradiometer (MODIS) satellite images (MOD13A3 V005). The correlations between these perpendicular indices and two other remote sensing indices in ten different agro-climatic zones of Iran from February 2000 to December 2005 were analysed. The additional indices evaluated were the enhanced vegetation index (EVI) and the vegetation condition index (VCI) along with five water balance parameters, including climatic water balance (CL), crop water balance (CR), monthly reference crop evapotranspiration (ET0), crop evapotranspiration (ETc) and required irrigation water (I). Winter wheat was selected as the reference crop because it is grown in the majority of climatic conditions in Iran.The results show that in several climatic regions, there is a statistically significant correlation between PDI and MPDI and the water balance parameters, indicating an acceptable performance in detecting crop drought stress conditions. In all zones except at the sites located in northwest and northeast of Iran, VCI and EVI are less correlated with the applied water balance indicators compared to PDI and MPDI. In a temporal analysis, PDI and MPDI showed a greater ability to detect CR conditions than VCI and EVI in the most drought-sensitive winter wheat-growing stages. Since Iran is characterized by arid or semi-arid climatic conditions and winter wheat is a major agricultural crop, a combination of both PDI and MPDI could be used as simple remote sensing-based tool to map drought conditions for crops in Iran and in other developing countries with similar climatic conditions.

scholarly journals A Practical Satellite-Derived Vegetation Drought Index for Arid and Semi-Arid Grassland Drought Monitoring

2021 ◽  
Vol 13 (3) ◽  
pp. 414
Author(s):  
Sheng Chang ◽  
Hong Chen ◽  
Bingfang Wu ◽  
Elbegjargal Nasanbat ◽  
Nana Yan ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Drought Index ◽  
Temporal Frequency ◽  
Remote Sensing Data ◽  
Field Measurements ◽  
Drought Monitoring ◽  
Economic Losses ◽  
Drought Indices ◽  
Vegetation Health Index ◽  
Semi Arid

In semi-arid pasture areas, drought may directly influence livestock production, cause economic losses, and accelerate the processes of desertification along with destructive human activities (i.e., overgrazing). The aim of this article is to analyze the disadvantages of several drought indices derived from remote sensing data and develop a new vegetation drought index (VDI) for monitoring of grassland drought with high temporal frequency (dekad) and fine spatial resolution (1 km). The site-based soil moisture data from the field campaign in 2014 and the fenced biomass values at nine sites from 2000 to 2015 were adopted for validation. The results indicate that the proposed VDI would better reflect the extent, severity, and changes of drought compared with single drought indices or the vegetation health index (VHI); specifically, the VDI is more closely related to site-based soil moisture, with R human increasing to approximately 0.07 compared with the VHI; and with normalized fenced biomass (NFB) values, with average R human increasing to approximately 0.11 compared with the VHI. However, the correlations between VHI and VDI with NFB values are relatively lower in desert steppe regions. Furthermore, regional drought-affected data (RDA) are used to ensure spatial consistency of the evaluation; the VDI map is in good agreement with the RDA map based on field measurements. The presented VDI shows reliable and stable drought monitoring ability, which will play an important role in the future drought monitoring of inland grassland.

scholarly journals Different Drought Legacies of Rain-Fed and Irrigated Croplands in a Typical Russian Agricultural Region

2020 ◽  
Vol 12 (11) ◽  
pp. 1700
Author(s):  
Yuanhuizi He ◽  
Fang Chen ◽  
Huicong Jia ◽  
Lei Wang ◽  
Valery G. Bondur
Keyword(s):  
Surface Temperature ◽  
Drought Index ◽  
Growth Period ◽  
Meteorological Drought ◽  
Agricultural Drought ◽  
Drought Monitoring ◽  
Growth Stages ◽  
Drought Conditions ◽  
Irrigated Lands ◽  
Meteorological Droughts

Droughts are one of the primary natural disasters that affect agricultural economies, as well as the fire hazards of territories. Monitoring and researching droughts is of great importance for agricultural disaster prevention and reduction. The research significance of investigating the hysteresis of agricultural to meteorological droughts is to provide an important reference for agricultural drought monitoring and early warnings. Remote sensing drought monitoring indices can be employed for rapid and accurate drought monitoring at regional scales. In this paper, the Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices and the surface temperature product are used as the data sources. Calculating the temperature vegetation drought index (TVDI) and constructing a comprehensive drought disaster index (CDDI) based on the crop growth period allowed drought conditions and spatiotemporal evolution patterns in the Volgograd region in 2010 and 2012 to be effectively monitored. The causes of the drought were then analyzed based on the sensitivity of a drought to meteorological factors in rain-fed and irrigated lands. Finally, the lag time of agricultural to meteorological droughts and the hysteresis in different growth periods were analyzed using statistical analyses. The research shows that (1) the main drought patterns in 2010 were spring droughts from April to May and summer droughts from June to August, and the primary drought patterns in 2012 were spring droughts from April to June, with an affected area that reached 3.33% during the growth period; (2) local drought conditions are dominated by the average surface temperature factor. Rain-fed lands are sensitive to the temperature and are therefore prone to summer droughts. Irrigated lands are more sensitive to water shortages in the spring and less sensitive to extremely high temperature conditions; (3) there is a certain lag between meteorological and agricultural droughts during the different growth stages. The strongest lag relationship was found in the planting stage and the weakest one was found in the dormancy stage. Therefore, the meteorological drought index in the growth period has a better predictive ability for agricultural droughts during the appropriately selected growth stages.

Soil moisture and vegetation effects on the ASCAT backscatter-incidence angle dependence

2021 ◽  
Author(s):  
Isabella Pfeil ◽  
Wolfgang Wagner ◽  
Sebastian Hahn ◽  
Raphael Quast ◽  
Susan Steele-Dunne ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Radiative Transfer ◽  
Incidence Angle ◽  
Transfer Model ◽  
Study Region ◽  
Vegetation Effects ◽  
First Order ◽  
Vegetation Parameters ◽  
Low Incidence

<div> <p>Soil moisture (SM) datasets retrieved from the advanced scatterometer (ASCAT) sensor are well established and widely used for various hydro-meteorological, agricultural, and climate monitoring applications. Besides SM, ASCAT is sensitive to vegetation structure and vegetation water content, enabling the retrieval of vegetation optical depth (VOD; 1). The challenge in the retrieval of SM and vegetation products from ASCAT observations is to separate the two effects. As described by Wagner et al. (2), SM and vegetation affect the relation between backscatter and incidence angle differently.  At high incidence angles, the response from bare soil and thus the sensitivity to SM conditions is significantly weaker than at low incidence angles, leading to decreasing backscatter with increasing incidence angle. The presence of vegetation on the other hand decreases the backscatter dependence on the incidence angle. The dependence of backscatter on the incidence angle can be described by a second-order Taylor polynomial based on a slope and a curvature coefficient. It was found empirically that SM conditions have no significant effect on the steepness of the slope, and that therefore, SM and vegetation effects can be separated using the slope (2).  This is a major assumption in the TU Wien soil moisture retrieval algorithm used in several operational soil moisture products. However, recent findings by Quast et al. (3) using a first-order radiative transfer model for the inversion of soil and vegetation parameters from scatterometer observations indicate that SM may influence the slope, as the SM-induced backscatter increase is more pronounced at low incidence angles. </p> </div><div> <div> <p>The aim of this analysis is to revisit the assumption that SM does not affect the slope of the backscatter incidence angle relations by investigating if short-term variability, observed in ASCAT slope timeseries on top of the seasonal vegetation cycle, is caused by SM. We therefore compare timeseries and anomalies of the ASCAT slope to air temperature, rainfall and SM from the ERA5-Land dataset. We carry out the analysis in a humid continental climate (Austria) and a Mediterranean climate study region (Portugal). First results show significant negative correlations between slope and SM anomalies. However, correlations between temperature and slope anomalies are of a similar magnitude, albeit positive, which may reflect temperature-induced vegetation dynamics. The fact that temperature and SM are strongly correlated with each other complicates the interpretation of the results. Thus, our second approach is to investigate daily slope values and their change between dry and wet days. The results of this study shall help to quantify the uncertainties in ASCAT SM products caused by the potentially inadequate assumption of a SM-independent slope. </p> </div> <div> <p> </p> </div> <div> <p>(1) Vreugdenhil, Mariette, et al. "Analyzing the vegetation parameterization in the TU-Wien ASCAT soil moisture retrieval." IEEE Transactions on Geoscience and Remote Sensing 54.6 (2016): 3513-3531.</p> <p><span>(2) Wagner, Wolfgang, et al. "Monitoring soil moisture over the Canadian Prairies with the ERS scatterometer." IEEE Transactions on Geoscience and Remote Sensing 37.1 (1999): 206-216. </span></p> </div> <div> <p>(3) Quast, Raphael, et al. "A Generic First-Order Radiative Transfer Modelling Approach for the Inversion of Soil and Vegetation Parameters from Scatterometer Observations." Remote Sensing 11.3 (2019): 285.</p> </div> </div>

scholarly journals Machine Learning to Estimate Surface Soil Moisture from Remote Sensing Data

Water ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 3223
Author(s):  
Hamed Adab ◽  
Renato Morbidelli ◽  
Carla Saltalippi ◽  
Mahmoud Moradian ◽  
Gholam Abbas Fallah Ghalhari
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Soil Moisture ◽  
Surface Soil ◽  
Remote Sensing Data ◽  
Surface Soil Moisture ◽  
Machine Learning Methods ◽  
Land Use Types ◽  
Semi Arid Region ◽  
Semi Arid

Soil moisture is an integral quantity parameter in hydrology and agriculture practices. Satellite remote sensing has been widely applied to estimate surface soil moisture. However, it is still a challenge to retrieve surface soil moisture content (SMC) data in the heterogeneous catchment at high spatial resolution. Therefore, it is necessary to improve the retrieval of SMC from remote sensing data, which is important in the planning and efficient use of land resources. Many methods based on satellite-derived vegetation indices have already been developed to estimate SMC in various climatic and geographic conditions. Soil moisture retrievals were performed using statistical and machine learning methods as well as physical modeling techniques. In this study, an important experiment of soil moisture retrieval for investigating the capability of the machine learning methods was conducted in the early spring season in a semi-arid region of Iran. We applied random forest (RF), support vector machine (SVM), artificial neural network (ANN), and elastic net regression (EN) algorithms to soil moisture retrieval by optical and thermal sensors of Landsat 8 and knowledge of land-use types on previously untested conditions in a semi-arid region of Iran. The statistical comparisons show that RF method provided the highest Nash–Sutcliffe efficiency value (0.73) for soil moisture retrieval covered by the different land-use types. Combinations of surface reflectance and auxiliary geospatial data can provide more valuable information for SMC estimation, which shows promise for precision agriculture applications.

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