scholarly journals Assessment of the SMAP-Derived Soil Water Deficit Index (SWDI-SMAP) as an Agricultural Drought Index in China

2018 ◽  
Vol 10 (8) ◽  
pp. 1302 ◽  
Author(s):  
Jueying Bai ◽  
Qian Cui ◽  
Deqing Chen ◽  
Haiwei Yu ◽  
Xudong Mao ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Soil Water ◽  
Water Deficit ◽  
Microwave Remote Sensing ◽  
Agricultural Drought ◽  
Drought Monitoring ◽  
In Situ Observation ◽  
Soil Water Deficit ◽  
The South ◽  
The North

China is frequently subjected to local and regional drought disasters, and thus, drought monitoring is vital. Drought assessments based on available surface soil moisture (SM) can account for soil water deficit directly. Microwave remote sensing techniques enable the estimation of global SM with a high temporal resolution. At present, the evaluation of Soil Moisture Active Passive (SMAP) SM products is inadequate, and L-band microwave data have not been applied to agricultural drought monitoring throughout China. In this study, first, we provide a pivotal evaluation of the SMAP L3 radiometer-derived SM product using in situ observation data throughout China, to assist in subsequent drought assessment, and then the SMAP-Derived Soil Water Deficit Index (SWDI-SMAP) is compared with the atmospheric water deficit (AWD) and vegetation health index (VHI). It is found that the SMAP can obtain SM with relatively high accuracy and the SWDI-SMAP has a good overall performance on drought monitoring. Relatively good performance of SWDI-SMAP is shown, except in some mountain regions; the SWDI-SMAP generally performs better in the north than in the south for less dry bias, although better performance of SMAP SM based on the R is shown in the south than in the north; differences between the SWDI-SMAP and VHI are mainly shown in areas without vegetation or those containing drought-resistant plants. In summary, the SWDI-SMAP shows great application potential in drought monitoring.

scholarly journals Satellite Soil Moisture for Agricultural Drought Monitoring: Assessment of SMAP-Derived Soil Water Deficit Index in Xiang River Basin, China

2019 ◽  
Vol 11 (3) ◽  
pp. 362 ◽  
Author(s):  
Qian Zhu ◽  
Yulin Luo ◽  
Yue-Ping Xu ◽  
Ye Tian ◽  
Tiantian Yang
Keyword(s):  
Soil Moisture ◽  
Soil Water ◽  
Water Deficit ◽  
River Basin ◽  
Probability Of Detection ◽  
Agricultural Drought ◽  
Drought Monitoring ◽  
Soil Water Deficit ◽  
R Value ◽  
Xiang River

Agricultural drought can have long-lasting and harmful impacts on both the ecosystem and economy. Therefore, it is important to monitor and predict agricultural drought accurately. Soil moisture is the key variable to define the agricultural drought index. However, in situ soil moisture observations are inaccessible in many areas of the world. Remote sensing techniques enrich the surface soil moisture observations at different tempo-spatial resolutions. In this study, the Level 2 L-band radiometer soil moisture dataset was used to estimate the Soil Water Deficit Index (SWDI). The Soil Moisture Active Passive (SMAP) dataset was evaluated with the soil moisture dataset obtained from the China Land Soil Moisture Data Assimilation System (CLSMDAS). The SMAP-derived SWDI (SMAP_SWDI) was compared with the atmospheric water deficit (AWD) calculated with precipitation and evapotranspiration from meteorological stations. Drought monitoring and comparison were accomplished at a weekly scale for the growing season (April to November) from 2015 to 2017. The results were as follows: (1) in terms of Pearson correlation coefficients (R-value) between SMAP and CLSMDAS, around 70% performed well and only 10% performed poorly at the grid scale, and the R-value was 0.62 for the whole basin; (2) severe droughts mainly occurred from mid-June to the end of September from 2015 to 2017; (3) severe droughts were detected in the southern and northeastern Xiang River Basin in mid-May of 2015, and in the northern basin in early August of 2016 and end of November 2017; (4) the values of percentage of drought weeks gradually decreased from 2015 to 2017, and increased from the northeast to the southwest of the basin in 2015 and 2016; and (5) the average value of R and probability of detection between SMAP_SWDI and AWD were 0.6 and 0.79, respectively. These results show SMAP has acceptable accuracy and good performance for drought monitoring in the Xiang River Basin.

Satellite soil moisture for agricultural drought monitoring: Assessment of the SMOS derived Soil Water Deficit Index

2016 ◽  
Vol 177 ◽  
pp. 277-286 ◽  
Author(s):  
J. Martínez-Fernández ◽  
A. González-Zamora ◽  
N. Sánchez ◽  
A. Gumuzzio ◽  
C.M. Herrero-Jiménez
Keyword(s):  
Soil Moisture ◽  
Soil Water ◽  
Water Deficit ◽  
Agricultural Drought ◽  
Drought Monitoring ◽  
Soil Water Deficit

A modified soil water deficit index (MSWDI) for agricultural drought monitoring: Case study of Songnen Plain, China

2017 ◽  
Vol 194 ◽  
pp. 125-138 ◽  
Author(s):  
Huicai Yang ◽  
Huixiao Wang ◽  
Guobin Fu ◽  
Haiming Yan ◽  
Panpan Zhao ◽  
...  
Keyword(s):  
Soil Water ◽  
Water Deficit ◽  
Agricultural Drought ◽  
Drought Monitoring ◽  
Soil Water Deficit ◽  
Songnen Plain ◽  
Modified Soil

Soybean Irrigation Initiation in Mississippi: Yield, Soil Moisture, and Economic Response

2019 ◽  
Vol 35 (1) ◽  
pp. 39-50
Author(s):  
H. C. Pringle, III ◽  
L. L. Falconer ◽  
D. K. Fisher ◽  
L. J. Krutz
Keyword(s):  
Soil Moisture ◽  
Water Potential ◽  
Soil Water ◽  
Water Deficit ◽  
Mississippi Delta ◽  
Soil Water Potential ◽  
Irrigation Scheduling ◽  
Silty Clay ◽  
Soil Water Deficit ◽  
Soybean Yield

Abstract. Irrigated acreage is expanding and groundwater supplies are decreasing in the Mississippi Delta. Efficient irrigation scheduling of soybean [ (L.) Merr] will aid in conservation efforts to sustain groundwater resources. The objective of this study was to develop irrigation initiation recommendations for soybean grown on Mississippi Delta soils. Field studies were conducted on a deep silty clay (SiC) in 2012, 2013, 2014, and 2015 and on a deep silty clay loam (SiCL) and deep silt loam (SiL) or loam (L) soil in 2013, 2014, and 2015. Irrigation was initiated multiple times during the growing season and soybean yield and net return were determined to evaluate the effectiveness of each initiation timing. Growth stage, soil water potential (SWP), and soil water deficit (SWD) were compared at these initiation timings to determine which parameter or combination of parameters consistently predicted the resulting greatest yields and net returns. Stress conditions that reduce yield can occur at any time from late vegetative stages to full seed on these deep soils. The wide range of trigger values found for SWP and SWD to increase yields in different years emphasizes the complexity of irrigation scheduling. Monitoring soil moisture by itself or use of a single trigger value is not sufficient to optimize irrigation scheduling to maximize soybean yield with the least amount of water every year on these soils. Monitoring one or more parameters (e.g., leaf water potential, canopy temperature, air temperature, humidity, solar radiation, and wind) is needed in conjunction with soil moisture to directly or indirectly quantify the abiotic stresses on the plant to better define when a yield reducing stress is occurring. Keywords: Irrigation initiation, Irrigation scheduling, Soil water deficit, Soil water potential, Soybean, Water conservation.

scholarly journals A fine-resolution soil moisture dataset for China in 2002–2018

2020 ◽  
Author(s):  
Xiangjin Meng ◽  
Kebiao Mao ◽  
Fei Meng ◽  
Jiancheng Shi ◽  
Jiangyuan Zeng ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Spatial Resolution ◽  
Microwave Remote Sensing ◽  
Passive Microwave ◽  
Agricultural Drought ◽  
Drought Monitoring ◽  
Matching Problem ◽  
Change Models ◽  
Fine Resolution ◽  
Microwave Data

Abstract. Soil moisture is an important parameter required for agricultural drought monitoring and climate change models. Passive microwave remote sensing technology has become an important means to quickly obtain soil moisture over large areas, but the coarse spatial resolution of microwave data imposes great limitations on the application of these data. We provide a unique soil moisture dataset (0.05°, monthly) for China from 2002–2018 based on reconstruction model-based downscaling techniques using soil moisture data from different passive microwave products (including the AMSR-E/2 Level 3 products and the SMOS-INRA-CESBIO (SMOS-IC) products) calibrated with a consistent model in combination with ground observation data. This new fine-resolution soil moisture dataset with a high spatial resolution overcomes the multisource data time matching problem between optical and microwave data sources and eliminates the difference between the different sensor observation errors. The validation analysis indicates that the accuracy of the new dataset is satisfactory (bias: −0.024, −0.030 and −0.016 m3/m3, unbiased root mean square error (ubRMSE): 0.051, 0.048 and 0.042, correlation coefficient (R): 0.82, 0.88, and 0.90 on monthly, seasonal and annual scales, respectively). The new dataset was used to analyze the spatiotemporal patterns of soil water content across China from 2002 to 2018. In the past 17 years, China's soil moisture has shown cyclical fluctuations and a downward trend (slope = −0.167, R = 0.750) and can be summarized as wet in the south and dry in the north, with increases in the west and decreases in the east. The reconstructed dataset can be widely used to significantly improve hydrologic and drought monitoring and can serve as an important input for ecological and other geophysical models. The data are published in the Zenodo at http://doi.org/10.5281/zenodo.4049958 (Meng et al., 2020).

scholarly journals Sustainable irrigation based on co-regulation of soil water supply and atmospheric evaporative demand

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Jingwen Zhang ◽  
Kaiyu Guan ◽  
Bin Peng ◽  
Ming Pan ◽  
Wang Zhou ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Water Supply ◽  
Soil Water ◽  
Water Deficit ◽  
Irrigation Water ◽  
Crop Yields ◽  
Water Productivity ◽  
Soil Water Deficit ◽  
Irrigation Scheme ◽  
Irrigation Water Use

AbstractIrrigation is an important adaptation to reduce crop yield loss due to water stress from both soil water deficit (low soil moisture) and atmospheric aridity (high vapor pressure deficit, VPD). Traditionally, irrigation has primarily focused on soil water deficit. Observational evidence demonstrates that stomatal conductance is co-regulated by soil moisture and VPD from water supply and demand aspects. Here we use a validated hydraulically-driven ecosystem model to reproduce the co-regulation pattern. Specifically, we propose a plant-centric irrigation scheme considering water supply-demand dynamics (SDD), and compare it with soil-moisture-based irrigation scheme (management allowable depletion, MAD) for continuous maize cropping systems in Nebraska, United States. We find that, under current climate conditions, the plant-centric SDD irrigation scheme combining soil moisture and VPD, could significantly reduce irrigation water use (−24.0%) while maintaining crop yields, and increase economic profits (+11.2%) and irrigation water productivity (+25.2%) compared with MAD, thus SDD could significantly improve water sustainability.

scholarly journals A fine-resolution soil moisture dataset for China in 2002–2018

2021 ◽  
Vol 13 (7) ◽  
pp. 3239-3261 ◽  
Author(s):  
Xiangjin Meng ◽  
Kebiao Mao ◽  
Fei Meng ◽  
Jiancheng Shi ◽  
Jiangyuan Zeng ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Spatial Resolution ◽  
Microwave Remote Sensing ◽  
Passive Microwave ◽  
Agricultural Drought ◽  
Drought Monitoring ◽  
Matching Problem ◽  
Change Models ◽  
Fine Resolution ◽  
Microwave Data

Abstract. Soil moisture is an important parameter required for agricultural drought monitoring and climate change models. Passive microwave remote sensing technology has become an important means to quickly obtain soil moisture across large areas, but the coarse spatial resolution of microwave data imposes great limitations on the application of these data. We provide a unique soil moisture dataset (0.05∘, monthly) for China from 2002 to 2018 based on reconstruction model-based downscaling techniques using soil moisture data from different passive microwave products – including AMSR-E and AMSR2 (Advanced Microwave Scanning Radiometer for Earth Observing System) JAXA (Japan Aerospace Exploration Agency) Level 3 products and SMOS-IC (Soil Moisture and Ocean Salinity designed by the Institut National de la Recherche Agronomique, INRA, and Centre d’Etudes Spatiales de la BIOsphère, CESBIO) products – calibrated with a consistent model in combination with ground observation data. This new fine-resolution soil moisture dataset with a high spatial resolution overcomes the multisource data time matching problem between optical and microwave data sources and eliminates the difference between the different sensor observation errors. The validation analysis indicates that the accuracy of the new dataset is satisfactory (bias: −0.057, −0.063 and −0.027 m3 m−3; unbiased root mean square error (ubRMSE): 0.056, 0.036 and 0.048; correlation coefficient (R): 0.84, 0.85 and 0.89 on monthly, seasonal and annual scales, respectively). The new dataset was used to analyze the spatiotemporal patterns of soil water content across China from 2002 to 2018. In the past 17 years, China's soil moisture has shown cyclical fluctuations and a slight downward trend and can be summarized as wet in the south and dry in the north, with increases in the west and decreases in the east. The reconstructed dataset can be widely used to significantly improve hydrologic and drought monitoring and can serve as an important input for ecological and other geophysical models. The data are published in Zenodo at https://doi.org/10.5281/zenodo.4738556 (Meng et al., 2021a).

scholarly journals Assessment of SMADI and SWDI agricultural drought indices using remotely sensed root zone soil moisture

2018 ◽  
Vol 380 ◽  
pp. 55-66
Author(s):  
Miriam Pablos ◽  
Ángel González-Zamora ◽  
Nilda Sánchez ◽  
José Martínez-Fernández
Keyword(s):  
Soil Moisture ◽  
Soil Water ◽  
Water Deficit ◽  
Surface Soil ◽  
Root Zone ◽  
Soil Layer ◽  
Statistical Correlation ◽  
Agricultural Drought ◽  
Drought Indices ◽  
Moisture Index

Abstract. The increasing frequency of drought events has expanded the research interest in drought monitoring. In this regard, remote sensing is a useful tool to globally mapping the agricultural drought. While this type of drought is directly linked to the availability of root zone soil moisture (RZSM) for plants growth, current satellite soil moisture observations only characterize the water content of the surface soil layer (0–5 cm). In this study, two soil moisture-based agricultural drought indices were obtained at a weekly rate from June 2010 to December 2016, using RZSM estimations at 1 km from the Soil Moisture and Ocean Salinity (SMOS) satellite, instead of surface soil moisture (SSM). The RZSM was estimated by applying the Soil Water Index (SWI) model to the SMOS SSM. The Soil Moisture Agricultural Drought Index (SMADI) and the Soil Water Deficit Index (SWDI) were assessed over the Castilla y León region (Spain) at 1 km spatial resolution. They were compared with the Atmospheric Water Deficit (AWD) and the Crop Moisture Index (CMI), both computed at different weather stations distributed over the study area. The level of agreement was analyzed through statistical correlation. Results showed that the use of RZSM does not influence the characterization of drought, both for SMADI and SWDI.

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