scholarly journals Analysis of Drought Using Meteorological and Microwave Remote Sensed Data: A Case of Wami Watershed, Tanzania

2018 ◽  
Vol 37 (2) ◽  
pp. 107-124
Author(s):  
Mwajuma Juma ◽  
Deogratias M.M. Mulungu
Keyword(s):  
Soil Moisture ◽  
Rainy Season ◽  
Agricultural Sector ◽  
Microwave Remote Sensing ◽  
Rainfall Data ◽  
Drought Monitoring ◽  
Drought Severity ◽  
Microwave Radar ◽  
Spatial Coverage ◽  
Meteorological Droughts

Agricultural sector is important for the economy of Tanzania, but in recent years there is decline in its growth and performance because of persistent droughts. An in-depth study of droughts was conducted on Wami watershed through rainfall and satellite microwave remote sensing data leading for estimates of meteorological droughts and soil moisture based droughts, respectively. Rainfall data during 1973-2008 was used to obtain Drought Severity Index (DSI) and active imaging microwave radar data during 1997-2009 from ESA’s SAR missions of ENVISAT and ERS was used to obtain soil moisture anomalies (SMA). Soil map was used to explain discrepancies in droughts from SMA to DSI maps at intervals of time. Seasonality analysis and DSI results showed that the main sub-seasons contributing to rainy season are October through December, January-February and March through May, and drought years were 1984, 1991, 1994, 2004 and 2006. Results showed that the last decade (2000s) had severe droughts that covered 35-39% of the Wami watershed and could have affected 1128000 people. The soil moisture based drought maps showed the same drought conditions as DSI maps in January, March, May and October. This indicated that in most areas the meteorological droughts can be used to infer to droughts conditions in the soil during the rainy season. The obtained drought events and impacts were confirmed in the field through interviews. However, in July SMA map showed normal and wet conditions whereas it was a dry season for DSI map. This showed that when rainy season ends, the soil still holds some moisture, which can be available for simple crops like vegetables. Therefore, it can be concluded that the SMA was able to provide a better alternative to DSI especially for increased spatial coverage and accuracy of drought monitoring for agricultural production. The SMA enables to map droughts conditions at any point spatially rather than point based DSI maps, which may be prone to rainfall data gaps and spatial interpolation errors. The SMA approach for drought monitoring may be useful to rainfall data scarcity areas of Tanzania and for agricultural droughts risk management.

scholarly journals The Value of Remotely Sensed Information: The Case of a GRACE-Enhanced Drought Severity Index

2018 ◽  
Vol 10 (1) ◽  
pp. 187-203 ◽  
Author(s):  
Richard Bernknopf ◽  
David Brookshire ◽  
Yusuke Kuwayama ◽  
Molly Macauley ◽  
Matthew Rodell ◽  
...  
Keyword(s):  
Conceptual Framework ◽  
Agricultural Sector ◽  
Economic Value ◽  
Severity Index ◽  
Drought Monitoring ◽  
Drought Severity ◽  
Satellite Mission ◽  
Assistance Program ◽  
Grace Data ◽  
Effects Of Drought

Abstract A decision framework is developed for quantifying the economic value of information (VOI) from the Gravity Recovery and Climate Experiment (GRACE) satellite mission for drought monitoring, with a focus on the potential contributions of groundwater storage and soil moisture measurements from the GRACE data assimilation (GRACE-DA) system. The study consists of (i) the development of a conceptual framework to evaluate the socioeconomic value of GRACE-DA as a contributing source of information to drought monitoring; (ii) structured listening sessions to understand the needs of stakeholders who are affected by drought monitoring; (iii) econometric analysis based on the conceptual framework that characterizes the contribution of GRACE-DA to the U.S. Drought Monitor (USDM) in capturing the effects of drought on the agricultural sector; and (iv) a demonstration of how the improved characterization of drought conditions may influence decisions made in a real-world drought disaster assistance program. Results show that GRACE-DA has the potential to lower the uncertainty associated with the understanding of drought and that this improved understanding has the potential to change policy decisions that lead to tangible societal benefits.

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 An improved global remote-sensing-based surface soil moisture (RSSSM) dataset covering 2003–2018

2021 ◽  
Vol 13 (1) ◽  
pp. 1-31
Author(s):  
Yongzhe Chen ◽  
Xiaoming Feng ◽  
Bojie Fu
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Soil Moisture ◽  
Data Fusion ◽  
Surface Soil ◽  
Microwave Remote Sensing ◽  
Satellite Monitoring ◽  
Surface Soil Moisture ◽  
Spatial Coverage

Abstract. Soil moisture is an important variable linking the atmosphere and terrestrial ecosystems. However, long-term satellite monitoring of surface soil moisture at the global scale needs improvement. In this study, we conducted data calibration and data fusion of 11 well-acknowledged microwave remote-sensing soil moisture products since 2003 through a neural network approach, with Soil Moisture Active Passive (SMAP) soil moisture data applied as the primary training target. The training efficiency was high (R2=0.95) due to the selection of nine quality impact factors of microwave soil moisture products and the complicated organizational structure of multiple neural networks (five rounds of iterative simulations, eight substeps, 67 independent neural networks, and more than 1 million localized subnetworks). Then, we developed the global remote-sensing-based surface soil moisture dataset (RSSSM) covering 2003–2018 at 0.1∘ resolution. The temporal resolution is approximately 10 d, meaning that three data records are obtained within a month, for days 1–10, 11–20, and from the 21st to the last day of that month. RSSSM is proven comparable to the in situ surface soil moisture measurements of the International Soil Moisture Network sites (overall R2 and RMSE values of 0.42 and 0.087 m3 m−3), while the overall R2 and RMSE values for the existing popular similar products are usually within the ranges of 0.31–0.41 and 0.095–0.142 m3 m−3), respectively. RSSSM generally presents advantages over other products in arid and relatively cold areas, which is probably because of the difficulty in simulating the impacts of thawing and transient precipitation on soil moisture, and during the growing seasons. Moreover, the persistent high quality during 2003–2018 as well as the complete spatial coverage ensure the applicability of RSSSM to studies on both the spatial and temporal patterns (e.g. long-term trend). RSSSM data suggest an increase in the global mean surface soil moisture. Moreover, without considering the deserts and rainforests, the surface soil moisture loss on consecutive rainless days is highest in summer over the low latitudes (30∘ S–30∘ N) but mostly in winter over the mid-latitudes (30–60∘ N, 30–60∘ S). Notably, the error propagation is well controlled with the extension of the simulation period to the past, indicating that the data fusion algorithm proposed here will be more meaningful in the future when more advanced microwave sensors become operational. RSSSM data can be accessed at https://doi.org/10.1594/PANGAEA.912597 (Chen, 2020).

scholarly journals Reconstruction of droughts in India using multiple land-surface models (1951–2015)

2018 ◽  
Vol 22 (4) ◽  
pp. 2269-2284 ◽  
Author(s):  
Vimal Mishra ◽  
Reepal Shah ◽  
Syed Azhar ◽  
Harsh Shah ◽  
Parth Modi ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Crop Yields ◽  
Soil Depth ◽  
Monsoon Season ◽  
Agricultural Drought ◽  
Drought Severity ◽  
Rabi Season ◽  
Community Land Model ◽  
Spatial Coverage

Abstract. India has witnessed some of the most severe historical droughts in the current decade, and severity, frequency, and areal extent of droughts have been increasing. As a large part of the population of India is dependent on agriculture, soil moisture drought affecting agricultural activities (crop yields) has significant impacts on socio-economic conditions. Due to limited observations, soil moisture is generally simulated using land-surface hydrological models (LSMs); however, these LSM outputs have uncertainty due to many factors, including errors in forcing data and model parameterization. Here we reconstruct agricultural drought events over India during the period of 1951–2015 based on simulated soil moisture from three LSMs, the Variable Infiltration Capacity (VIC), the Noah, and the Community Land Model (CLM). Based on simulations from the three LSMs, we find that major drought events occurred in 1987, 2002, and 2015 during the monsoon season (June through September). During the Rabi season (November through February), major soil moisture droughts occurred in 1966, 1973, 2001, and 2003. Soil moisture droughts estimated from the three LSMs are comparable in terms of their spatial coverage; however, differences are found in drought severity. Moreover, we find a higher uncertainty in simulated drought characteristics over a large part of India during the major crop-growing season (Rabi season, November to February: NDJF) compared to those of the monsoon season (June to September: JJAS). Furthermore, uncertainty in drought estimates is higher for severe and localized droughts. Higher uncertainty in the soil moisture droughts is largely due to the difference in model parameterizations (especially soil depth), resulting in different persistence of soil moisture simulated by the three LSMs. Our study highlights the importance of accounting for the LSMs' uncertainty and consideration of the multi-model ensemble system for the real-time monitoring and prediction of drought over India.

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 AGRICULTURAL DROUGHT AND SOIL MOISTURE ANALYSIS USING SATELLITE IMAGE BASED INDICES

2019 ◽  
Vol XLII-3/W6 ◽  
pp. 507-514
Author(s):  
S. Sreekesh ◽  
N. Kaur ◽  
S. R. Sreerama Naik
Keyword(s):  
Soil Moisture ◽  
Satellite Image ◽  
Condition Index ◽  
Agricultural Drought ◽  
Drought Severity ◽  
Severe Drought ◽  
Landsat Images ◽  
Moisture Conditions ◽  
The Impact ◽  
Meteorological Droughts

<p><strong>Abstract.</strong> The deficiency in rainfall leads to meteorological droughts. Its manifestations are visible both in the vegetation cover and soil moisture. The present study assessed the characteristics of agricultural drought following meteorological droughts. The study also assessed the severity of meteorological droughts and their manifestation on the agriculture and soil moisture in a semi-arid area. The study has been carried out for the Malaprabha sub-basin which partly covers three districts of North Interior Karnataka, India. India Meteorological Department’s (IMD) criteria have been used to identify the drought years, and its severity has been assessed through the Standard Precipitation Index (SPI). The IMD’s monthly rainfall data were used to identify the drought years and periods for the region. Among the drought years, the mild, moderate, and severe drought along with deficit and excess rainfall years were considered to assess and characterize the soil moisture conditions and the agricultural drought. The satellite image based indices for these selected years were constructed to determine the soil moisture conditions and the agricultural drought severity. The Temperature-Vegetation Dryness Index (TVDI) was used to determine the soil moisture conditions. The indices employed to determine the agriculture drought are NDVI, Thermal Condition Index (TCI), Vegetation Condition Index (VCI), and Vegetation Health Index (VHI). These satellite-based indices were calculated using the Landsat images of the selected drought and non-drought years. The results showed that the seasonal and annual drought are frequent in the study area. There are spatial and temporal variations in the drought years and their severity. The satellite-based indices clearly indicate the spatial variation in the agriculture droughts and its intensity. It has been found that the impact of drought on agriculture has significantly reduced due to the development of well-irrigation in the sub-basin. VHI is more appropriate in determining the agricultural drought and its characteristics.</p>

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).

Research on the Variation in Soil Moisture in Arid Regions in Northern China Based on AMSR-E

2013 ◽  
Vol 781-784 ◽  
pp. 2353-2356
Author(s):  
Jing Wen Xu ◽  
Shuang Liu ◽  
Jun Fang Zhao ◽  
Jin Lun Han ◽  
Peng Wang
Keyword(s):  
Spatial Distribution ◽  
Soil Moisture ◽  
Arid Regions ◽  
Microwave Remote Sensing ◽  
Northern China ◽  
Agricultural Drought ◽  
Drought Monitoring ◽  
Study Region ◽  
Near Surface ◽  
Soil Moisture Data

Soil moisture plays an important role in agricultural drought monitoring. However, the traditional observed soil moisture data from meteorological stations has not been able to meet the demands for large-scale drought monitoring temporally and spatially. The microwave remote sensing is a new effective way for obtaining near surface soil moisture. The temporal and spatial variation in soil moisture in arid regions in northern China is examined based on the soil moisture data retrieved from AMSR-E (Advanced Microwave Scanning Radiometer-EOS), with which the observed soil moisture data at 10 cm depth from meteorological stations are compared. The results show that there is a small change in soil moisture with seasons for the central and west areas, while a large change for the east areas of the study region and the soil moisture in summer and autumn is greater than that in spring and winter. And that it decreases from southeast to northwest spatially, as is agree with the spatial distribution of precipitation in the study area. Moreover, there is a great difference in spatial distribution between the soil moisture retrieved from AMSR-E and the observed soil moisture data from meteorological stations for the central and west areas, while a small difference for the east areas of the study region.

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