Evaluation of the Optimum Interpolation and Nudging Techniques for Soil Moisture Analysis Using FIFE Data

Surface soil moisture is an important hydrological parameter in agricultural areas. Periodic measurements in tropical mountain environments are poorly representative of larger areas, while satellite resolution is too coarse to be effective in these topographically varied landscapes, making spatial resolution an important parameter to consider. The Las Palmas catchment area near Medellin in Colombia is a vital water reservoir that stores considerable amounts of water in its andosol. In this tropical Andean setting, we use an unmanned aerial vehicle (UAV) with multispectral (visible, near infrared) sensors to determine the correlation of three agricultural land uses (potatoes, bare soil, and pasture) with surface soil moisture. Four vegetation indices (the perpendicular drought index, PDI; the normalized difference vegetation index, NDVI; the normalized difference water index, NDWI, and the soil-adjusted vegetation index, SAVI) were applied to UAV imagery and a 3 m resolution to estimate surface soil moisture through calibration with in situ field measurements. The results showed that on bare soil, the indices that best fit the soil moisture results are NDVI, NDWI and PDI on a detailed scale, whereas on potatoes crops, the NDWI is the index that correlates significantly with soil moisture, irrespective of the scale. Multispectral images and vegetation indices provide good soil moisture understanding in tropical mountain environments, with 3 m remote sensing images which are shown to be a good alternative to soil moisture analysis on pastures using the NDVI and UAV images for bare soil and potatoes.

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Operational assimilation of ASCAT surface soil wetness at the Met Office

Hydrology and Earth System Sciences ◽

10.5194/hess-15-2729-2011 ◽

2011 ◽

Vol 15 (8) ◽

pp. 2729-2746 ◽

Cited By ~ 117

Author(s):

I. Dharssi ◽

K. J. Bovis ◽

B. Macpherson ◽

C. P. Jones

Keyword(s):

Soil Moisture ◽

Surface Soil ◽

Positive Impact ◽

Surface Soil Moisture ◽

Moisture Analysis ◽

Analysis Scheme ◽

Temperature And Humidity ◽

Global Soil ◽

Soil Wetness ◽

Screen Temperature

Abstract. Currently, no extensive, near real time, global soil moisture observation network exists. Therefore, the Met Office global soil moisture analysis scheme has instead used observations of screen temperature and humidity. A number of new space-borne remote sensing systems, operating at microwave frequencies, have been developed that provide a more direct retrieval of surface soil moisture. These systems are attractive since they provide global data coverage and the horizontal resolution is similar to weather forecasting models. Several studies show that measurements of normalised backscatter (surface soil wetness) from the Advanced Scatterometer (ASCAT) on the meteorological operational (MetOp) satellite contain good quality information about surface soil moisture. This study describes methods to convert ASCAT surface soil wetness measurements to volumetric surface soil moisture together with bias correction and quality control. A computationally efficient nudging scheme is used to assimilate the ASCAT volumetric surface soil moisture data into the Met Office global soil moisture analysis. This ASCAT nudging scheme works alongside a soil moisture nudging scheme that uses observations of screen temperature and humidity. Trials, using the Met Office global Unified Model, of the ASCAT nudging scheme show a positive impact on forecasts of screen temperature and humidity for the tropics, North America and Australia. A comparison with in-situ soil moisture measurements from the US also indicates that assimilation of ASCAT surface soil wetness improves the soil moisture analysis. Assimilation of ASCAT surface soil wetness measurements became operational during July 2010.

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A Global Root-Zone Soil Moisture Analysis Using Simulated L-band Brightness Temperature in Preparation for the Hydros Satellite Mission

Journal of Hydrometeorology ◽

10.1175/jhm525.1 ◽

2006 ◽

Vol 7 (5) ◽

pp. 1126-1146 ◽

Cited By ~ 26

Author(s):

G. Balsamo ◽

J-F. Mahfouf ◽

S. Bélair ◽

G. Deblonde

Keyword(s):

Soil Moisture ◽

Data Assimilation ◽

Brightness Temperature ◽

Land Surface ◽

Root Zone ◽

Satellite Observations ◽

Reference State ◽

Observation Error ◽

Moisture Analysis ◽

L Band

Abstract The aim of this study is to test a land data assimilation prototype for the production of a global daily root-zone soil moisture analysis. This system can assimilate microwave L-band satellite observations such as those from the future Hydros NASA mission. The experiments are considered in the framework of the Interaction Soil Biosphere Atmosphere (ISBA) land surface scheme used operationally at the Meteorological Service of Canada for regional and global weather forecasting. A land surface reference state is obtained after a 1-yr global land surface simulation, forced by near-surface atmospheric fields provided by the Global Soil Wetness Project, second initiative (GSWP-2). A radiative transfer model is applied to simulate the microwave L-band passive emission from the surface. The generated brightness temperature observations are distributed in space and time according to the satellite trajectory specified by the Hydros mission. The impact of uncertainties related to the satellite observations, the land surface, and microwave emission models is investigated. A global daily root-zone soil moisture analysis is produced with a simplified variational scheme. The applicability and performance of the system are evaluated in a data assimilation cycle in which the L-band simulated observations, generated from a land surface reference state, are assimilated to correct a prescribed initial root-zone soil moisture error. The analysis convergence is satisfactory in both summer and winter cases. In summer, when considering a 3-K observation error, 90% of land surface converges toward the reference state with a soil moisture accuracy better than 0.04 m3 m−3 after a 4-week assimilation cycle. A 5-K observation error introduces 1-week delay in the convergence. A study of the analysis error statistics is performed for understanding the properties of the system. Special features associated with the interactions between soil water and soil ice, and the presence of soil moisture vertical gradients, are examined.

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Towards a Kalman Filter based soil moisture analysis system for the operational ECMWF Integrated Forecast System

Geophysical Research Letters ◽

10.1029/2009gl037716 ◽

2009 ◽

Vol 36 (10) ◽

Cited By ~ 60

Author(s):

M. Drusch ◽

K. Scipal ◽

P. de Rosnay ◽

G. Balsamo ◽

E. Andersson ◽

...

Keyword(s):

Soil Moisture ◽

Kalman Filter ◽

Forecast System ◽

Moisture Analysis ◽

Analysis System

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Impact of precipitation and increasing temperatures on drought trends in eastern Africa

Earth System Dynamics ◽

10.5194/esd-12-17-2021 ◽

2021 ◽

Vol 12 (1) ◽

pp. 17-35

Author(s):

Sarah F. Kew ◽

Sjoukje Y. Philip ◽

Mathias Hauser ◽

Mike Hobbins ◽

Niko Wanders ◽

...

Keyword(s):

Soil Moisture ◽

A Priori ◽

Agricultural Drought ◽

Eastern Africa ◽

Precipitation Trend ◽

Crop Failure ◽

Evaporative Demand ◽

Clear Trend ◽

Moisture Analysis ◽

The Impact

Abstract. In eastern Africa droughts can cause crop failure and lead to food insecurity. With increasing temperatures, there is an a priori assumption that droughts are becoming more severe. However, the link between droughts and climate change is not sufficiently understood. Here we investigate trends in long-term agricultural drought and the influence of increasing temperatures and precipitation deficits. Using a combination of models and observational datasets, we studied trends, spanning the period from 1900 (to approximate pre-industrial conditions) to 2018, for six regions in eastern Africa in four drought-related annually averaged variables: soil moisture, precipitation, temperature, and evaporative demand (E0). In standardized soil moisture data, we found no discernible trends. The strongest influence on soil moisture variability was from precipitation, especially in the drier or water-limited study regions; temperature and E0 did not demonstrate strong relations to soil moisture. However, the error margins on precipitation trend estimates are large and no clear trend is evident, whereas significant positive trends were observed in local temperatures. The trends in E0 are predominantly positive, but we do not find strong relations between E0 and soil moisture trends. Nevertheless, the E0 trend results can still be of interest for irrigation purposes because it is E0 that determines the maximum evaporation rate. We conclude that until now the impact of increasing local temperatures on agricultural drought in eastern Africa is limited and we recommend that any soil moisture analysis be supplemented by an analysis of precipitation deficit.

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