Towards a continuous operational system to estimate the root-zone soil moisture from intermittent remotely sensed surface moisture

1995 ◽  
Vol 173 (1-4) ◽  
pp. 1-25 ◽  
Author(s):  
R. Ragab
Keyword(s):  
Soil Moisture ◽  
Root Zone ◽  
Remotely Sensed ◽  
Operational System ◽  
Surface Moisture

scholarly journals Field-scale soil moisture bridges the spatial-scale gap between drought monitoring and agricultural yields

2020 ◽  
Author(s):  
Noemi Vergopolan ◽  
Sitian Xiong ◽  
Lyndon Estes ◽  
Niko Wanders ◽  
Nathaniel W. Chaney ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Spatial Scale ◽  
Root Zone ◽  
Maize Yield ◽  
Remotely Sensed ◽  
Drought Monitoring ◽  
Surface Model ◽  
Field Scale ◽  
Yield Data ◽  
Scale Modeling

Abstract. Soil moisture is highly variable in space, and its deficits (i.e. droughts) plays an important role in modulating crop yields and its variability across landscapes. Limited hydroclimate and yield data, however, hampers drought impact monitoring and assessment at the farmer field-scale. This study demonstrates the potential of field-scale soil moisture simulations to advance high-resolution agricultural yield prediction and drought monitoring at the smallholder farm field-scale. We present a multi-scale modeling approach that combines HydroBlocks, a physically-based hyper-resolution Land Surface Model (LSM), and machine learning. We applied HydroBlocks to simulate root zone soil moisture and soil temperature in Zambia at 3-hourly 30-m resolution. These simulations along with remotely sensed vegetation indices, meteorological conditions, and data describing the physical properties of the landscape (topography, land cover, soil properties) were combined with district-level maize data to train a random forest model (RF) to predict maize yields at the district- and field-scale (250-m) levels. Our model predicted yields with a coefficient of variation (R2) of 0.61, Mean Absolute Error (MAE) of 349 kg ha−1, and mean normalized error of 22 %. We captured maize losses due to the 2015/2016 El Niño drought at similar levels to losses reported by the Food and Agriculture Organization (FAO). Our results revealed that soil moisture is the strongest and most reliable predictor of maize yield, driving its spatial and temporal variability. Consequently, soil moisture was also the most effective indicator of drought impacts in crops when compared with precipitation, soil and air temperatures, and remotely-sensed NDVI-based drought indices. By combining field-scale root zone soil moisture estimates with observed maize yield data, this research demonstrates how field-scale modeling can help bridge the spatial scale discontinuity gap between drought monitoring and agricultural impacts.

scholarly journals Suitability of modelled and remotely sensed essential climate variables for monitoring Euro-Mediterranean droughts

2014 ◽  
Vol 7 (3) ◽  
pp. 931-946 ◽  
Author(s):  
C. Szczypta ◽  
J.-C. Calvet ◽  
F. Maignan ◽  
W. Dorigo ◽  
F. Baret ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Interannual Variability ◽  
Land Surface ◽  
Root Zone ◽  
Mediterranean Area ◽  
Remotely Sensed ◽  
Climatic Conditions ◽  
Leaf Life Span ◽  
Moisture Profile ◽  
Area Index

Abstract. Two new remotely sensed leaf area index (LAI) and surface soil moisture (SSM) satellite-derived products are compared with two sets of simulations of the ORganizing Carbon and Hydrology In Dynamic EcosystEms (ORCHIDEE) and Interactions between Soil, Biosphere and Atmosphere, CO2-reactive (ISBA-A-gs) land surface models. We analyse the interannual variability over the period 1991–2008. The leaf onset and the length of the vegetation growing period (LGP) are derived from both the satellite-derived LAI and modelled LAI. The LGP values produced by the photosynthesis-driven phenology model of ISBA-A-gs are closer to the satellite-derived LAI and LGP than those produced by ORCHIDEE. In the latter, the phenology is based on a growing degree day model for leaf onset, and on both climatic conditions and leaf life span for senescence. Further, the interannual variability of LAI is better captured by ISBA-A-gs than by ORCHIDEE. In order to investigate how recent droughts affected vegetation over the Euro-Mediterranean area, a case study addressing the summer 2003 drought is presented. It shows a relatively good agreement of the modelled LAI anomalies with the observations, but the two models underestimate plant regrowth in the autumn. A better representation of the root-zone soil moisture profile could improve the simulations of both models. The satellite-derived SSM is compared with SSM simulations of ISBA-A-gs only, as ORCHIDEE has no explicit representation of SSM. Overall, the ISBA-A-gs simulations of SSM agree well with the satellite-derived SSM and are used to detect regions where the satellite-derived product could be improved. Finally, a correspondence is found between the interannual variability of detrended SSM and LAI. The predictability of LAI is less pronounced using remote sensing observations than using simulated variables. However, consistent results are found in July for the croplands of the Ukraine and southern Russia.

scholarly journals Cross-evaluation of modelled and remotely sensed surface soil moisture with in situ data in southwestern France

2010 ◽  
Vol 14 (11) ◽  
pp. 2177-2191 ◽  
Author(s):  
C. Albergel ◽  
J.-C. Calvet ◽  
P. de Rosnay ◽  
G. Balsamo ◽  
W. Wagner ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Surface Soil ◽  
Root Zone ◽  
Weather Prediction ◽  
Remotely Sensed ◽  
Surface Model ◽  
Surface Soil Moisture ◽  
Moisture Profile

Abstract. The SMOSMANIA soil moisture network in Southwestern France is used to evaluate modelled and remotely sensed soil moisture products. The surface soil moisture (SSM) measured in situ at 5 cm permits to evaluate SSM from the SIM operational hydrometeorological model of Météo-France and to perform a cross-evaluation of the normalised SSM estimates derived from coarse-resolution (25 km) active microwave observations from the ASCAT scatterometer instrument (C-band, onboard METOP), issued by EUMETSAT and resampled to the Discrete Global Grid (DGG, 12.5 km gridspacing) by TU-Wien (Vienna University of Technology) over a two year period (2007–2008). A downscaled ASCAT product at one kilometre scale is evaluated as well, together with operational soil moisture products of two meteorological services, namely the ALADIN numerical weather prediction model (NWP) and the Integrated Forecasting System (IFS) analysis of Météo-France and ECMWF, respectively. In addition to the operational SSM analysis of ECMWF, a second analysis using a simplified extended Kalman filter and assimilating the ASCAT SSM estimates is tested. The ECMWF SSM estimates correlate better with the in situ observations than the Météo-France products. This may be due to the higher ability of the multi-layer land surface model used at ECMWF to represent the soil moisture profile. However, the SSM derived from SIM corresponds to a thin soil surface layer and presents good correlations with ASCAT SSM estimates for the very first centimetres of soil. At ECMWF, the use of a new data assimilation technique, which is able to use the ASCAT SSM, improves the SSM and the root-zone soil moisture analyses.

scholarly journals Estimating Root Zone Moisture from Surface Soil Using Limited Data

2017 ◽  
Vol 24 (4) ◽  
pp. 501-516
Author(s):  
Wen-Zhi Zeng ◽  
Guo-Qing Lei ◽  
Hong-Ya Zhang ◽  
Ming-Hai Hong ◽  
Chi Xu ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Soil Moisture ◽  
Linear Regression ◽  
Monte Carlo Simulations ◽  
Linear Regression Model ◽  
Surface Soil ◽  
Root Zone ◽  
Surface Moisture ◽  
Moisture Estimation ◽  
Hydrus 1D

Abstract For estimation of root-zone moisture content from EO-1/Hyperion imagery, surface soil moisture was first predicted by hyperspectral reflectance data using partial least square regression (PLSR) analysis. The textures of more than 300 soil samples extracted from a 900 m × 900 m field site located within the Hetao Irrigation District in China were used to parameterize the HYDRUS-1D numerical model. The study area was spatially discretized into 18,000 compartments (30 m × 30 m × 0.02 m), and Monte Carlo simulations were applied to generate 2000 different soil-particle size distributions for each compartment. Soil hydraulic properties for each realization were determined by application of artificial neural network analysis and used to parameterize HYDRUS-1D to simulate averaged soil-moisture contents within the root zone (0-40 cm) and surface (approximately 0-4 cm). Then the link between surface moisture and root zone was established by use of linear regression analysis, resulting in R and RMSE of 0.38 and 0.03, respectively. Kriging and co-kriging with observed surface moisture, and co-kriging with surface moisture obtained from Hyperion imagery were also used to estimate root-zone moisture. Results indicated that PLSR is a powerful tool for soil moisture estimation from hyperspectral data. Furthermore, co-kriging with observed surface moisture had the highest R (0.41) and linear regression model, and HYDRUS Monte Carlo simulations had a lowest RMSE (0.03) among the four methods. In regions that have similar climatic and soil conditions to our study area, a linear regression model with HYDRUS Monte Carlo simulations is a practical method for root-zone moisture estimation before sowing and it can be easily coupled with remote sensing technology.

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