Modeling of terracette-hillslope soil moisture as a function of aspect, slope and vegetation in a semi-arid environment

Abstract. Soil and water management is particularly relevant in semi-arid regions to enhance agricultural productivity. During periods of water scarcity soil moisture differences are important indicators of the soil water deficit and are traditionally used for allocating water resources among farmers of a village community. Here we present a simple, inexpensive soil wetness classification scheme based on qualitative indicators which one can see or touch on the soil surface. It incorporates the local farmers' knowledge on the best soil moisture conditions for seeding and brick making in the semi-arid environment of the study site near Arusha, Tanzania. The scheme was tested twice in 2014 with farmers, students and experts (April: 40 persons, June: 25 persons) for inter-rater reliability, bias of individuals and functional relation between qualitative and quantitative soil moisture values. During the test in April farmers assigned the same wetness class in 46% of all cases while students and experts agreed in about 60% of all cases. Students who had been trained in how to apply the method gained higher inter-rater reliability than their colleagues with only a basic introduction. When repeating the test in June, participants were given improved instructions, organized in small sub-groups, which resulted in a higher inter-rater reliability among farmers. In 66% of all classifications farmers assigned the same wetness class and the spread of class assignments was smaller. This study demonstrates that a wetness classification scheme based on qualitative indicators is a robust tool and can be applied successfully regardless of experience in crop growing and education level when an in-depth introduction and training is provided. The use of a simple and clear layout of the assessment form is important for reliable wetness class assignments.

Download Full-text

Qualitative soil moisture assessment in semi-arid Africa – the role of experience and training on inter-rater reliability

Hydrology and Earth System Sciences ◽

10.5194/hess-19-3505-2015 ◽

2015 ◽

Vol 19 (8) ◽

pp. 3505-3516 ◽

Cited By ~ 3

Author(s):

M. Rinderer ◽

H. C. Komakech ◽

D. Müller ◽

G. L. B. Wiesenberg ◽

J. Seibert

Keyword(s):

Soil Moisture ◽

Classification Scheme ◽

Functional Relation ◽

Soil Surface ◽

Arid Environment ◽

Rater Reliability ◽

Moisture Conditions ◽

And Training ◽

Semi Arid

Abstract. Soil and water management is particularly relevant in semi-arid regions to enhance agricultural productivity. During periods of water scarcity, soil moisture differences are important indicators of the soil water deficit and are traditionally used for allocating water resources among farmers of a village community. Here we present a simple, inexpensive soil wetness classification scheme based on qualitative indicators which one can see or touch on the soil surface. It incorporates the local farmers' knowledge on the best soil moisture conditions for seeding and brick making in the semi-arid environment of the study site near Arusha, Tanzania. The scheme was tested twice in 2014 with farmers, students and experts (April: 40 persons, June: 25 persons) for inter-rater reliability, bias of individuals and functional relation between qualitative and quantitative soil moisture values. During the test in April farmers assigned the same wetness class in 46 % of all cases, while students and experts agreed on about 60 % of all cases. Students who had been trained in how to apply the method gained higher inter-rater reliability than their colleagues with only a basic introduction. When repeating the test in June, participants were given improved instructions, organized in small subgroups, which resulted in a higher inter-rater reliability among farmers. In 66 % of all classifications, farmers assigned the same wetness class and the spread of class assignments was smaller. This study demonstrates that a wetness classification scheme based on qualitative indicators is a robust tool and can be applied successfully regardless of experience in crop growing and education level when an in-depth introduction and training is provided. The use of a simple and clear layout of the assessment form is important for reliable wetness class assignments.

Download Full-text

Microwave Remote Sensing of Soil Moisture in Semi-arid Environment

Geoscience and Remote Sensing ◽

10.5772/8301 ◽

2009 ◽

Author(s):

A. K. M. Azad Hossain ◽

Greg Easso

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

Microwave Remote Sensing ◽

Arid Environment ◽

Semi Arid

Download Full-text

Soil moisture prediction, evapotranspiration, yield and water use efficiency of soybean (Glycine max L.) crop under variable weather conditions in a semi-arid environment

MAUSAM ◽

10.54302/mausam.v67i3.1388 ◽

2021 ◽

Vol 67 (3) ◽

pp. 683-690

Author(s):

S. PRADHAN ◽

V. K. SEHGAL ◽

D. K. DAS ◽

K. K. BANDYOPADHYAY ◽

A. K. JAIN ◽

...

Keyword(s):

Soil Moisture ◽

Water Use Efficiency ◽

Water Use ◽

Sandy Loam ◽

Arid Environment ◽

Balance Model ◽

Soybean Crop ◽

Complete Life Cycle ◽

Use Efficiency ◽

Semi Arid

A field experiment was conducted during kharif season of 2009 and 2010 in a sandy loam soil of New Delhi to study the effect of weather, achieved by sowing at normal (D1) and late (D2), on soil moisture prediction, evapotranspiration (ET), yield and water use efficiency (WUE) of three varieties (V1: JS 335, V2: Pusa 9712 and Pusa 9814) of soybean. Study of soybean phenology showed that there was reduction in the number of days taken for the crop to complete life cycle with delayed sowing. The agrometeorological water balance model could satisfactorily predict soil moisture content during soybean crop growth period with RMSE (%) varying between 6.27 to 12.06 and correlation coefficient between 0.828 to 0.982. The ET decreased significantly with delay in sowing; however there was no significant variation among the varieties. Among the stages of the soybean crop, mid season stage had highest ET followed by development stage, late season stage and initial stage. Normal sowing resulted in higher yield but lower WUE than the late sowing. Among the cultivars, JS 335 resulted in lower yield and WUE than Pusa 9712 and Pusa 9814. It may be recommended that, Pusa 9712 or Pusa 9814 may be sown during first and second week of July (normal sowing) to achieve higher yield in the semi-arid environment of Delhi region.

Download Full-text

Eranet-Med Optimed-Water Project: Results On Soil Moisture Maps Of Semi-Arid Environment By Using Optical/Microwave Satellite Data

2020 Mediterranean and Middle-East Geoscience and Remote Sensing Symposium (M2GARSS) ◽

10.1109/m2garss47143.2020.9105186 ◽

2020 ◽

Author(s):

S. Paloscia ◽

G. Fontanelli ◽

S. Pettinato ◽

E. Santi ◽

G. Ramat ◽

...

Keyword(s):

Soil Moisture ◽

Satellite Data ◽

Arid Environment ◽

Water Project ◽

Semi Arid

Download Full-text

Validation and scaling of soil moisture in a semi-arid environment: SMAP validation experiment 2015 (SMAPVEX15)

Remote Sensing of Environment ◽

10.1016/j.rse.2017.04.022 ◽

2017 ◽

Vol 196 ◽

pp. 101-112 ◽

Cited By ~ 39

Author(s):

Andreas Colliander ◽

Michael H. Cosh ◽

Sidharth Misra ◽

Thomas J. Jackson ◽

Wade T. Crow ◽

...

Keyword(s):

Soil Moisture ◽

Arid Environment ◽

Validation Experiment ◽

Semi Arid

Download Full-text

Improvement of the Soil Moisture Retrieval Procedure Based on the Integration of UAV Photogrammetry and Satellite Remote Sensing Information

Remote Sensing ◽

10.3390/rs13244968 ◽

2021 ◽

Vol 13 (24) ◽

pp. 4968

Author(s):

Amal Chakhar ◽

David Hernández-López ◽

Rocío Ballesteros ◽

Miguel A. Moreno

Keyword(s):

Soil Moisture ◽

Bare Soil ◽

Arid Environment ◽

Early Growth ◽

Precise Determination ◽

Growth Stages ◽

Crop Fields ◽

Moisture Conditions ◽

Semi Arid ◽

Cultivated Soils

In countries characterized by arid and semi-arid climates, a precise determination of soil moisture conditions on the field scale is critically important, especially in the first crop growth stages, to schedule irrigation and to avoid wasting water. The objective of this study was to apply the operative methodology that allowed surface soil moisture (SSM) content in a semi-arid environment to be estimated. SSM retrieval was carried out by combining two scattering models (IEM and WCM), supplied by backscattering coefficients at the VV polarization obtained from the C-band Synthetic Aperture Radar (SAR), a vegetation descriptor NDVI obtained from the optical sensor, among other essential parameters. The inversion of these models was performed by Neural Networks (NN). The combined models were calibrated by the Sentinel 1 and Sentinel 2 data collected on bare soil, and in cereal, pea and onion crop fields. To retrieve SSM, these scattering models need accurate measurements of the roughness surface parameters, standard deviation of the surface height (hrms) and correlation length (L). This work used a photogrammetric acquisition system carried on Unmanned Aerial Vehicles (UAV) to reconstruct digital surface models (DSM), which allowed these soil roughness parameters to be acquired in a large portion of the studied fields. The obtained results showed that the applied improved methodology effectively estimated SSM on bare and cultivated soils in the principal early growth stages. The bare soil experimentation yielded an R2 = 0.74 between the estimated and observed SSMs. For the cereal field, the relation between the estimated and measured SSMs yielded R2 = 0.71. In the experimental pea fields, the relation between the estimated and measured SSMs revealed R2 = 0.72 and 0.78, respectively, for peas 1 and peas 2. For the onion experimentation, the highest R2 equaled 0.5 in the principal growth stage (leaf development), but the crop R2 drastically decreased to 0.08 in the completed growth phase. The acquired results showed that the applied improved methodology proves to be an effective tool for estimating the SSM on bare and cultivated soils in the principal early growth stages.

Download Full-text