Estimating root zone soil moisture at distant sites using MODIS NDVI and EVI in a semi-arid region of southwestern USA

2010 ◽  
Vol 5 (5) ◽  
pp. 400-409 ◽  
Author(s):  
Mark T. Schnur ◽  
Hongjie Xie ◽  
Xianwei Wang
Keyword(s):  
Soil Moisture ◽  
Arid Region ◽  
Root Zone ◽  
Modis Ndvi ◽  
Southwestern Usa ◽  
Semi Arid Region ◽  
Semi Arid

scholarly journals An Optimal Irrigation Scheduling for Drip Irrigated Onion in A- Semi-Arid Region Using the Computer Program CROPWAT 8.0

2020 ◽  
pp. 96-105
Author(s):  
P. R. Anjitha Krishna ◽  
B. Maheshwara Babu ◽  
A. T. Dandekar ◽  
R. H. Rajkumar ◽  
G. Ramesh ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Arid Region ◽  
Root Zone ◽  
Management Strategies ◽  
Field Capacity ◽  
Irrigation Scheduling ◽  
Water Requirement ◽  
Estimation Model ◽  
Semi Arid Region ◽  
Semi Arid

Efficient utilization of available water resources requires appropriate management strategies considering the changing environmental conditions. The present study used a widely adopted crop water requirement estimation model-CROPWAT 8.0 for estimation and scheduling of irrigation requirement for onion crop grown under Vertisol in the Rabi season in the semi-arid region of Raichur district. The soil moisture at the root zone was not allowed to fall below 50% depletion. The irrigation events brought the soil moisture back to the field capacity level. The total water requirement for the 1st and 2nd seasons was 428.77 mm and 399.98 mm respectively at 90% irrigation efficiency. CROPWAT based two days irrigation scheduling scenario was found to be appropriate to maintain optimal soil moisture range within the crop root zone at different crop stages.

scholarly journals Drying and Wetting Trends and Vegetation Covariations in the Drylands of China

Water ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 933 ◽  
Author(s):  
Chuanzhuang Liang ◽  
Tiexi Chen ◽  
Han Dolman ◽  
Tingting Shi ◽  
Xueqiong Wei ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Arid Region ◽  
Vegetation Index ◽  
Root Zone ◽  
Regional Scale ◽  
Climatic Research Unit ◽  
Drought Indices ◽  
Drought Severity ◽  
Semi Arid Region ◽  
Semi Arid

The semi-arid and arid drylands of China, which are located in the inland region of Eurasia, have experienced rapid climate change. Some regions in particular, have shown upward trends in the observational records of precipitation. However, there is more to drying and wetting than just changes in precipitation which still have large uncertainties. Coherent results, however, can be obtained, at the regional scale, with the use of multiple indices as shown in the recent literature. We divided the drylands of China into three sub-regions, i.e., a semi-arid (SA), an eastern-arid (EA) and a western-arid (WA) region. Precipitation from the China Meteorological Administration (CMA) and Climatic Research Unit (CRU), statistical and physical drought indices, including the Standardized Precipitation Evapotranspiration Index (SPEI), the Palmer Drought Severity Index (PDSI), self-calibrating PDSI (sc_PDSI), Root zone soil moisture (Root_sm) and Surface soil moisture (Surf_sm) from Global Land Evaporation Amsterdam Model (GLEAM), and Normalized Difference Vegetation Index (NDVI) were used to identify temporal and spatial patterns in drying and wetting. Data were selected from 1982–2012, in line with the availability of the remotely sensed vegetation data. Results show that the drylands of China exhibits a pattern of wetting in the west and drying in the east. The semi-arid region in the east is becoming drier and the drought area is increasing, with the values of CMA_P, CRU_P, PDSI, sc_PDSI, SPEI-01,SPEI-06, SPEI-12, Root_sm, Surf_sm at −1.064 mm yr−1, −0.834 mm yr−1, −0.050 yr−1 (p < 0.1), −0.174 yr−1 (p < 0.1), −0.014 yr−1, −0.06, −0.021 (p < 0.1), −0.257×10−3 m3 m−3 yr−1, −0.024×10−3 m3 m−3 yr−1, respectively. The arid region generally exhibits a wetting trend, while the area in drought declines only in the western arid region, but not in the eastern arid part. In the semi-arid region, growing season (May to September) NDVI is significantly correlated (p < 0.1) with eight out of nine indicators. We show in this study that the semi-arid region needs more study to protect the vegetation ecosystem and the water resources.

scholarly journals Soil Moisture and Irrigation Mapping in A Semi-Arid Region, Based on the Synergetic Use of Sentinel-1 and Sentinel-2 Data

2018 ◽  
Vol 10 (12) ◽  
pp. 1953 ◽  
Author(s):  
Safa Bousbih ◽  
Mehrez Zribi ◽  
Mohammad El Hajj ◽  
Nicolas Baghdadi ◽  
Zohra Lili-Chabaane ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Arid Region ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Cloud Model ◽  
Remotely Sensed Data ◽  
Semi Arid Region ◽  
Semi Arid ◽  
Sentinel 2 ◽  
Reference Fields

This paper presents a technique for the mapping of soil moisture and irrigation, at the scale of agricultural fields, based on the synergistic interpretation of multi-temporal optical and Synthetic Aperture Radar (SAR) data (Sentinel-2 and Sentinel-1). The Kairouan plain, a semi-arid region in central Tunisia (North Africa), was selected as a test area for this study. Firstly, an algorithm for the direct inversion of the Water Cloud Model (WCM) was developed for the spatialization of the soil water content between 2015 and 2017. The soil moisture retrieved from these observations was first validated using ground measurements, recorded over 20 reference fields of cereal crops. A second method, based on the use of neural networks, was also used to confirm the initial validation. The results reported here show that the soil moisture products retrieved from remotely sensed data are accurate, with a Root Mean Square Error (RMSE) of less than 5% between the two moisture products. In addition, the analysis of soil moisture and Normalized Difference Vegetation Index (NDVI) products over cultivated fields, as a function of time, led to the classification of irrigated and rainfed areas on the Kairouan plain, and to the production of irrigation maps at the scale of individual fields. This classification is based on a decision tree approach, using a combination of various statistical indices of soil moisture and NDVI time series. The resulting irrigation maps were validated using reference fields within the study site. The best results were obtained with classifications based on soil moisture indices only, with an accuracy of 77%.

Spatial and temporal dynamics of soil moisture for surfaces with a change in land use in the semi-arid region of Brazil

CATENA ◽  
2020 ◽  
Vol 188 ◽  
pp. 104457 ◽  
Author(s):  
Maria Gabriela de Queiroz ◽  
Thieres George Freire da Silva ◽  
Sérgio Zolnier ◽  
Alexandre Maniçoba da Rosa Ferraz Jardim ◽  
Carlos André Alves de Souza ◽  
...  
Keyword(s):  
Land Use ◽  
Soil Moisture ◽  
Arid Region ◽  
Temporal Dynamics ◽  
Spatial And Temporal Dynamics ◽  
Semi Arid Region ◽  
Semi Arid

Quantification of irrigation water using remote sensing of soil moisture in a semi-arid region

2019 ◽  
Vol 231 ◽  
pp. 111226 ◽  
Author(s):  
Ehsan Jalilvand ◽  
Masoud Tajrishy ◽  
Sedigheh Alsadat Ghazi Zadeh Hashemi ◽  
Luca Brocca
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Irrigation Water ◽  
Arid Region ◽  
Semi Arid Region ◽  
Semi Arid

scholarly journals Semi-Arid Region Soil Moisture Prediction using Multivariate Regression

2019 ◽  
Vol 8 (4) ◽  
pp. 12457-12460
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Soil Properties ◽  
Arid Region ◽  
Soil Moisture Content ◽  
Linear Regression Analysis ◽  
Organic Content ◽  
Dry Density ◽  
Semi Arid Region ◽  
Semi Arid

The Water Scarcity is a prominent feature in Arid and Semi-Arid region. Soil moisture content is significant factor in deciding vegetation growth and also affects the performance of any water harvesting system in place. This paper evaluates the interrelationship of Soil properties with Soil Moisture content. The study covers about 13 soil Samples from Single Watershed. The soil properties covered in the study are Conductivity, pH, Bulk Density, Dry Density, Specific gravity, organic content, void ratio, and Moisture Content. Multiple linear regression analysis was done to determine significance of each soil properties for soil moisture content as individual and as whole. Modelling was done based on soil characteristics to predict Soil Moisture. Principal Component Analysis was performed to identify most significant soil properties responsible for variation of prediction of Soil Moisture content. The Correlation between location topography and Moisture Content was obtained through Cluster Analysis.

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