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%.

scholarly journals Comparative Analysis of Landsat-8, Sentinel-2, and GF-1 Data for Retrieving Soil Moisture over Wheat Farmlands

2020 ◽  
Vol 12 (17) ◽  
pp. 2708 ◽  
Author(s):  
Qi Wang ◽  
Jiancheng Li ◽  
Taoyong Jin ◽  
Xin Chang ◽  
Yongchao Zhu ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Optical Sensors ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Cloud Model ◽  
Vegetation Indices ◽  
Optical Data ◽  
Landsat 8 ◽  
Different Sources ◽  
Sentinel 2

Soil moisture is an important variable in ecological, hydrological, and meteorological studies. An effective method for improving the accuracy of soil moisture retrieval is the mutual supplementation of multi-source data. The sensor configuration and band settings of different optical sensors lead to differences in band reflectivity in the inter-data, further resulting in the differences between vegetation indices. The combination of synthetic aperture radar (SAR) data with multi-source optical data has been widely used for soil moisture retrieval. However, the influence of vegetation indices derived from different sources of optical data on retrieval accuracy has not been comparatively analyzed thus far. Therefore, the suitability of vegetation parameters derived from different sources of optical data for accurate soil moisture retrieval requires further investigation. In this study, vegetation indices derived from GF-1, Landsat-8, and Sentinel-2 were compared. Based on Sentinel-1 SAR and three optical data, combined with the water cloud model (WCM) and the advanced integral equation model (AIEM), the accuracy of soil moisture retrieval was investigated. The results indicate that, Sentinel-2 data were more sensitive to vegetation characteristics and had a stronger capability for vegetation signal detection. The ranking of normalized difference vegetation index (NDVI) values from the three sensors was as follows: the largest was in Sentinel-2, followed by Landsat-8, and the value of GF-1 was the smallest. The normalized difference water index (NDWI) value of Landsat-8 was larger than that of Sentinel-2. With reference to the relative components in the WCM model, the contribution of vegetation scattering exceeded that of soil scattering within a vegetation index range of approximately 0.55–0.6 in NDVI-based models and all ranges in NDWI1-based models. The threshold value of NDWI2 for calculating vegetation water content (VWC) was approximately an NDVI value of 0.4–0.55. In the soil moisture retrieval, Sentinel-2 data achieved higher accuracy than data from the other sources and thus was more suitable for the study for combination with SAR in soil moisture retrieval. Furthermore, compared with NDVI, higher accuracy of soil moisture could be retrieved by using NDWI1 (R2 = 0.623, RMSE = 4.73%). This study provides a reference for the selection of optical data for combination with SAR in soil moisture retrieval.

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 Multi-scale estimation of surface moisture in a semi-arid region using ENVISAT ASAR radar data

2010 ◽  
Vol 7 (5) ◽  
pp. 8045-8089 ◽  
Author(s):  
M. Zribi ◽  
A. Chahbi ◽  
M. Shabou ◽  
Z. Lili-Chabaane ◽  
B. Duchemin ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Arid Region ◽  
Cloud Model ◽  
Incidence Angle ◽  
Ground Truth ◽  
Radar Signal ◽  
Envisat Asar ◽  
Semi Arid Region ◽  
Semi Arid ◽  
Moisture Variability

Abstract. In this paper, we propose an approach for the estimation and monitoring of soil moisture in a semi-arid region in North Africa, using ENVISAT ASAR images. Our approach is based on soil moisture mapping over two types of vegetation covers. The first mapping process is dedicated solely to the monitoring of moisture variability related to rainfall events. We chose to implement this analysis over areas in the "non-irrigated olive tree" class of land use. The developed approach is based on a simple linear relationship between soil moisture and the backscattered radar signal normalised at a reference incidence angle. The second process is proposed over wheat fields, using an analysis of moisture variability due to both rainfall and irrigation. A semi-empirical model, based on the water-cloud model for vegetation correction, is used to retrieve soil moisture from the radar signal. Moisture mapping is carried out over wheat fields, showing high variability between irrigated and non-irrigated wheat covers. This study is based on the reduction of a large database, including both ENVISAT ASAR and simultaneously acquired ground-truth measurements (moisture, vegetation, roughness), during the 2008–2009 vegetation cycle.

Use of MSI/Sentinel-2 and airborne LiDAR data for mapping vegetation and studying the relationships with soil attributes in the Brazilian semi-arid region

2018 ◽  
Vol 73 ◽  
pp. 179-190 ◽  
Author(s):  
Hilton Luís Ferraz da Silveira ◽  
Lênio Soares Galvão ◽  
Ieda Del’Arco Sanches ◽  
Iedo Bezerra de Sá ◽  
Tatiana Ayako Taura
Keyword(s):  
Arid Region ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Soil Attributes ◽  
Semi Arid Region ◽  
Airborne Lidar Data ◽  
Semi Arid ◽  
Sentinel 2

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
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