JWSS - Journal of Water and Soil Science

Determining the cultivation crops area is important for properly supplying crops. The aim of this study was mapping the cultivation area crops in Chadian city for spring and summer during 2015 by using the time series data of the Landsat 8 satellite of OLI imagery. At first, the under cultivation area was determined by setting a low threshold in the marginal pixels of the agricultural rain fed in the spring image NDVI index. The area cultivated with wheat and alfalfa was prepared by subtracting spring and summer NDVI values. Cultivation maps, which were cultivated with potatoes, corn and orchards, were prepared using the supervised classification with the FISHER method in a step by step manner. Spring and summer cultivation maps were combined; finally, the major cultivation crops maps were produced by the hybrid classification method. Map accuracy assessment was done by producing error matrix and calculating kappa coefficient, total accuracy, commission and omission error, producer, and use accuracy; in all indices, they had an acceptable value, showing the capability of OLI and the used methods in separating each cultivation.

Soil moisture is one of the most important factors that can affect productivity in ecosystems in arid and semiarid regions. The aim of this study was to investigate soil moisture and vegetation changes in the Isfahan province at the seasonal scale. For this purpose, MODIS Land Surface Temperature (LST) and NDVI data were used to calculate the TVDI index, and the rate of soil moisture content was also measured at several soil depths including 5, 10, 20, 30 cm. in the growing season. Seasonal changes of LST and NDVI indices were also studied in different climate regions ranging from humid to hyperarid. The results showed that the changes in NDVI and LST in this region were different, depending on the climate type and soil conditions; the LST and its changes mostly depended on the amount of vegetation cover NDVI changes based on the plant phenology in humid regions, which was were greater than that in arid and semi-arid climates. Soil moisture monitoring indicated that the relationships between TDVI and different soil depths varied based on the seasonal conditions. In the early growing season, the soil moisture at the depth of 0-5 cm had a higher correlation with TVDI, but in the middle of growing season, the deeper soil moisture (10-30 cm) showed the highest correlation. Therefore, the findings of this research indicated the importance of the growing season, soil conditions and vegetation percentage and types in the soil moisture studies by using satellite data.

Due to the impact of climate change on the plant water demand and the availability of water, especially in drylands, it is vital to estimate the evapotranspiration rates accurately. In this study, the vegetation status in the marginal desert areas of Varamin Plain was studied, and the actual evapotranspiration and water demand of intercropped farms were assessed. This study also evaluated the potential relationship between the evapotranspiration of different agricultural lands and their vegetation index using remote sensing techniques. A collection of satellite images from Landsat 7 in consecutive seasons was used to determine the greenness rate of marginal desert areas during 2013 and 2014. ENVI software was used for the image processing, which included geometric corrections and atmospheric corrections, to develop NDVI maps. Also, weather data and crop properties of Varamin Plain were collected, and the actual evapotranspiration rate of plant cover was estimated using CropWat. The correlation between NDVI extracted from satellite images and the evaluated evapotranspiration rate was assessed. The results showed a strong relationship between evapotranspiration of heterogeneous agricultural lands and NDVI. This confirmed that the NDVI derived by remote sensing approach could be a useful index to evaluate vegetation status and water demand of farmlands in the desert borders.

Identification and investigation of changes in the area under cultivation of various crops seem to be essential for the management supply of crop production. In this study, r to identify and investigate change of the area under cultivation in major crop Hoseynabade Mishmast region in Qom province, we used the time series images of OLI and ETM sensors of landsat 8 and 7satellites, according to the crop calendar of this region. By using the vegetation index (NDVI) in the decision tree algorithm, the thresholds of this index were adjusted according to the major crops of this region; then a map of the cultivation pattern of the crop of this region was prepared. In order to evaluate the results, the statistics of the provinces agricultural jihad were used during 2005, 2009, 2014 and 2019 crop years. The results showed that by using the threshold of NDVI index, crops in this region in 2005 included wheat and barley and alfalfa, and their areas had an error of 17/1 and 6/1 percent in comparison with the statistics of agricultural Jihad, respectively; in 2009, wheat and barley, alfalfa and corn had an error of 0/5, 9/6 and 0/1 percent. Also, in 2014, wheat and barley, alfalfa, corn and sophie crops had an error equal to 4/9, 0.4, 11/4 and 2/4 percent, and the same crops in 2019 had an error 0/04, 11/6, 1/4 and 17/5 percent; that error was not significant. According to the results, the appropriate efficiency NDVI index in estimating crop cultivation area was determined by their phenology. Also, in 2009 and 2014, corn and sophie crops were added to the regions crops, and the area under crops cultivation in 2019 was increased, as compared to 2014.

The annual soil erosion in different regions of the world has been estimated using various empirical and numerical methods whose accuracy is very dependent on their utilized parameters. One of the most common methods in the evaluation of the mean annual soil erosion especially in sheet and furrow regions is the USLE method. In this relationship, almost all factors that normally affect the soil loss process such as land cover, slope, precipitation, soil type, and support practice parameter of soil have been employed but, in this research, it was shown that the accuracy of this method in mountainous areas covered by rock and snow is somewhat low. To do this, a part of the Tibet plateau in China, where observation soil loss data were available, was selected for investigation. To implement the numerical and analytical analysis, many maps including DEM, NDVI, orientation, soil type, mean monthly and annual precipitation for 30 years were collected. For increasing the accuracy of the model, the cover management parameter was extracted from high accuracy NDVI maps and all USLE parameters were calculated in ArcGIS. The final results were shown that the amount of annual soil loss which was estimated by the USLE method is more than the observed data which were collected by Chinese researchers. This is because the large areas of the study area are covered by lichen and snow where soil loss due to the erosion process is very low but these regions cannot be recognized from NDVI maps. Also, the analysis of the NDVI maps was shown that the relationships of Fu, Patil, and Sharma were not suitable for soil loss estimation in elevated mountainous areas. If the other relationships such as Lin, Zhu, and Durigon are used for the regions with a height of more than 5500 m, a new correction coefficient needs to be used for the C factor which was calculated as 0.2 for the study area.

The ability of remote sensing (RS) in irrigation scheduling has been accepted in the world due to the collection of data on a large scale and the determination of water stress indicators with greater speed and less cost. Crop Water Stress Index (CWSI) and Water Deficit Index (WDI) are components of the most recognized water stress indices. Despite the accuracy and precision of the CWSI index that has been proven in plant irrigation scheduling, the lack of complete density of vegetation, especially in the early stages of growth, is one of the most important defects of using this method in crop irrigation scheduling. While estimating the water deficit index using remote sensing technology does not have these limitations. An experiment was performed in the crop year 98-99 in the city of Karaj to check the accuracy of this index. The amount of WDI and CWSI in a wheat field with optimized irrigation management was determined and compared and evaluated using statistical parameters. The results showed that the coefficient of explanation between these two indicators in the months of April, May, and June is 0.77, 0.85, and 0.71, respectively.