Abstract
As a basic agricultural parameter in the formation, transformation, and consumption of surface water resources, soil moisture has a very important influence on the vegetation growth, agricultural production, and healthy operation of regional ecosystems. The Aksu river basin is a typical semi-arid agricultural area which seasonally suffers from water shortage. Due to the lack of knowledge on soil moisture change, the water management and decision-making processes have been a difficult issue for local government. Therefore, soil moisture monitoring by remote sensing became a reasonable way to schedule crop irrigation and evaluate the irrigation efficiency. Compared to in situ measurements, the use of remote sensing for the monitoring of soil water content is convenient and can be repetitively applied over a large area. To verify the applicability of the typical drought index to the rapid acquisition of soil moisture in arid and semi-arid regions, this study simulated, compared, and validated the effectiveness of soil moisture inversion. GF-1 WFV images, Landsat 8 OLI images, and the measured soil moisture data were used to determine the Perpendicular Drought Index (PDI), the Modified Perpendicular Drought Index (MPDI), and the Vegetation Adjusted Perpendicular Drought Index (VAPDI). First, the determination coefficients of the correlation analyses on the PDI, MPDI, VAPDI, and measured soil moisture in the 0–10, 10–20, and 20–30 cm depth layers based on the GF-1 WFV and Landsat 8 OLI images were good. Notably, in the 0–10 cm depth layers, the average determination coefficient was 0.68; all models met the accuracy requirements of soil moisture inversion. Both indicated that the drought indices based on the Near Infrared (NIR)-Red spectral space derived from the optical remote sensing images are more sensitive to soil moisture near the surface layer; however, the accuracy of retrieving the soil moisture in deep layers was slightly lower in the study area. Second, in areas of vegetation coverage, MPDI and VAPDI had a higher inversion accuracy than PDI. To a certain extent, they overcame the influence of mixed pixels on the soil moisture spectral information. VAPDI modified by Perpendicular Vegetation Index (PVI) was not susceptible to vegetation saturation and, thus, had a higher inversion accuracy, which makes it performs better than MPDI’s in vegetated areas. Third, the spatial heterogeneity of the soil moisture retrieved by the GF-1 WFV and Landsat 8 OLI image were similar. However, the GF-1 WFV images were more sensitive to changes in the soil moisture, which reflected the actual soil moisture level covered by different vegetation. These results provide a practical reference for the dynamic monitoring of surface soil moisture, obtaining agricultural information and agricultural condition parameters in arid and semi-arid regions.
Highlights
Soil moisture, or soil water content, is a key factor that describes the land/atmosphere energy transfer and the water cycle, as it plays a vital role in near-surface water circulation and ecosystem functions [1]
Besides the fitted Perpendicular Drought Index (PDI) and Modified Perpendicular Drought Index (MPDI) models for the 20–30 cm depth layer based on the GF-1 WFV images and the fitted PDI model for the 20–30 cm depth layer based on the Landsat8 OLI images, which achieved P values of
Besides the fitted PDI and MPDI models for the 20–30 cm depth layer based on the GF-1 WFV images and the fitted PDI model for the 20–30 cm depth layer based on the Landsat8 OLI images, which achieved P values of
Summary
Soil water content, is a key factor that describes the land/atmosphere energy transfer and the water cycle, as it plays a vital role in near-surface water circulation and ecosystem functions [1]. The Aksu river basin is a typical semi-arid region; it is an important agriculture area in Xinjiang Uygur Autonomous Region, China. The Aksu river basin suffers from water shortages seasonally, especially in crop growth season [5,6]. Due the lack of information on soil moisture, it is hard for the local water managers to understand which area is the most water-deficient and how much water is needed for each region. If we can acquire the soil moisture information of the entire basin, it will be convenient for us to learn which areas are the most water-deficient and give priority to the water supply in these areas. Soil moisture is important in calculating irrigation quotas; accurate and reliable irrigation quotas can help to improve the water use efficiency. Traditional in situ measurement is not applicable in a research area as broad as the Aksu river basin, and so using remote sensing has become our alternative option
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