Estimating Regional Soil Moisture Distribution Based on NDVI and Land Surface Temperature Time Series Data in the Upstream of the Heihe River Watershed, Northwest China

Bai, Xiaoyong; Zhang, Lanhui; He, Chansheng; Zhu, Yi

doi:10.3390/rs12152414

Cited by 20 publications

(15 citation statements)

References 49 publications

(63 reference statements)

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“…Retrieving SSM using remote sensing technology has been investigated for more than 30 years. Among the remote sensing methods, most optical methods estimate SSM by using the spectral reflectance indices, which are easy to implement but can be easily affected by weather [8]. In thermal infrared methods, SSM is mainly estimated from thermal inertia [9].…”

Section: Introductionmentioning

confidence: 99%

Retrieving Surface Soil Moisture over Wheat-Covered Areas Using Data from Sentinel-1 and Sentinel-2

Zhang

Heng

2021

Water

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Surface soil moisture (SSM) is a major factor that affects crop growth. Combined microwave and optical data have been widely used to improve the accuracy of SSM retrievals. However, the influence of vegetation indices derived from the red-edge spectral bands of multi-spectral optical data on retrieval accuracy has not been sufficiently analyzed. In this study, we retrieved soil moisture from wheat-covered surfaces using Sentinel-1/2 data. First, a modified water cloud model (WCM) was proposed to remove the influence of vegetation from the backscattering coefficient of the radar data. The vegetation fraction (FV) was then introduced in this WCM, and the vegetation water content (VWC) was calculated using a multiple linear regression model. Subsequently, the support vector regression technique was used to retrieve the SSM. This approach was validated using in situ measurements of wheat fields in Hebi, located in northern Henan Province, China. The key findings of this study are: (1) Based on vegetation indices obtained from Sentinel-2 data, the proposed VWC estimation model effectively eliminated the influence of vegetation; (2) Compared with vertical transmit and horizontal receive (VH) polarization, vertical transmit and vertical receive (VV) polarization was better for detecting changes in SSM key phenological phases of wheat; (3) The validated model indicates that the proposed approach successfully retrieved SSM in the study area using Sentinel-1 and Sentinel-2 data.

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Section: Introductionmentioning

confidence: 99%

Retrieving Surface Soil Moisture over Wheat-Covered Areas Using Data from Sentinel-1 and Sentinel-2

Zhang

Heng

2021

Water

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“…Lu et al (2006) used the ST at a depth of 0.8m in China from 1954 to 2001, found that the variation trends of ST in different regions were different. Bai et al (2020) pointed out that the previous division of ST was mostly based on administrative and mechanical grid, but changes in ST have potential effects on vegetation growth, so ST division based on vegetation area is very meaningful. Research shows that the spatial distribution of shallow ST in China varies greatly during plant growth seasons.…”

Section: The Work Ofmentioning

confidence: 99%

Analysis on the Temporal and Spatial Characteristics of the Shallow Soil Temperature of the Qinghai-Tibet Plateau

Zhang

et al. 2022

Preprint

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Shallow soil refers to the soil layer within 50 cm underground. Shallow soil temperature (ST) directly or indirectly affects many processes in the soil, such as seed germination, plant growth, water evaporation. Shallow ST is also affected by solar radiation, atmospheric circulation, air temperature and precipitation changes. Therefore, the study of shallow ST is of great significance in understanding surface energy, water cycle, ecology and climate change. This work collected the observational data from 141 meteorological stations on the Qinghai-Tibet Plateau from 1981 to 2020, analyzed the ST as well as its temporal and spatial change characteristics at different levels. The results show that: 1) The shallow ST has a gradually increasing trend from north to south, from west to east. From the perspective of time characteristics, the increasing trend is obvious. The temperature increase of 0-20cm (the surface layer of the shallow soil) is roughly the same. The average annual ST is 9.15–9.57 ℃, the interdecadal variabilities are 0.49–0.53 K/10a. The average annual ST of 40cm (the bottom layer) is 8.69 ℃, the interdecadal variability reaches by 0.98 K/10a; 2) Considering the 7 regions, the warming trend is obvious, and there are certain regional differences. The average annual ST in different regions ranges from 5.2°C (Northeast of the Plateau) to 17.1°C (Western Sichuan Plateau), with a difference of nearly 12K; the standard deviation ranges from 0.40K (Western Sichuan Plateau) to 0.61K (Qiangtang Plateau), with a difference of 0.21K. This work is significant for understanding the characteristics of the ST evolution and the land-atmosphere interaction on the Qinghai-Tibet Plateau.

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“…Refs. [ 4 , 5 , 6 , 7 , 8 , 9 ] explored the relationship between NDVI and climate influences such as climate temperature and rainfall. Some studies found that the normalized NDVI has some correlation with soil moisture [ 10 , 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

Research on Tea Tree Growth Monitoring Model Using Soil Information

Huang

Jiang

Wang

2022

Plants

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Crop growth monitoring is an important component of agricultural information, and suitable soil temperature (ST), soil moisture content (SMC) and soil electrical conductivity (SEC) play a key role in crop growth. Real-time monitoring of the three soil parameters to predict the growth of tea plantation helps tea trees grow healthily and to accurately grasp the growth trend of tea trees. In this paper, five different models based on the polynomial model and power model were used to construct the soil temperature, soil water content and soil conductivity and tea plantation growth monitoring models. Experiments proved that tea plantation growth were positively correlated with ST and negatively correlated with SMC and SEC, and among the constructed models, the ternary cubic polynomial model was the best, and R square (R2) of the constructed models were 0.6369, 0.4510 and 0.5784, respectively, indicating that SEC was the most relevant to tea plantation growth maximum. To improve the prediction accuracy, a model based on sum of soil temperature (SST), sum of soil water content (SSMC) and sum of soil conductivity (SSEC) was proposed, and the experiments also showed that the ternary cubic polynomial model was the best, with 0.9638, 0.9733 and 0.9660, respectively. At the same time, a model incorporating three parameters such as soil temperature, soil water content and soil conductivity was also suggested, with 0.6605 and 0.9761, respectively, which effectively improved the prediction accuracy. Validation experiments were conducted. Twelve data sets were utilized to verify the performance of the model. The experiments showed that the regressions in the polynomial models achieved a better prediction effect. Finally, a long short-term memory (LSTM) network prediction model optimized by the bald eagle search algorithm (BES) was also constructed, and R2, root mean square error (RMSE), mean squared error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of prediction were 0.8666, 0.0629, 0.0040, 0.0436 and 10.5257, respectively, which significantly outperformed the LSTM network and achieved better performance. The model proposed in this paper can be used to predict the actual situation during the growing period of tea leaves, which can improve the production management of tea plantations and also provide a scientific basis for accurate tea planting and a decision basis for agricultural policy formulation, as well as provide technical support for the realization of agricultural modernization.

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Estimating Regional Soil Moisture Distribution Based on NDVI and Land Surface Temperature Time Series Data in the Upstream of the Heihe River Watershed, Northwest China

Cited by 20 publications

References 49 publications

Retrieving Surface Soil Moisture over Wheat-Covered Areas Using Data from Sentinel-1 and Sentinel-2

Retrieving Surface Soil Moisture over Wheat-Covered Areas Using Data from Sentinel-1 and Sentinel-2

Analysis on the Temporal and Spatial Characteristics of the Shallow Soil Temperature of the Qinghai-Tibet Plateau

Research on Tea Tree Growth Monitoring Model Using Soil Information

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