scholarly journals Study on soil moisture by thermal infrared data

2013 ◽  
Vol 17 (5) ◽  
pp. 1375-1381 ◽  
Author(s):  
Jun He ◽  
Xiao-Hua Yang ◽  
Shi-Feng Huang ◽  
Chong-Li Di ◽  
Ying Mei
Keyword(s):  
Soil Moisture ◽  
Large Scale ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Soil Layer ◽  
Growth Period ◽  
Vegetation Growth ◽  
Term Monitoring ◽  
Infrared Data

Information on soil moisture is important for environment management. This study bases on the daily observation to study the normalized difference vegetation index and to classify the index data. The results indicate that: (1) the index is able to adequately reflect the changes of soil moisture content in 10 cm and 20 cm thickness of soil layer during the vegetation growth period and (2) Information on soil moisture can be used for regional drought monitoring. The method can be extended for long-term monitoring of droughts over large-scale regions.

scholarly journals Satellite-Derived Estimation of Grassland Aboveground Biomass in the Three-River Headwaters Region of China during 1982–2018

2021 ◽  
Vol 13 (15) ◽  
pp. 2993
Author(s):  
Ruiyang Yu ◽  
Yunjun Yao ◽  
Qiao Wang ◽  
Huawei Wan ◽  
Zijing Xie ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Aboveground Biomass ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Growth Period ◽  
Spatiotemporal Variation ◽  
Coefficient Of Determination ◽  
Gradient Boosting ◽  
Ancillary Data

The long-term estimation of grassland aboveground biomass (AGB) is important for grassland resource management in the Three-River Headwaters Region (TRHR) of China. Due to the lack of reliable grassland AGB datasets since the 1980s, the long-term spatiotemporal variation in grassland AGB in the TRHR remains unclear. In this study, we estimated AGB in the grassland of 209,897 km2 using advanced very high resolution radiometer (AVHRR), MODerate-resolution Imaging Spectroradiometer (MODIS), meteorological, ancillary data during 1982–2018, and 75 AGB ground observations in the growth period of 2009 in the TRHR. To enhance the spatial representativeness of ground observations, we firstly upscaled the grassland AGB using a gradient boosting regression tree (GBRT) model from ground observations to a 1 km spatial resolution via MODIS normalized difference vegetation index (NDVI), meteorological and ancillary data, and the model produced validation results with a coefficient of determination (R2) equal to 0.76, a relative mean square error (RMSE) equal to 88.8 g C m−2, and a bias equal to −1.6 g C m−2 between the ground-observed and MODIS-derived upscaled AGB. Then, we upscaled grassland AGB using the same model from a 1 km to 5 km spatial resolution via AVHRR NDVI and the same data as previously mentioned with the validation accuracy (R2 = 0.74, RMSE = 57.8 g C m−2, and bias = −0.1 g C m−2) between the MODIS-derived reference and AVHRR-derived upscaled AGB. The annual trend of grassland AGB in the TRHR increased by 0.37 g C m−2 (p < 0.05) on average per year during 1982–2018, which was mainly caused by vegetation greening and increased precipitation. This study provided reliable long-term (1982–2018) grassland AGB datasets to monitor the spatiotemporal variation in grassland AGB in the TRHR.

scholarly journals Satellite-Observed Effects from Ozone Pollution and Climate Change on Growing-Season Vegetation Activity over China during 1982–2020

Atmosphere ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1390
Author(s):  
Zhaosheng Wang
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Large Scale ◽  
Vegetation Index ◽  
Growing Season ◽  
Ozone Pollution ◽  
Vegetation Growth ◽  
Vegetation Activity ◽  
The Impact

Remote sensing vegetation index data contain important information about the effects of ozone pollution, climate change and other factors on vegetation growth. However, the absence of long-term observational data on surface ozone pollution and neglected air pollution-induced effects on vegetation growth have made it difficult to conduct in-depth studies on the long-term, large-scale ozone pollution effects on vegetation health. In this study, a multiple linear regression model was developed, based on normalized difference vegetation index (NDVI) data, ozone mass mixing ratio (OMR) data at 1000 hPa, and temperature (T), precipitation (P) and surface net radiation (SSR) data during 1982–2020 to quantitatively assess the impact of ozone pollution and climate change on vegetation growth in China on growing season. The OMR data showed an increasing trend in 99.9% of regions in China over the last 39 years, and both NDVI values showed increasing trends on a spatial basis with different ozone pollution levels. Additionally, the significant correlations between NDVI and OMR, temperature and SSR indicate that vegetation activity is closely related to ozone pollution and climate change. Ozone pollution affected 12.5% of NDVI, and climate change affected 26.7% of NDVI. Furthermore, the effects from ozone pollution and climate change on forest, shrub, grass and crop vegetation were evaluated. Notably, the impact of ozone pollution on vegetation growth was 0.47 times that of climate change, indicating that the impact of ozone pollution on vegetation growth cannot be ignored. This study not only deepens the understanding of the effects of ozone pollution and climate change on vegetation growth but also provides a research framework for the large-scale monitoring of air pollution on vegetation health using remote sensing vegetation data.

scholarly journals Rice Mapping and Growth Monitoring Based on Time Series GF-6 Images and Red-Edge Bands

2021 ◽  
Vol 13 (4) ◽  
pp. 579
Author(s):  
Xueqin Jiang ◽  
Shenghui Fang ◽  
Xia Huang ◽  
Yanghua Liu ◽  
Linlin Guo
Keyword(s):  
Time Series ◽  
Precision Agriculture ◽  
Large Scale ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Southern China ◽  
Growth Period ◽  
Growth Monitoring ◽  
Rice Growth ◽  
Red Edge

Accurate rice mapping and growth monitoring are of great significance for ensuring food security and agricultural sustainable development. Remote sensing (RS), as an efficient observation technology, is expected to be useful for rice mapping and growth monitoring. Due to the fragmented distribution of paddy fields and the undulating terrain in Southern China, it is very difficult in rice mapping. Moreover, there are many crops with the same growth period as rice, resulting in low accuracy of rice mapping. We proposed a red-edge decision tree (REDT) method based on the combination of time series GF-6 images and red-edge bands to solve this problem. The red-edge integral and red-edge vegetation index integral were computed by using two red-edge bands derived from GF-6 images to construct the REDT. Meanwhile, the conventional method based on time series normalized difference vegetation index (NDVI), normalized difference water index (NDWI), enhanced vegetation index (EVI) (NNE) was employed to compare the effectiveness of rice mapping. The results indicated that the overall accuracy and Kappa coefficient of REDT ranged from 91%–94% and 0.82–0.87, improving about 7% and 0.15 compared with the NNE method. This proved that the proposed technology was able to efficiently solve the problem of rice mapping on a large scale and regions with fragmented landscapes. Additionally, two red-edge bands of GF-6 images were applied to monitor rice growth. It concluded that the two red-edge bands played different roles in rice growth monitoring. The red-edge bands of GF-6 images were superior in rice mapping and growth monitoring. Further study needs to develop more vegetation indices (VIs) related to the red-edge to make the best use of red-edge characteristics in precision agriculture.

scholarly journals Evaluation of Fengyun-3C Soil Moisture Products Using In situ Data from the Chinese Automatic Soil Moisture Observation Stations: A Case Study in Henan Province, China

2018 ◽  
Author(s):  
Yongchao Zhu ◽  
Simon Pearson ◽  
Dongli Wu ◽  
Ruijing Sun ◽  
Shibo Fang
Keyword(s):  
Soil Moisture ◽  
Correlation Coefficient ◽  
Large Scale ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Density ◽  
Statistical Parameters ◽  
In Situ Data ◽  
Moderate Resolution Imaging Spectroradiometer

Soil moisture (SM) products derived from passive satellite missions are playing an increasingly important role in agricultural applications, especially in crop monitoring and disaster warning. Evaluating the dependability of those products before they can be used on a large scale is crucial. In this study, we assessed the level 2 (L2) SM product from the Chinese Fengyun-3C (FY-3C) radiometer against in situ measurements collected from the Chinese Automatic Soil Moisture Observation Stations (CASMOS) during a one-year period from January 1 to December 31, 2016 in Henan, which is an agricultural province in China. Four statistical parameters were used to evaluate the products&rsquo; reliability: mean difference, root-mean-square error (RMSE), unbiased RMSE (ubRMSE), and the correlation coefficient. These statistical indicators revealed that the FY-3C L2 SM product generally did not agree with the in situ SM data from CASMOS. The time-series analysis further indicated that the correlations and estimated error were highly related to the growing periods of the crops in our study area. FY-3C L2 SM data tended to overestimate soil moisture during May, August, and September, when the crops reach their maximum vegetation density, and tended to underestimate the soil moisture content during the rest of the year. The averaged correlation coefficient between FY-3C SM and the Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index was 0.55, which demonstrates that the vegetation water content of the crops considerably influences the SM product. To improve the accuracy of the FY-3C SM product, an improved algorithm that can filter out the influences of the crops should be applied in the future.

scholarly journals Analysis of Changes in Vegetation Index Through Long-term Monitoring of Petroglyphs of Cheonjeon-ri, Ulju

2021 ◽  
Vol 37 (6) ◽  
pp. 659-669
Author(s):  
Yu Bin Ahn ◽  
Ji Hyun Yoo ◽  
Yu Gun Chun ◽  
Myeong Seong Lee
Keyword(s):  
Spectral Reflectance ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
The Other ◽  
Hyperspectral Images ◽  
Other Hand ◽  
Depth Study ◽  
Long Term Monitoring ◽  
Term Monitoring

In this study, vegetation index, the vegetation index calculated based on hyperspectral images was used to monitor Petroglyphs of Cheonjeon-ri, Ulju from 2014 to 2020. To select suitable the vegetation index for monitoring, indoor analysis was performed, and considering the sensitivity to biocontamination, Normalized Difference Vegetation Index (NDVI) and Triangular Vegetation Index (TVI) were selected. As a result of monitoring using the selected vegetation index, NDVI increased from 2014 to 2018 and then decreased in 2020, after preservation treatment. On the other hand, TVI was difficult to confirm the tendency during the monitoring. This difference was due to the variation in spectral reflectance according to the photographing conditions by year. Therefore NDVI is less sensitive to spectral reflectance deviation than TVI, so it can be used for monitoring. In order for TVI to be used, however, in-depth study is needed.

scholarly journals Does Vegetation Restoration Change Regional Ecohydrological Condition at the Loess Plateau in China?

2016 ◽  
Vol 6 (4) ◽  
pp. 116
Author(s):  
Xiongwen Chen ◽  
Jianzhi Niu
Keyword(s):  
Loess Plateau ◽  
Large Scale ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Rainfall Intensity ◽  
Vegetation Restoration ◽  
Soil Water Storage ◽  
The Loess Plateau ◽  
Derived Data

The Loess Plateau is a severely eroded and very venerable area in the northwestern China. Large scale vegetation restoration has been conducted in this region during the recent decades, its effect on the regional ecohydrology is under concern. In this study, long term satellite and derived data were used to analyze regional hydrological condition at the major part of the Loess Plateau (35°-37°N and 105°-110° E). The results indicate that there was an increase in the regional normalized difference vegetation index, evapotranspiration, rainfall intensity, soil water storage (surface 1m layer) and runoff. It was also observed that the total annual precipitation did not change significantly.The possible mechanisms may be related to the complicated processes of vegetation on ecohydrology. Our results and approach may be useful to evaluate the benefits of ecological restoration and further vegetation restoration at the Loess Plateau and other regions.

scholarly journals Integration of Microwave and Optical/Infrared Derived Datasets for a Drought Hazard Inventory in a Sub-Tropical Region of India

2019 ◽  
Vol 11 (4) ◽  
pp. 439 ◽  
Author(s):  
Varsha Pandey ◽  
Prashant Srivastava
Keyword(s):  
Decision Making ◽  
Soil Moisture ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Tropical Region ◽  
Hazard Management ◽  
Drought Assessment ◽  
Drought Hazard

Drought is an intricate phenomenon assessed by analyzing several hydro-meteorological factors such as rainfall, soil moisture, temperature, evapotranspiration, vegetation cover, etc. For effective drought hazard management and preparedness, the monitoring of drought requires the evaluation of influencing factors via the Drought Hazard Inventory (DHI). The main objective of this study is to compare spatial occurrences of drought hazard with the help of microwave and Optical/Infrared datasets obtained from multiple satellites. The long-term climatology of the Tropical Rainfall Measuring Mission (TRMM) Rainfall, Climate Change Initiative soil moisture (CCI-SM) and Moderate Resolution Imaging Spectroradiometer (MODIS) derived Land Surface Temperature (LST), Evapotranspiration (ET) and Normalized Difference Vegetation Index (NDVI) were used in this study for drought hazard assessment. This study was carried out in the Bundelkhand region of Uttar Pradesh, considered as one of the most frequent and dominant drought-prone areas of India. The current study includes the Analytical Hierarchy Process (AHP) technique based on Multi-Criteria Decision Making Analysis (MCDM) for weighting assignment and decision making, while the geospatial platform was used for data layer standardization, integration, and drought assessment. The results indicate that a large percentage of area (38.05% and 27.54%, respectively) lying in the central part of Bundelkhand region is under high to extreme drought conditions, where precautionary measures are needed. To demonstrate the robustness of our results, we compare them with the long-term in-situ ground water depletion as a proxy. Finally, based on the findings of this study, we recommend the methodology for drought assessment at a larger scale, as well as in the remote areas where ground based measurements are limited.

scholarly journals Global soil moisture data derived through machine learning trained with in-situ measurements

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Sungmin O. ◽  
Rene Orth
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Large Scale ◽  
Short Term Memory ◽  
Temporal Dynamics ◽  
Soil Moisture Data ◽  
Wide Range ◽  
Global Soil

AbstractWhile soil moisture information is essential for a wide range of hydrologic and climate applications, spatially-continuous soil moisture data is only available from satellite observations or model simulations. Here we present a global, long-term dataset of soil moisture derived through machine learning trained with in-situ measurements, SoMo.ml. We train a Long Short-Term Memory (LSTM) model to extrapolate daily soil moisture dynamics in space and in time, based on in-situ data collected from more than 1,000 stations across the globe. SoMo.ml provides multi-layer soil moisture data (0–10 cm, 10–30 cm, and 30–50 cm) at 0.25° spatial and daily temporal resolution over the period 2000–2019. The performance of the resulting dataset is evaluated through cross validation and inter-comparison with existing soil moisture datasets. SoMo.ml performs especially well in terms of temporal dynamics, making it particularly useful for applications requiring time-varying soil moisture, such as anomaly detection and memory analyses. SoMo.ml complements the existing suite of modelled and satellite-based datasets given its distinct derivation, to support large-scale hydrological, meteorological, and ecological analyses.

scholarly journals Contributions of Vegetation Greening and Climate Change to Evapotranspiration Trend after Large-Scale Vegetation Restoration on the Loess Plateau, China

Water ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 1755
Author(s):  
Shuo Wang ◽  
Chenfeng Cui ◽  
Qin Dai
Keyword(s):  
Climate Change ◽  
Loess Plateau ◽  
Large Scale ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Potential Evapotranspiration ◽  
Development Trend ◽  
Future Trend ◽  
Natural Ecosystem ◽  
The Loess Plateau

Since the early 2000s, the vegetation cover of the Loess Plateau (LP) has increased significantly, which has been fully recorded. However, the effects on relevant eco-hydrological processes are still unclear. Here, we made an investigation on the changes of actual evapotranspiration (ETa) during 2000–2018 and connected them with vegetation greening and climate change in the LP, based on the remote sensing data with correlation and attribution analysis. Results identified that the average annual ETa on the LP exhibited an obvious increasing trend with the value of 9.11 mm yr−1, and the annual ETa trend was dominated by the changes of ETa in the third quarter (July, August, and September). The future trend of ETa was predicted by the Hurst exponent. Partial correlation analysis indicated that annual ETa variations in 87.8% regions of the LP were controlled by vegetation greening. Multiple regression analysis suggested that the relative contributions of potential evapotranspiration (ETp), precipitation, and normalized difference vegetation index (NDVI), to the trend of ETa were 5.7%, −26.3%, and 61.4%, separately. Vegetation greening has a close relationship with the Grain for Green (GFG) project and acts as an essential driver for the long-term development trend of water consumption on the LP. In this research, the potential conflicts of water demanding between the natural ecosystem and social-economic system in the LP were highlighted, which were caused by the fast vegetation expansion.

scholarly journals Mapping the Groundwater Level and Soil Moisture of a Montane Peat Bog Using UAV Monitoring and Machine Learning

2021 ◽  
Vol 13 (5) ◽  
pp. 907
Author(s):  
Theodora Lendzioch ◽  
Jakub Langhammer ◽  
Lukáš Vlček ◽  
Robert Minařík
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Spatial Data ◽  
Groundwater Level ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Sampling Strategy ◽  
Peat Bog ◽  
Strong Impact ◽  
Ground Truth Data

One of the best preconditions for the sufficient monitoring of peat bog ecosystems is the collection, processing, and analysis of unique spatial data to understand peat bog dynamics. Over two seasons, we sampled groundwater level (GWL) and soil moisture (SM) ground truth data at two diverse locations at the Rokytka Peat bog within the Sumava Mountains, Czechia. These data served as reference data and were modeled with a suite of potential variables derived from digital surface models (DSMs) and RGB, multispectral, and thermal orthoimages reflecting topomorphometry, vegetation, and surface temperature information generated from drone mapping. We used 34 predictors to feed the random forest (RF) algorithm. The predictor selection, hyperparameter tuning, and performance assessment were performed with the target-oriented leave-location-out (LLO) spatial cross-validation (CV) strategy combined with forward feature selection (FFS) to avoid overfitting and to predict on unknown locations. The spatial CV performance statistics showed low (R2 = 0.12) to high (R2 = 0.78) model predictions. The predictor importance was used for model interpretation, where temperature had strong impact on GWL and SM, and we found significant contributions of other predictors, such as Normalized Difference Vegetation Index (NDVI), Normalized Difference Index (NDI), Enhanced Red-Green-Blue Vegetation Index (ERGBVE), Shape Index (SHP), Green Leaf Index (GLI), Brightness Index (BI), Coloration Index (CI), Redness Index (RI), Primary Colours Hue Index (HI), Overall Hue Index (HUE), SAGA Wetness Index (TWI), Plan Curvature (PlnCurv), Topographic Position Index (TPI), and Vector Ruggedness Measure (VRM). Additionally, we estimated the area of applicability (AOA) by presenting maps where the prediction model yielded high-quality results and where predictions were highly uncertain because machine learning (ML) models make predictions far beyond sampling locations without sampling data with no knowledge about these environments. The AOA method is well suited and unique for planning and decision-making about the best sampling strategy, most notably with limited data.

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