scholarly journals Evaluation and Hydrological Application of TRMM and GPM Precipitation Products in a Tropical Monsoon Basin of Thailand

Water ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 818 ◽  
Author(s):  
Rui Li ◽  
Jiancheng Shi ◽  
Dabin Ji ◽  
Tianjie Zhao ◽  
Vichian Plermkamon ◽  
...  
Keyword(s):  
Water Management ◽  
Remote Sensing ◽  
Tropical Rainfall Measuring Mission ◽  
Microwave Remote Sensing ◽  
Infiltration Capacity ◽  
Runoff Simulation ◽  
Hydrological Simulation ◽  
Simulation Accuracy ◽  
Area Index ◽  
Dry Seasons

Watershed runoff is essential for water management. However, runoff materials are lacking in poorly gauged catchments and not always accessible. Microwave remote sensing offers emerging capabilities for hydrological simulation. In this study based on multi-satellite retrievals for Global Precipitation Measurement (IMERG), Tropical Rainfall Measuring Mission (TRMM) products, and World Meteorological Organization (WMO) interpolated precipitation data, we simulated runoff using a variable infiltration capacity (VIC) model and studied the differences among the results. Then, we analyzed the impacts of the runoff on a moderate-resolution imaging spectroradiometer vegetation leaf area index (LAI) during dry seasons. The results showed that (1) IMERG V5 and TRMM products are capable of monitoring the night-day rainfall diurnal cycle and have higher correlations than the WMO daily observation interpolations. However, the WMO shows less overestimation of total precipitation than remote-sensing precipitation; (2) in the downstream, the TRMM shows better runoff simulation accuracy in the tributaries, and the WMO shows better results in the mainstreams. Therefore, at basin outlets in mainstreams, the Nash–Sutcliffe efficiency coefficients of monthly runoff by the WMO are higher than the simulations by the TRMM; (3) for the whole basin during dry seasons, the LAI variation is correlated with the outlet runoff, which is similar to the correlation with three- to six-month accumulated precipitation. TRMM products can be used to depict both precipitation deficit and runoff deficit, which cause vegetation variations. Our research suggests the potential of microwave precipitation products for detailed watershed runoff simulations and water management.

scholarly journals Detection of Shoot Beetle Stress on Yunnan Pine Forest Using a Coupled LIBERTY2-INFORM Simulation

2018 ◽  
Vol 10 (7) ◽  
pp. 1133 ◽  
Author(s):  
Qinan Lin ◽  
Huaguo Huang ◽  
Linfeng Yu ◽  
Jingxu Wang
Keyword(s):  
Remote Sensing ◽  
Mountain Pine Beetle ◽  
Simulation Accuracy ◽  
Area Index ◽  
Stress Indicator ◽  
Leaf Chlorophyll ◽  
Pine Beetle ◽  
Yellow Index ◽  
Pine Shoot Beetles ◽  
Heterogeneous Canopies

Yunnan pine shoot beetles (PSB), Tomicus yunnanensis and Tomicus minor have spread through southwestern China in the last five years, leading to millions of hectares of forest being damaged. Thus, there is an urgent need to develop an effective approach for accurate early warning and damage assessment of PSB outbreaks. Remote sensing is one of the most efficient methods for this purpose. Despite many studies existing on the mountain pine beetle (MPB), very little work has been undertaken on assessing PSB stress using remote sensing. The objective of this paper was to develop a spectral linear mixing model aided by radiative transfer (RT) and a new Yellow Index (YI) to simulate the reflectance of heterogeneous canopies containing damaged needles and quantitatively inverse their PSB stress. The YI, the fraction of dead needles, is a physically-explicit stress indicator that represents the plot shoots damage ratio (plot SDR). The major steps of this methods include: (1) LIBERTY2 was developed to simulate the reflectance of damaged needles using YI to linearly mix the green needle spectra with the dead needle spectra; (2) LIBERTY2 was coupled with the INFORM model to scale the needle spectra to the canopy scale; and (3) a look-up table (LUT) was created against Sentinel 2 (S2) imagery and inversed leaf chlorophyll content (LCC), green leaf area index (LAI) and plot SDR. The results show that (1) LIBERTY2 effectively simulated the reflectance spectral values on infested needles (mean relative error (MRE) = 1.4–18%), and the YI can indicate the degrees of needles damage; (2) the coupled LIBERTY2-INFORM model is suitable to estimate LAI (R2 = 0.73, RMSE = 0.17 m m−2, NRMSE = 11.41% and the index of agreement (IOA) = 0.92) and LCC (R2 = 0.49, RMSE = 56.24 mg m−2, NRMSE = 25.22% and IOA = 0.72), and is better than the original LIBERTY model (LAI: R2 = 0.38, RMSE = 0.43 m m−2, NRMSE = 28.85% and IOA = 0.68; LCC: R2 = 0.34, RMSE = 76.44 mg m−2, NRMSE = 34.23% and IOA = 0.57); and (3) the inversed YI is positively correlated with the measured plot SDR (R2 = 0.40, RMSE = 0.15). We conclude that the LIBERTY2 model improved the reflectance simulation accuracy of both the needles and canopies, making it suitable for assessing PSB stress. The YI has the potential to assess PSB damage.

scholarly journals DOWNSCALING OF SMAP SOIL MOISTURE PRODUCT BY DATA FUSION WITH VIIRS LST/EVI PRODUCT

2021 ◽  
Vol XLVI-4/W5-2021 ◽  
pp. 355-360
Author(s):  
X. Lei ◽  
Y. Wang ◽  
T. Guo
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Data Fusion ◽  
Large Scale ◽  
Microwave Remote Sensing ◽  
Global Scale ◽  
Moisture Distribution ◽  
Optical Data ◽  
Hydrological Simulation ◽  
Original Dataset

Abstract. Soil moisture is an essential variable of environment and climate change, which affects the energy and water exchange between soil and atmosphere. The estimation of soil moisture is thus very important in geoscience, while at same time also challenging. Satellite remote sensing provides an efficient way for large-scale soil moisture distribution mapping, and microwave remote sensing satellites/sensors, such as Soil Moisture and Ocean Salinity (SMOS), Advanced Microwave Scanning Radiometer (AMSR), and Soil Moisture Active Passive (SMAP) satellite, are widely used to retrieve soil moisture in a global scale. However, most microwave products have relatively coarse resolution (tens of kilometres), which limits their application in regional hydrological simulation and disaster prevention. In this study, the SMAP soil moisture product with spatial resolution of 9km is downscaled to 750m by fusing with VIIRS optical products. The LST-EVI triangular space pattern provides the physical foundation for the microwave-optical data fusion, so that the downscaled soil moisture product not only matches well with the original SMAP product, but also presents more detailed distribution patterns compared with the original dataset. The results show a promising prospect to use the triangular method to produce finer soil moisture datasets (within 1 km) from the coarse soil moisture product.

High-resolution soil moisture mapping through the use of Cosmic-Ray Neutron Sensor and Sentinel-1 data for temperate and semi-arid environments

2021 ◽  
Author(s):  
Hami Said ◽  
Modou Mbaye ◽  
Lee Kheng Heng ◽  
Emil Fulajtar ◽  
Georg Weltin ◽  
...  
Keyword(s):  
Water Management ◽  
Remote Sensing ◽  
Soil Moisture ◽  
Regional Scale ◽  
Cosmic Ray ◽  
Microwave Remote Sensing ◽  
Nuclear Technology ◽  
Agricultural Water ◽  
Agricultural Water Management ◽  
Semi Arid

<p>Global climate change has a major impact on the availability of water in agriculture. Sustainable agricultural productivity to ensure food security requires good agricultural water management.</p><p>Soil moisture is one of the important variables in irrigation management, and there are many different techniques for estimating it at different scales, from point to landscape scales.</p><p>Cosmic-Ray Neutron Sensor (CRNS) technology has the capability to estimate field-scale soil moisture (SM) in large areas of up to 20 to 30 ha and has demonstrated its ability to support agricultural water management and hydrology studies. However, measurement of soil moisture on a global or regional scale can only be achieved from satellite remote sensing.</p><p>Recently, active microwave remote sensing Synthetic Aperture Radar (SAR) imaging from Sentinel-1 shows great potential for high spatial resolution soil moisture monitoring and can be the basis for producing soil moisture maps. However, these maps can be only used after calibration. Such calibration can be done through traditional, point soil moisture sampling or measurement, which is time-consuming and costly. CRNS technology can be used for calibration and validation remote sensing imagery predictions at field and area-wide level.</p><p>In this study a conversion model to retrieve soil moisture from Sentinel-1 (SAR) was developed using the VV (vertical-vertical) polarization, which is highly sensitive to soil moisture, and then calibrated and validated using CRNS data from temperate (Austria) and semi-arid (Kuwait) Environments. This study is a major step in the monitoring of soil moisture at high spatial and temporal resolution by combining remote sensing and the CRNS based nuclear technology. The preliminary results show the great potential of using nuclear technology such as CRNS for remote sensing calibration of Sentinel-1 (SAR).</p>

scholarly journals Spring Thaw and Its Effect on Terrestrial Vegetation Productivity in the Western Arctic Observed from Satellite Microwave and Optical Remote Sensing

2006 ◽  
Vol 10 (21) ◽  
pp. 1-22 ◽  
Author(s):  
J. S. Kimball ◽  
K. C. McDonald ◽  
M. Zhao
Keyword(s):  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Microwave Remote Sensing ◽  
Vegetation Productivity ◽  
Area Index ◽  
Annual Variability ◽  
Annual Productivity ◽  
Western Arctic ◽  
Satellite Microwave ◽  
Seasonal Thawing

Abstract Global satellite remote sensing records show evidence of recent vegetation greening and an advancing growing season at high latitudes. Satellite remote sensing–derived measures of photosynthetic leaf area index (LAI) and vegetation gross and net primary productivity (GPP and NPP) from the NOAA Advanced Very High Resolution Radiometer (AVHRR) Pathfinder record are utilized to assess annual variability in vegetation productivity for Alaska and northwest Canada in association with the Western Arctic Linkage Experiment (WALE). These results are compared with satellite microwave remote sensing measurements of springtime thaw from the Special Sensor Microwave Imager (SSM/I). The SSM/I-derived timing of the primary springtime thaw event was well correlated with annual anomalies in maximum LAI in spring and summer (P ≤ 0.009; n = 13), and GPP and NPP (P ≤ 0.0002) for the region. Mean annual variability in springtime thaw was on the order of ±7 days, with corresponding impacts to annual productivity of approximately 1% day−1. Years with relatively early seasonal thawing showed generally greater LAI and annual productivity, while years with delayed seasonal thawing showed corresponding reductions in canopy cover and productivity. The apparent sensitivity of LAI and vegetation productivity to springtime thaw indicates that a recent advance in the seasonal thaw cycle and associated lengthening of the potential period of photosynthesis in spring is sufficient to account for the sign and magnitude of an estimated positive vegetation productivity trend for the western Arctic from 1982 to 2000.

scholarly journals Simulating the regional water balance through hydrological model based on TRMM satellite rainfall data

2015 ◽  
Vol 12 (2) ◽  
pp. 2497-2525 ◽  
Author(s):  
D. Li ◽  
X. Ding ◽  
J. Wu
Keyword(s):  
Water Balance ◽  
Water Resource Management ◽  
Hydrological Model ◽  
Model Performance ◽  
Tropical Rainfall Measuring Mission ◽  
Rain Gauge ◽  
Rainfall Data ◽  
Hydrological Models ◽  
Runoff Simulation ◽  
Hydrological Simulation

Abstract. Spatial rainfall is a key input to Distributed Hydrological Models, which is the most important limitation for the accuracy of hydrological models. Model performance and uncertainty could increase when rain gauge is sparse. Satellite-based precipitation products would be an alternative to ground-based rainfall estimates in present and the foreseeable future, however, it is necessary to evaluate the products before further implication. The objective of this paper is to provide assessments of: (a) the Tropical Rainfall Measuring Mission (TRMM) rainfall product using gauge data, (b) the TRMM rainfall as forcing data for hydrological simulation, and (c) the role of satellite data in calculating water balance and water management. TRMM rainfall data show reasonable performances at monthly and annual scales, fitting well with surface observation-based histogram of precipitation. Satisfactory performances in monthly runoff simulation (NS = 0.50 ~ 0.70, R2 = 0.73 ~ 0.85) observed in our study indicate that the TRMM rainfall data have potential applications in driving hydrological model, water balance analysis, and basin water resource management in developing countries or remote locations, where precipitation gauges are scare.

scholarly journals Modeling Mean Radiant Temperature Distribution in Urban Landscapes Using DART

2021 ◽  
Vol 13 (8) ◽  
pp. 1443
Author(s):  
Maria Angela Dissegna ◽  
Tiangang Yin ◽  
Hao Wu ◽  
Nicolas Lauret ◽  
Shanshan Wei ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Thermal Comfort ◽  
Leaf Area ◽  
Remote Sensing Data ◽  
Outdoor Thermal Comfort ◽  
Transfer Model ◽  
Mean Radiant Temperature ◽  
Area Index ◽  
Sensing Data ◽  
Radiant Temperature

The microclimatic conditions of the urban environment influence significantly the thermal comfort of human beings. One of the main human biometeorology parameters of thermal comfort is the Mean Radiant Temperature (Tmrt), which quantifies effective radiative flux reaching a human body. Simulation tools have proven useful to analyze the radiative behavior of an urban space and its impact on the inhabitants. We present a new method to produce detailed modeling of Tmrt spatial distribution using the 3-D Discrete Anisotropic Radiation Transfer model (DART). Our approach is capable to simulate Tmrt at different scales and under a range of parameters including the urban pattern, surface material of ground, walls, roofs, and properties of the vegetation (coverage, shape, spectral signature, Leaf Area Index and Leaf Area Density). The main advantages of our method are found in (1) the fine treatment of radiation in both short-wave and long-wave domains, (2) detailed specification of optical properties of urban surface materials and of vegetation, (3) precise representation of the vegetation component, and (4) capability to assimilate 3-D inputs derived from multisource remote sensing data. We illustrate and provide a first evaluation of the method in Singapore, a tropical city experiencing strong Urban Heat Island effect (UHI) and seeking to enhance the outdoor thermal comfort. The comparison between DART modelled and field estimated Tmrt shows good agreement in our study site under clear-sky condition over a time period from 10:00 to 19:00 (R2 = 0.9697, RMSE = 3.3249). The use of a 3-D radiative transfer model shows promising capability to study urban microclimate and outdoor thermal comfort with increasing landscape details, and to build linkage to remote sensing data. Our methodology has the potential to contribute towards optimizing climate-sensitive urban design when combined with the appropriate tools.

scholarly journals An Assessment of Drought Stress in Tea Estates Using Optical and Thermal Remote Sensing

2021 ◽  
Vol 13 (14) ◽  
pp. 2730
Author(s):  
Animesh Chandra Das ◽  
Ryozo Noguchi ◽  
Tofael Ahamed
Keyword(s):  
Remote Sensing ◽  
Drought Stress ◽  
Land Surface ◽  
Climatic Factors ◽  
Standardized Precipitation Index ◽  
Landsat 8 ◽  
Drought Frequency ◽  
Moisture Index ◽  
Area Index ◽  
Drought Period

Drought is one of the detrimental climatic factors that affects the productivity and quality of tea by limiting the growth and development of the plants. The aim of this research was to determine drought stress in tea estates using a remote sensing technique with the standardized precipitation index (SPI). Landsat 8 OLI/TIRS images were processed to measure the land surface temperature (LST) and soil moisture index (SMI). Maps for the normalized difference moisture index (NDMI), normalized difference vegetation index (NDVI), and leaf area index (LAI), as well as yield maps, were developed from Sentinel-2 satellite images. The drought frequency was calculated from the classification of droughts utilizing the SPI. The results of this study show that the drought frequency for the Sylhet station was 38.46% for near-normal, 35.90% for normal, and 25.64% for moderately dry months. In contrast, the Sreemangal station demonstrated frequencies of 28.21%, 41.02%, and 30.77% for near-normal, normal, and moderately dry months, respectively. The correlation coefficients between the SMI and NDMI were 0.84, 0.77, and 0.79 for the drought periods of 2018–2019, 2019–2020 and 2020–2021, respectively, indicating a strong relationship between soil and plant canopy moisture. The results of yield prediction with respect to drought stress in tea estates demonstrate that 61%, 60%, and 60% of estates in the study area had lower yields than the actual yield during the drought period, which accounted for 7.72%, 11.92%, and 12.52% yield losses in 2018, 2019, and 2020, respectively. This research suggests that satellite remote sensing with the SPI could be a valuable tool for land use planners, policy makers, and scientists to measure drought stress in tea estates.

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