Soil moisture analysis using RADARSAT satellite image in the Choke Canyon reservoir Watershed, south Texas

2004 ◽  
Author(s):  
A. Drunpob ◽  
N.B. Chang ◽  
M. Beaman ◽  
C. Wyatt
Keyword(s):  
Soil Moisture ◽  
Satellite Image ◽  
South Texas ◽  
Moisture Analysis ◽  
Reservoir Watershed

HEALTHINESS OF OIL PALM PLANTATION TOWARDS SUSTAINABILITY OF ENVIRONMENT

2020 ◽  
Vol 7 (1) ◽  
pp. 21
Author(s):  
Faradina Marzukhi ◽  
Nur Nadhirah Rusyda Rosnan ◽  
Md Azlin Md Said
Keyword(s):  
Oil Palm ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Satellite Image ◽  
Vegetation Indices ◽  
Soil Nutrient ◽  
Oil Palm Plantation ◽  
Sustainable Environment ◽  
Moisture Analysis ◽  
The Relationship

The aim of this study is to analyse the relationship between vegetation indices of Normalized Difference Vegetation Index (NDVI) and soil nutrient of oil palm plantation at Felcra Nasaruddin Bota in Perak for future sustainable environment. The satellite image was used and processed in the research. By Using NDVI, the vegetation index was obtained which varies from -1 to +1. Then, the soil sample and soil moisture analysis were carried in order to identify the nutrient values of Nitrogen (N), Phosphorus (P) and Potassium (K). A total of seven soil samples were acquired within the oil palm plantation area. A regression model was then made between physical condition of the oil palms and soil nutrients for determining the strength of the relationship. It is hoped that the risk map of oil palm healthiness can be produced for various applications which are related to agricultural plantation.

scholarly journals Soil Moisture Analysis by Means of Multispectral Images According to Land Use and Spatial Resolution on Andosols in the Colombian Andes

2020 ◽  
Vol 10 (16) ◽  
pp. 5540 ◽  
Author(s):  
Maria Casamitjana ◽  
Maria C. Torres-Madroñero ◽  
Jaime Bernal-Riobo ◽  
Diego Varga
Keyword(s):  
Soil Moisture ◽  
Vegetation Index ◽  
Surface Soil ◽  
Vegetation Indices ◽  
Bare Soil ◽  
Multispectral Images ◽  
Surface Soil Moisture ◽  
Mountain Environments ◽  
Tropical Mountain ◽  
Moisture Analysis

Surface soil moisture is an important hydrological parameter in agricultural areas. Periodic measurements in tropical mountain environments are poorly representative of larger areas, while satellite resolution is too coarse to be effective in these topographically varied landscapes, making spatial resolution an important parameter to consider. The Las Palmas catchment area near Medellin in Colombia is a vital water reservoir that stores considerable amounts of water in its andosol. In this tropical Andean setting, we use an unmanned aerial vehicle (UAV) with multispectral (visible, near infrared) sensors to determine the correlation of three agricultural land uses (potatoes, bare soil, and pasture) with surface soil moisture. Four vegetation indices (the perpendicular drought index, PDI; the normalized difference vegetation index, NDVI; the normalized difference water index, NDWI, and the soil-adjusted vegetation index, SAVI) were applied to UAV imagery and a 3 m resolution to estimate surface soil moisture through calibration with in situ field measurements. The results showed that on bare soil, the indices that best fit the soil moisture results are NDVI, NDWI and PDI on a detailed scale, whereas on potatoes crops, the NDWI is the index that correlates significantly with soil moisture, irrespective of the scale. Multispectral images and vegetation indices provide good soil moisture understanding in tropical mountain environments, with 3 m remote sensing images which are shown to be a good alternative to soil moisture analysis on pastures using the NDVI and UAV images for bare soil and potatoes.

scholarly journals Prediction of Severe Drought Area Based on Random Forest: Using Satellite Image and Topography Data

Water ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 705 ◽  
Author(s):  
Haekyung Park ◽  
Kyungmin Kim ◽  
Dong kun Lee
Keyword(s):  
Soil Moisture ◽  
Random Forest ◽  
Meteorological Data ◽  
Satellite Image ◽  
Remote Sensing Data ◽  
Absolute Error ◽  
Severe Drought ◽  
Drought Forecasting ◽  
Moisture Index ◽  
Training Area

The uncertainty of drought forecasting based on past meteorological data is increasing because of climate change. However, agricultural droughts, associated with food resources and determined by soil moisture, must be predicted several months ahead for timely resource allocation. Accordingly, we designed a severe drought area prediction (SDAP) model for short-term drought without meteorological data. The predictions of our proposed SDAP model indicate a forecast of serious drought areas assuming non-rainfall, not a probability prediction of drought occurrence. Furthermore, this prediction provides more practical information to help with rapid water allocation during a real drought. The model structure using remote sensing data consists of two parts. First, the drought function f(x) from the training area by random forest (RF) learned the changes in the pattern of soil moisture index (SMI) from the past drought and the training performance was found to be root mean square error (RMSE) = 0.052, mean absolute error (MAE) = 0.039, R2 = 0.91. Second, derived f(x) predicted the SMI of the study area, which is 20 times larger than the training area, of the same season of another year as RMSE = 0.382, MAE = 0.375, R2 = 0.58. We also obtained the variable importance stemming from RF and discussed its meaning along with the advantages and limitations of the model, training areas selection, and prediction coverage.

scholarly journals Operational assimilation of ASCAT surface soil wetness at the Met Office

2011 ◽  
Vol 15 (8) ◽  
pp. 2729-2746 ◽  
Author(s):  
I. Dharssi ◽  
K. J. Bovis ◽  
B. Macpherson ◽  
C. P. Jones
Keyword(s):  
Soil Moisture ◽  
Surface Soil ◽  
Positive Impact ◽  
Surface Soil Moisture ◽  
Moisture Analysis ◽  
Analysis Scheme ◽  
Temperature And Humidity ◽  
Global Soil ◽  
Soil Wetness ◽  
Screen Temperature

Abstract. Currently, no extensive, near real time, global soil moisture observation network exists. Therefore, the Met Office global soil moisture analysis scheme has instead used observations of screen temperature and humidity. A number of new space-borne remote sensing systems, operating at microwave frequencies, have been developed that provide a more direct retrieval of surface soil moisture. These systems are attractive since they provide global data coverage and the horizontal resolution is similar to weather forecasting models. Several studies show that measurements of normalised backscatter (surface soil wetness) from the Advanced Scatterometer (ASCAT) on the meteorological operational (MetOp) satellite contain good quality information about surface soil moisture. This study describes methods to convert ASCAT surface soil wetness measurements to volumetric surface soil moisture together with bias correction and quality control. A computationally efficient nudging scheme is used to assimilate the ASCAT volumetric surface soil moisture data into the Met Office global soil moisture analysis. This ASCAT nudging scheme works alongside a soil moisture nudging scheme that uses observations of screen temperature and humidity. Trials, using the Met Office global Unified Model, of the ASCAT nudging scheme show a positive impact on forecasts of screen temperature and humidity for the tropics, North America and Australia. A comparison with in-situ soil moisture measurements from the US also indicates that assimilation of ASCAT surface soil wetness improves the soil moisture analysis. Assimilation of ASCAT surface soil wetness measurements became operational during July 2010.

scholarly journals Evaluation of the Optimum Interpolation and Nudging Techniques for Soil Moisture Analysis Using FIFE Data

2000 ◽  
Vol 128 (6) ◽  
pp. 1733-1756 ◽  
Author(s):  
Hervé Douville ◽  
Pedro Viterbo ◽  
Jean-François Mahfouf ◽  
Anton C. M. Beljaars
Keyword(s):  
Soil Moisture ◽  
Optimum Interpolation ◽  
Moisture Analysis

A Global Root-Zone Soil Moisture Analysis Using Simulated L-band Brightness Temperature in Preparation for the Hydros Satellite Mission

2006 ◽  
Vol 7 (5) ◽  
pp. 1126-1146 ◽  
Author(s):  
G. Balsamo ◽  
J-F. Mahfouf ◽  
S. Bélair ◽  
G. Deblonde
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Brightness Temperature ◽  
Land Surface ◽  
Root Zone ◽  
Satellite Observations ◽  
Reference State ◽  
Observation Error ◽  
Moisture Analysis ◽  
L Band

Abstract The aim of this study is to test a land data assimilation prototype for the production of a global daily root-zone soil moisture analysis. This system can assimilate microwave L-band satellite observations such as those from the future Hydros NASA mission. The experiments are considered in the framework of the Interaction Soil Biosphere Atmosphere (ISBA) land surface scheme used operationally at the Meteorological Service of Canada for regional and global weather forecasting. A land surface reference state is obtained after a 1-yr global land surface simulation, forced by near-surface atmospheric fields provided by the Global Soil Wetness Project, second initiative (GSWP-2). A radiative transfer model is applied to simulate the microwave L-band passive emission from the surface. The generated brightness temperature observations are distributed in space and time according to the satellite trajectory specified by the Hydros mission. The impact of uncertainties related to the satellite observations, the land surface, and microwave emission models is investigated. A global daily root-zone soil moisture analysis is produced with a simplified variational scheme. The applicability and performance of the system are evaluated in a data assimilation cycle in which the L-band simulated observations, generated from a land surface reference state, are assimilated to correct a prescribed initial root-zone soil moisture error. The analysis convergence is satisfactory in both summer and winter cases. In summer, when considering a 3-K observation error, 90% of land surface converges toward the reference state with a soil moisture accuracy better than 0.04 m3 m−3 after a 4-week assimilation cycle. A 5-K observation error introduces 1-week delay in the convergence. A study of the analysis error statistics is performed for understanding the properties of the system. Special features associated with the interactions between soil water and soil ice, and the presence of soil moisture vertical gradients, are examined.

scholarly journals Towards a Kalman Filter based soil moisture analysis system for the operational ECMWF Integrated Forecast System

2009 ◽  
Vol 36 (10) ◽  
Author(s):  
M. Drusch ◽  
K. Scipal ◽  
P. de Rosnay ◽  
G. Balsamo ◽  
E. Andersson ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Kalman Filter ◽  
Forecast System ◽  
Moisture Analysis ◽  
Analysis System

scholarly journals Estimation of Indonesian Peat Forest Carbon Emissions based on Soil Moisture Active Passive (SMAP) Satellite Image

Agromet ◽  
2019 ◽  
Vol 33 (1) ◽  
pp. 1-7
Author(s):  
A Awaluddin ◽  
Albertus Sulaiman
Keyword(s):  
Soil Moisture ◽  
Water Table ◽  
Carbon Emissions ◽  
Carbon Emission ◽  
Unsolved Problem ◽  
Satellite Image ◽  
Empirical Relation ◽  
Forest Carbon ◽  
Tropical Peatland

Calculation of carbon emission in tropical peatland forest still unsolved problem. In this paper, we propose a method to calculate carbon emission by using Soil Moisture Active Passive (SMAP) satellite. The SMAP images on January  2018 enhanced 1.3 radiometer Global Daily 9 km, EASE grid overlayed with peat map. The water table mapping obtained by using empirical relation between soil moisture and water table in January 2018 shows a pattern according to some observation. The carbon emission map on January 2018 shows the average is about 280 gC km-2.

scholarly journals Impact of precipitation and increasing temperatures on drought trends in eastern Africa

2021 ◽  
Vol 12 (1) ◽  
pp. 17-35
Author(s):  
Sarah F. Kew ◽  
Sjoukje Y. Philip ◽  
Mathias Hauser ◽  
Mike Hobbins ◽  
Niko Wanders ◽  
...  
Keyword(s):  
Soil Moisture ◽  
A Priori ◽  
Agricultural Drought ◽  
Eastern Africa ◽  
Precipitation Trend ◽  
Crop Failure ◽  
Evaporative Demand ◽  
Clear Trend ◽  
Moisture Analysis ◽  
The Impact

Abstract. In eastern Africa droughts can cause crop failure and lead to food insecurity. With increasing temperatures, there is an a priori assumption that droughts are becoming more severe. However, the link between droughts and climate change is not sufficiently understood. Here we investigate trends in long-term agricultural drought and the influence of increasing temperatures and precipitation deficits. Using a combination of models and observational datasets, we studied trends, spanning the period from 1900 (to approximate pre-industrial conditions) to 2018, for six regions in eastern Africa in four drought-related annually averaged variables: soil moisture, precipitation, temperature, and evaporative demand (E0). In standardized soil moisture data, we found no discernible trends. The strongest influence on soil moisture variability was from precipitation, especially in the drier or water-limited study regions; temperature and E0 did not demonstrate strong relations to soil moisture. However, the error margins on precipitation trend estimates are large and no clear trend is evident, whereas significant positive trends were observed in local temperatures. The trends in E0 are predominantly positive, but we do not find strong relations between E0 and soil moisture trends. Nevertheless, the E0 trend results can still be of interest for irrigation purposes because it is E0 that determines the maximum evaporation rate. We conclude that until now the impact of increasing local temperatures on agricultural drought in eastern Africa is limited and we recommend that any soil moisture analysis be supplemented by an analysis of precipitation deficit.

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