Prediction of subsurface water level change from satellite data     

Suphan Saykawlard  Kiyoshi Honda  Ashim Das Gupta  Apisit Eiumnoh  Xiaoyong Chen 《水文研究》2005,19(4):947-954

This study explores the potential for predicting the spatial variation in subsurface water level change with crop growth stage from satellite data in Thabua Irrigation Project, situated in the northern central region of Thailand. The relationship between subsurface water level change from pumping water to irrigate rice in the dry season and the age of the rice was analysed. The spatial model of subsurface water level change was developed from the classification using greenness or (normalized difference vegetation index NDVI) derived from Landsat 5 Thematic Mapper data. The NDVI of 52 rice fields was employed to assess its relationship to the age of the rice. It was found that NDVI and rice age have a good correlation (R² = 0·73). The low NDVI values (−0·059 to 0·082) in these fields were related to the young rice stage (0–30 days). NDVI and subsurface water level change were also correlated in this study and found to have a high correlation (Water level change (m day⁻¹) = 0·3442 × NDVI − 0·0372; R² = 0·96). From this model, the water level change caused by rice at different growth stages was derived. This was used to show the spatial variation of water level change in the project during the 1998–99 dry‐season cropping. This simple method of using NDVI relationships with water level change and crop growth stages proves to be useful in determining the areas prone to excessive lowering of the subsurface water level during the dry season. This could assist in the appropriate planning of the use of subsurface water resources in dry‐season cropping. Copyright © 2004 John Wiley & Sons, Ltd.  相似文献   

Modelling electrical conductivity of soil from backscattering coefficient of microwave remotely sensed data using artificial neural network     

Walaiporn Phonphan  Nitin K. Tripathi  Taravudh Tipdecho  Apisit Eiumnoh 《国际地球制图》2013,28(8):842-859

Soil salinity is one of the main agricultural problems which expand to larger areas. Soil scientists categorize salinity level by electrical conductivity (EC) measurement. However, field measurements of EC require extensive time, cost and experiences. Remote sensing is one suitable option to investigate and collect spatial data in larger areas. Many researches estimated soil moisture through microwave, but there are fewer studies which mentioned about direct relationship between EC and backscattering coefficient (BC). Thus, this study aims to propose the estimation of EC directly from BC of microwave. The relationship between EC obtained from field survey and BC from microwave is non-linear, artificial neural network (ANN) is one technique proposed in this study to figure out EC and BC relationship. ANN uses multilayer of interconnected processing resulting in EC value with high accuracy which is acceptable. For this reason, ANN model can be successfully utilized as an effective tool for EC estimation from microwave.  相似文献