Precipitation is a function of many topographical features as well as geographical locations. The correlations between precipitation and topographical and geographical features can be used to improve estimation of precipitation distribution. In this paper, we built seasonal precipitation model based on GIS techniques in Zhejiang Province in southeastern China. Terrain variables derived from the 1 km resolution DEM are used as predictors of the seasonal precipitation, using a regression-based approach. Variables used for model development include: longitude, latitude, elevation, and distance from the nearest coast, direction to the nearest coast, slope, aspect, and the ratio of land to sea within given radii. Seasonal precipitation data, for the observation period 1971 to 2000, were assembled from 59 meteorological stations. Precipitation data from 52 meteorological stations were used to initialize the regression model. The data from the other 7 stations were retained for model validation. Seasonal precipitation surfaces were constructed using the regression equations, and refined by kriging the residuals from the regression model and subtracting the result from the predicted surface. Latitude, elevation and distance from the sea are found to be the most effective predictors of local seasonal precipitation. Validation determined that regression plus kriging predicts mean seasonal precipitation with a coefficient of determination (R<sup>2</sup>), between the estimated and observed values, of 0.546 (winter) and 0.895 (spring). A simple regression model without kriging yields less accurate results in all seasons.
The objective of this paper is to discuss the action of derivative spectra of corn in determining spectral bands that are best suited for characterizing corn agronomic parameters. The data for this study comes from filed and indoor reflectance measurements of corn by a ASD FieldSpec Pro FR. Observed corn characteristics included leaf area index, aboveground fresh biomass chlorophyll concentration and carotenoid concentration. The study shows that the derivative spectra can determine some characteristic wavelength of corn and eliminate the influence of soil background in some degree. Leaf area index, above-ground biomass, chlorophyll concentration and carotenoid concentration were strongly correlative to the red edge parameters which derive from derivative spectra of corn (the position of red edgeλ<sub>red</sub>, the slope of red edge D<sub>red</sub> and the area of red edge S<sub>red</sub>). A strong relationship with corn characteristics is located in specific narrow bands in the longer wavelength portion of red (710nm to 740nm). The study concludes it is feasible for the derivative spectra of corn to determine its some agronomic parameters.
The Relationships between rape biomass and hyperspectral vegetation indices are investigated in this paper. The data for this study comes from field hyperspectral reflectance measurements of rape during 2002-2003 growing period. Reflectance was measured in discrete narrow bands between 350 and 2500 nm. Observed rape biomass included wet biomass (WBM including leaf wet biomass-LWBM, stem wet biomass-SWBM, fruit wet biomass-FWBM), and dry biomass(DBM: including leaf dry biomass-LDBM, stem dry biomass, fruit dry biomass-FDBM). Narrow band normalized difference vegetation index (NBNDVI) and narrow band ratio vegetation index (NBRVI)involving all possible two-band combinations of discrete channels was tested. Special narrow band lambda (λ1) versus lambda (λ2) plots of R2 values illustrate the most effective wavelength combinations (λ1 and λ2) and band-width (Δλ1 and Δλ2) for predicting rape biomass at different development stage. A strong relationship with rape biomass is located in red-edge, the longer portion of red, moisture-sensitive NIR, longer portion of the blue band, the intermediate portion of SWIR, and the longer portion of SWIR.
The data for this study was collected from two-year (1999 and 2000) field experiments that based on different artificial nitrogen treatments. Linear, non-linear and stepwise multiple regression analysis were adopted for modeling The data in 1999 was utilized as training sample for modeling hyperspectra remote sensing estimation of rice aboveground fresh biomass, and the data in 2000 was evaluated and tested the models' predictive accuracy. Results of fitness analysis between hyperspectral variables and rice aboveground fresh biomass indicate that some hyperspectral characteristic variables and their combinations are closely correlated to aboveground biomass, such as red edge wavelength (λr),maximum reflectivity in green region, minimum reflectivity in red region, and the vegetation index based on the sum of first derivative spectral reflectance in blue region and that in red region. Determining the highest correlated wavebands and the best-fitting variables for raw spectra, first derivative spectra and hyperspectral characteristic variables through stepwise multiple regressions, and the results reveal that the relationship between the first derivative spectra and rice aboveground biomass is much clearer and simpler when compared with the rest. The best model for rice aboveground biomass estimation is based on the ration vegetation indices that calculated with the sum of the first derivative spectra reflectance in blue region and that in red region.
The hyperspectral reflectance of the canopy in field, the first and the third unfold leaves from the top of corn are measured indoor in different stages by a ASD FieldSpec Pro FR. The concentrations of chlorophyll and carotenoid of leaves corresponding to the spectra are determined by biochemical method. The correlation between the pigment concentrations, leaf area indices, above ground biomass and fresh leaf mass and the red edge parameters of corn are analyzed. The hyperspectral reflectance are gradually getting smaller in the visible region and bigger in the near infrared region along with growth. The difference of reflectance between in the near infrared region and in the visible region is the biggest in flowering stage. There are “two peak” phenomena for the red edge of canopy spectra of corn. These phenomena are first the clearer with growth, then the clearest in flowering stage and after that are gradually weaken. The position of red edge (λ<sub>red</sub>) of canopy spectra are between 710nm and 740nm. There are "red shift’ phenomena for λ<sub>red</sub> before flowering stage, the slope of red edge (Dλ<sub>red</sub>) and the area of red edge (S<sub>red</sub>) before the elongation stage, but are gradually smaller and "blue shift’ after flowering stage for the slope of red edge (Dλ<sub>red</sub>) and the area of red edge (S<sub>red</sub>) of the canopy spectra. The leaf area indices (LAI), above ground fresh biomass, above ground dry biomass and fresh leaf mass are very significantly correlative to the red edge parameters <sub>λred</sub>, Dλ<sub>red</sub> and S<sub>red</sub> of the canopy spectra, and the concentrations of chlorophyll-a, chlorophyll-b, total chlorophyll and carotenoid of leaves also significantly correlative to their red edge parameters λ<sub>red</sub> and Dλ<sub>red</sub>. These prove that the red edge parameters (λ<sub>red</sub>, Dλ<sub>red</sub> and S<sub>red</sub>) can be used to estimate LAI, above ground biomass and fresh leaf mass. The parameters λ<sub>red</sub> and Dλ<sub>red</sub> can be used to estimate the concentrations chlorophyll and carotenoid of leaves for corn.
The Relationships between Narrow Band Normalized Vegetation Index and Rice Agronomics Variables are reported in this paper. The data for this study comes from ground-level hyperspectral reflectance measurements of rice at different stage of 2002 growing period. Reflectance was measured in discrete narrow bands between 350 and 2500 nm. Observed rice agronomics variables included wet biomass, leaf area index. Narrow band normalized difference vegetation index (NBNDVI) involving all possible two-band combinations of discrete channels was tested. Special narrow band lambda (λ<sub>1</sub>) versus lambda (λ<sub>2</sub>) plots of R<sup>2</sup> values illustrates the most effective wavelength combinations (λ<sub>1</sub> and λ<sub>2</sub>) and band-width (Δλ<sub>1</sub> and Δλ<sub>2</sub>) for predicting rice agronomics variables at different development stage. The best of the NBNDVI models explained 53% to 83% variability rice agronomics variables at different development stage. A strong relationship with rice agronomics variables is located in red-edge, 700 nm to 750 nm, the longer portion of red, 650 nm to 700 nm, moisture-sensitive NIR, 950 nm to 1000 nm, longer portion of the blue band, 450 nm to 500 nm, longer portion of the green, 550 nm to 600 nm, the intermediate portion of SWIR, 1600 nm to 1700 nm, and the longer portion of SWIR, 2150 nm to 2250 nm.
The canopy spectra of rice under different nitrogen levels were studied. Some red edge parameters in the first derivative reflectance curve (wavelength, amplitude and area of the red edge peak) were used to evaluate rice leaf chlorophyll, LAI. Red edge positions move to longer wave bands till booting stage and move to short bands after booting stage. A high correlation was found between chlorophyll content of top leaves and the wavelength of the red edge position and between LAI and the red edge parameter. Then, the red edge was found valuable for assessment of rice above stand leaves chlorophyll contents. But a correlation was not found between chlorophyll b content of leaves or carotenoid or albumen-nitrogen or non-albumen-nitrogen and the wavelength of the red parameters. Some red edge parameters are one of the best remote sensing descriptors.
In this paper, we report some correlation analysis results between hyperspectral data in the spectral range of approximately 350nm-935nm and LAI of rice. Hyperspectral measurements were taken using an Analytical Spectral Devices (ASD) FieldSpec UV/VNIR Spectroradiometer at the experiment farm in 1999 and 2000.The potential of
hyperspectral data for estimating LAI was evaluated using univariate correlation method with different types of predictors: original and the first-order derivative spectra, vegetation index (VI) based, spectral position-based, area-based predictors. The 6 VIs were constructed from the green-peek and red-well spectra bands; spectral
position-based, predictors consisted of parameters extracted from the blue, yellow, and red edges, the green-peek and the red-well; area-based variables were calculated as the sum of the first derivative values at each of the three edges.
Results showed that for univariate correlation analysis, the better results were obtained for LAI. The best LAI was obtained with the area-based predictors in prediction models for LAI. In univariate correlation analysis, it seems that only wavelength at maximum value of 1st derivative within red edge (Wr), reflectance at green-peak and at red-well, and their VIs may be employed to predict LAI, and betterR<sup>2</sup> valued were obtained from the maximum first derivative spectra of blue edge (SDb) and red edge(SDr). In general, the results obtained from the accuracy assessment the best
predictors are area-based ones, the VIs of SDb and SDr.
Results from the correlation analysis showed that in the regions of the "three edges" for estimating LAI, sum of 1st derivative values within red edge was the most effective, sum of 1st derivative values within blue edge was the mere effective, sum of 1st derivative values within yellow edge was not effective.
The canopy spectra of rice under different nitrogen levels were studied. Some red edge parameters in the first derivative reflectance curve (wavelength, amplitude and area of the red edge peak) were used to evaluate rice leaf chlorophyll, LAI. Red edge positions move to longer wave bands till booting stage and move to short bands after booting stage. A high correlation was found between chlorophyll content of top leaves and the wavelength of the red edge position and between LAI and the red edge parameter. Then, the red edge was found valuable for assessment of carotenoid or albumen-nitrogen or non-albumen-nitrogen and the wavelength of the red parameters. Some red edge parameters are one of the best remote sensing descriptors.