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 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 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.
Correlation analyses between the spectral reflectance and the pigment contents of five crop leaves including rice, cotton, color cotton, corn and sugarcane were reported in this paper. Spectral reflectance over the 350-2500 nm range with a spectral resolution of 3 nm and the content of chlorophyll <i>a</i>, <i>b</i>, <i>a</i>+<i>b</i>, and total carotenoids were determined for leaves from five crops covering a wide range of chlorophyll <i>a</i> content (0.2440 -3.8755mg/g). Maximum sensitivity of reflectance to variation in pigment content was found in the green wavelength region at 550 nm and at 707 nm. The reflectance in the main the pigment absorption regions in the blue (400-500 nm) and red (660-690 nm) wavelengths proved to be insensitive to variation in pigment content. The ratio <i>R</i>670/(<i>R</i>550*<i>R</i>707) correlated best with chlorophyll <i>a</i>, <i>a</i>+<i>b</i>, and carotenoids contents. The ratio <i>R</i>670/<i>R</i>550 correlated best with chlorophyll <i>b</i> content.
Comparison of extracting areas of paddy fields of Zhejiang Province in 2002 using Moderate-Resolution Imaging Spectroradiometer (MODIS), Advance Very High Resolution Radiometer (AVHRR), geographic information system (GIS) and global position system (GPS) was reported in this paper. Training samples are selected and located with the help of GPS to provide maximal accuracy. A concept of assessing areas of potential cultivation of rice is suggested by means of GIS integration. MODIS data of September 1st, 2002 and NOAA/AVHRR data of September 2st, 2002 covering the whole of Zhejiang Province were acquired. By integration of Remote Sensing (RS), GIS and GPS technologies the actual areas of rice fields in 2002 have been mapped. The classification accuracy was 95.1% percent for MODIS and 92.7 percent for NOAA compared with the statistical data of the agricultural bureau of Zhejiang Province.
The hyperspectral reflectances of the canopy, the sword leaf, the third unfolding leaf from the top and ear of the main stem of two varieties of rice are measured by a ASD FieldSpec Pro FR in field and indoor under 3 nitrogen support levels in mature process. The concentrations of chlorophyll and carotenoid of leaves and ears corresponding to the spectra were determined by biochemical method. The spectral differences are significant for the canopy and leaves of rice under differet nitrogen support level, and the concentrations of chlorophyll and carotenoid of leaves increase with the increasing of nitrogen applying. There exist significant differences for the pigment concentrations of the leaves of rice under different nitrogen levels. The spectral reflectances of the canopy are gradually getting bigger in the visible region and smaller in the near infrared region as the growth stage goes on. 'Blue shift' phenomena for the spectra red edge position of the canopy, leaves and ears were proved. The concentrations of chlorophyll and carotenoid of leaves and ears are very significantly correlative to the spectral vegetation indices VI1(= R<sub>990</sub>/R<sub>553</sub>), VI2(=R<sub>1200</sub>/R<sub>553</sub>), VI3(=R<sub>750</sub>/R<sub>553</sub>), VI4(=R<sub>670</sub>/R<sub>440</sub>), VI5(= R<sub>553</sub>/R<sub>670</sub>), PRVI(=R<sub>800</sub>/R<sub>553</sub>), PSSRa, PSNDa and λ<sub>red</sub> (the red edge position). The results show that these VIs can be used to estimate the concentrations of chlorophyll and carotenoid of leaves and ears of rice.
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, the data of spectral observation and the grass yield of natural grassland in the north of XinJiang from 1991 to 1994 are used for analyzing the spectral vegetation index characteristics, studying the relationship between the ground spectrum and satellite remote sensing data and establishing the grass yield monitoring model and estimating model of natural grassland in the north of XinJiang. Based on the relation between the spectral vegetation indices and the satellite greenness value, satellite remote sensing monitoring model can be established. Then, the satellite remote sensing monitoring models were established.
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.
To improve our understanding of photon transporting inside leaves, and hence improve the accuracy of yield estimating and growth monitoring of rice by remotely sensed data, we simulated rice leaf reflectance by PROSPECT model. The experiment, which were referred to as the late rice experiment, were conducted at Zhejiang University in 1999 and 2000 with one species of rice (which is called Xiushui 63); In 1999 the rice was planted normally, but in 2000 it was fertilized in three different levels (low, medium and high). Leaf spectrum (reflectance and transmittance), biochemical concentration such as chlorophyll, protein, cellulose, lignin and water content, and leaf area were measured during the experiment. By the PROSPECT model, we simulated leaf reflectance on four days’ data set in 1999 and one day’s data set of three fertilizations in 2000. The correlation coefficients between actual and simulated values are more than 0.995, the <i>RMSE</i> values are less than 0.0212. On the other hand, the model has been inversed to estimate chlorophyll concentration. Compared with actual value, the comparative errors are less than 10%.
Some correlation analysis between hyperspectral data and chlorophyll-a, chlorophyll-b and carotenoid contents of leaves in different sites of rice were reported in this paper. Hyperspectral data of late rice in whole growing stage between 1999 and 2000 have been measured by using the ASD FieldSpec UV/VNIR (350-1050nm) Spectroradiometer with resolution of 3nm. The pigment contents of rice leaves, including chlorophyll-a, chlorophyll-b and carotenoid content in different nitrogen levels, have been measured. There are strong correlation among the pigments. The correlation coefficient between chlorophyll and carotenoid reaches the extremely significance level. The chlorophyll-a content of upper leaf was well correlated with the spectral variables. The potential of hyperspectral data for estimating chlorophyll-a of upper leaves was evaluated using univariate correlation and multivariate regression analysis methods with different types of predictors. Results show that the best prediction of chlorophyll-a of upper leaves was obtained with some hyperspectral variables such as SDr, SDb and their integration.
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.