ABSTRACT
Accurate crop classification is the basis of agricultural research, and remote sensing is the only effective measuring technique to classify crops over large areas. Optical remote sensing is effective in regions with good illumination; however, it usually fails to meet requirements for highly accurate crop classification in cloud-covered areas and rainy regions. Synthetic aperture radar (SAR) can achieve active data acquisition by transmitting signals; thus, it has strong resistance to cloud and rain interference. In this study, we designed an improved crop planting structure mapping framework for cloudy and rainy regions by combining optical data and SAR data, and we revealed the synchronous-response relationship of these two data types. First, we extracted geo-parcels from optical images with high spatial resolution. Second, we built a recurrent neural network (RNN)-based classifier suitable for remote sensing images on the geo-parcel scale. Third, we classified crops based on the two datasets and established the network. Fourth, we analyzed the synchronous response relationships of crops based on the results of the two classification schemes. This work is the basis for the application of remote sensing data for the fine mapping and growth monitoring of crop planting structures in cloudy and rainy areas in the future.
ABSTRACT
Radiometric normalization attempts to normalize the radiomimetic distortion caused by non-land surface-related factors, for example, different atmospheric conditions at image acquisition time and sensor factors, and to improve the radiometric consistency between remote sensing images. Using a remote sensing image and a reference image as a pair is a traditional method of performing radiometric normalization. However, when applied to the radiometric normalization of long time-series of images, this method has two deficiencies: first, different pseudo-invariant features (PIFs)-radiometric characteristics of which do not change with time-are extracted in different pairs of images; and second, when processing an image based on a reference, we can minimize the residual between them, but the residual between temporally adjacent images may induce steep increases and decreases, which may conceal the information contained in the time-series indicators, such as vegetative index. To overcome these two problems, we propose an optimization strategy for radiometric normalization of long time-series of remote sensing images. First, the time-series gray-scale values for a pixel in the near-infrared band are sorted in ascending order and segmented into different parts. Second, the outliers and inliers of the time-series observation are determined using a modified Inflexion Based Cloud Detection (IBCD) method. Third, the variation amplitudes of the PIFs are smaller than for vegetation but larger than for water, and accordingly the PIFs are identified. Last, a novel optimization strategy aimed at minimizing the correction residual between the image to be processed and the images processed previously is adopted to determine the radiometric normalization sequence. Time-series images from the Thematic Mapper onboard Landsat 5 for Hangzhou City are selected for the experiments, and the results suggest that our method can effectively eliminate the radiometric distortion and preserve the variation of vegetation in the time-series of images. Smoother time-series profiles of gray-scale values and uniform root mean square error distributions can be obtained compared with those of the traditional method, which indicates that our method can obtain better radiometric consistency and normalization performance.
ABSTRACT
In this study, the synthesis and characterization of sepiolite-supported nanoscale zero-valent iron particles (S-NZVI) was investigated for the adsorption/reduction of Cr(VI) and Pb(II) ions. Nanoscale zero-valent iron (NZVI) supported on sepiolite was successfully used to remove Cr(VI) and Pb(II) from groundwater with high efficiency. The removal mechanism was proposed as a two-step interaction including both the physical adsorption of Cr(VI) and Pb(II) on the surface or inner layers of the sepiolite-supported NZVI particles and the subsequent reduction of Cr(VI) to Cr(III) and Pb(II) to Pb(0) by NZVI. The immobilization of the NZVI particles on the surface of sepiolite could help to overcome the disadvantage of NZVI particles, which have strong tendency to agglomerate into larger particles, resulting in an adverse effect on both the effective surface area and reaction performance. The techniques of XRD, XPS, BET, Zeta potential, and TEM were used to characterize the S-NZVI and interaction between S-NZVI and heavy metals. The appropriate S-NZVI dosage was 1.6 g L(-1). The removal efficiency of Cr(VI) and Pb(II) by S-NZVI was not affected to any considerable extent by the presence of co-existing ions, such as H2PO4(-), SiO3(2-), Ca(2+) and HCO3(-). The Cr(VI) and Pb(II) removal kinetics followed a pseudo-first-order rate expression, and both Langmuir isotherm model and Freundlich isotherm model were proposed. The results suggested that supporting NZVI on sepiolite had the potential to become a promising technique for in situ heavy metal-contaminated groundwater remediation.
