Evaluating the Capability of Satellite Hyperspectral Imager, the ZY1–02D, for Topsoil Nitrogen Content Estimation and Mapping of Farmlands in Black Soil Area, China

Abstract

Soil nitrogen (N) content plays a vital role in agriculture and biogeochemical processes, ranging from the N fertilization management for intensive agricultural production to the patterns of N cycling in agroecological systems. While proximal sensing in laboratory settings can achieve ideal soil N estimation accuracy, the estimation and mapping by using remote sensing methods in a large spatial scale diplays low ability. A new hyperspectral imager with 166 spectral channels, the ZY1-02D, makes possible the detection of subtle but important spectral features of soil. This study aimed at exploring the capability of the ZY1-02D to estimate and map the topsoil N content of the black soil-covered farmlands in northeast China. To this aim, 646 soil samples from study sites were collected, processed, spectrally and geochemically measured for the soil N sensitive bands detection and partial least squares regression (PLSR) calibration and validation. The sensitive bands detection results showed an appealing regularity of the variability and stable tendency of the soil N sensitive spectral bands with the change of the sample size. Based on this, we compared the estimation capacity of the models developed with the full wavelength spectra and the models developed with the sensitive bands. The estimation based on ZY1-02D full wavelength spectral reflectance were robust, with R2 of 0.64 in validation. Further, the results of model developed with the sensitive bands showed better validation accuracy with R2 of 0.66 and were applied to create a map of topsoil N content of farmlands in the northeast China black soil area. The results demonstrated that sensitive bands modelling could enhance the accuracy of the estimation and simplify model, and what is more, showed the ideal capability of ZY1-02D for soil N content estimation at the regional scale.

Optimizing a Standard Spectral Measurement Protocol to Enhance the Quality of Soil Spectra: Exploration of Key Variables in Lab-Based VNIR-SWIR Spectral Measurement

“…Although the accuracies were lower than the R results, the models developed with A spectra also presented acceptable results. The validation accuracies of models based on R' and C spectra were relatively poor, which is consistent with previous studies reporting that, although the spectral transformation of the first derivative and the continuum removal of spectral reflectance could effectively enhance the calibration accuracies of statistical models [31][32][33][34][35][36][37], the characteristic spectral bands of certain specific soil attributes show obvious discontinuous and unstable variation on the whole VNIR-SWIR spectral region when the R' and C spectra are employed, which can distinctly influence validation accuracies [47]. Regarding the influence of soil temperature on the spectra variation and corresponding estimation ability, the results showed no distinct regularities between the estimation accuracy and the variation in soil temperature: In most cases, the benchmark models developed with soil spectra measured at a soil temperature of 20 • C showed the best estimation accuracies; however, the model TaR' (30 • C) obtained the highest R 2 v = 0.538 of the treatments in its group (Table 4 green zone).…”

The Weight of Hyperion and PRISMA Hyperspectral Sensor Characteristics on Image Capability to Retrieve Urban Surface Materials in the City of Venice

“…Despite the fact that research has been carried out for about 40 years in this field, there are many shortcomings yet to be overcome in all of the steps included in hyperspectral image pre-processing [ 49 , 72 , 73 ], processing [ 9 , 46 , 74 ], and validation [ 30 , 31 , 75 ]. However, not only the pre-processing, processing, and validation steps, but also sensor characteristics determine the capability of hyperspectral images.…”

“…With regard to the first approach, because spectral unmixing analysis is the most widely applied method to hyperspectral images (e.g., [ 32 , 33 , 34 ]), some authors proposed methods to solve the unmixing problem (e.g., pixel purity index [ 35 ], N-FINDR [ 36 ], interactive error analysis [ 37 ]), or to estimate the endmember fractional abundances (e.g., [ 38 ]), whereas other authors developed methods based on spatial analysis (e.g., Spectral Angle Mapping—SAM [ 39 ] and Spectral Information Divergence—SID [ 40 ]). With regard to the second approach, the results obtained from hyperspectral data were compared with those obtained from other hyperspectral data (e.g., Hyperion images were compared with CHRIS [ 41 ], Hyperspectral Satellite TianGong-1 [ 42 ], and PRISMA [ 43 ] hyperspectral data), from multispectral data (e.g., CASI and MIVIS hyperspectral images were compared with ATM multispectral data [ 44 ], and PRISMA hyperspectral images were compared with Sentinel-2A multispectral data [ 45 ]), and from other data (e.g., AHSI hyperspectral data were compared with the GlobalLand30 land cover data set [ 46 ]; MIVIS hyperspectral image was merged with DEM [ 47 ]). However, there are many sources of error as the capability evaluated from real image is due to both the characteristics of the sensor and each step of image pre-processing and processing (i.e., calibration [ 7 , 48 ]; atmospheric [ 49 , 50 ] and geometric [ 51 ] corrections; dimension reduction [ 30 ]; selected method [ 52 ]; etc.).…”

Hydromagnesite determination method based on Landsat8 and ZY1-02D data: A case study of the Jiezechaka Salt Lake in Tibet

“…The spectral data set for identifying the characteristic absorption bands of hydromagnesite is generated by preprocessing the laboratory spectra [42] . The raw spectra collected by the spectrometer were reflectance transformed and then converted into the txt file by ENVI software.The spectral data is processed by Savitzky-Golay (SG) filtering and smoothing using python language.…”