The suitability evaluation of agricultural land at the regional scale is of great significance for protecting land and water resources and building sustainable agricultural systems. Based on climate, soil, topographical, and surface water resources, land suitability index (LSI) data for maize, rice, and soybeans are established using an analytical hierarchy process and matter element analysis (AHP–MEA) model in Jilin Province, China. The results show that there is a significant positive linear correlation between the LSI and the measured yield, which indicates that the model has an ideal effect and certain reference and extension significance. The main limiting factors for maize and soybean planting are pH, total nitrogen (TN), available phosphorus (AP), and soil texture, while water shortage limits rice planting. Different spatial structure optimization schemes for planting are established using the LSI and measured yield, along with economic indices. This study shows that the scheme that integrates policy and cost can make full use of land and water resources and promote the economic growth of agriculture. After optimization, the planting areas of maize, rice, and soybeans were 7.22, 2.44, and 0.71 million ha, respectively, representing an increase of 15.71 billion yuan over the agricultural GDP for the existing planting structure. It is expected that this study will provide a basis for follow-up studies on crop cultivation suitability.

With the development of industrialization and urbanization, heavy metal contamination in agricultural soils tends to accumulate rapidly and harm human health. Visible and near-infrared (Vis-NIR) spectroscopy provides the feasibility of fast monitoring of the variation of heavy metals. This study explored the potential of fractional-order derivative (FOD), the optimal band combination algorithm and different mathematical models in estimating soil heavy metals with Vis-NIR spectroscopy. A total of 80 soil samples were collected from an agriculture area in Suzi river basin, Liaoning Province, China. The spectra for mercury (Hg), chromium (Cr), and copper (Cu) of the samples were obtained in the laboratory. For spectral preprocessing, FODs were allowed to vary from 0 to 2 with an increment of 0.2 at each step, and the optimal band combination algorithm was applied to the spectra after FOD. Then, four mathematical models, namely, partial least squares regression (PLSR), adaptive neural fuzzy inference system (ANFIS), random forest (RF) and generalized regression neural network (GRNN), were used to estimate the concentration of Hg, Cr and Cu. Results showed that high-order FOD had an excellent effect in highlighting hidden information and separating minor absorbing peaks, and the optimal band combination algorithm could remove the influence of spectral noise caused by high-order FOD. The incorporation of the optimal band combination algorithm and FOD is able to further mine spectral information. Furthermore, GRNN made an obvious improvement to the estimation accuracy of all studied heavy metals compared to ANFIS, PLSR, and RF. In summary, our results provided more feasibility for the rapid estimation of Hg, Cr, Cu and other heavy metal pollution areas in agricultural soils.

Black soil in northeast China is gradually degraded and soil organic matter (SOM) content decreases at a rate of 0.5% per year because of the long-term cultivation. SOM content can be obtained rapidly by visible and near-infrared (Vis–NIR) spectroscopy. It is critical to select appropriate preprocessing techniques for SOM content estimation through Vis–NIR spectroscopy. This study explored three categories of preprocessing techniques to improve the accuracy of SOM content estimation in black soil area, and a total of 496 ground samples were collected from the typical black soil area at 0–15 cm in Hai Lun City, Heilongjiang Province, northeast of China. Three categories of preprocessing include denoising, data transformation and dimensionality reduction. For denoising, Svitzky-Golay filter (SGF), wavelet packet transform (WPT), multiplicative scatter correction (MSC), and none (N) were applied to spectrum of ground samples. For data transformation, fractional derivatives were allowed to vary from 0 to 2 with an increment of 0.2 at each step. For dimensionality reduction, multidimensional scaling (MDS) and locally linear embedding (LLE) were introduced and compared with principal component analysis (PCA), which was commonly used for dimensionality reduction of soil spectrum. After spectral pretreatments, a total of 132 partial least squares regression (PLSR) models were constructed for SOM content estimation. Results showed that SGF performed better than the other three denoising methods. Low-order derivatives can accentuate spectral features of soil for SOM content estimation; as the order increases from 0.8, the spectrum were more susceptible to spectral noise interferences. In most cases, 0.2–0.8 order derivatives exhibited the best estimation performance. Furthermore, PCA yielded the optimal predictability, the mean residual predictive deviation (RPD) and maximum RPD of the models using PCA were 1.79 and 2.60, respectively. The application of appropriate preprocessing techniques could improve the efficiency and accuracy of SOM content estimation, which is important for the protection of ecological and agricultural environment in black soil area.

For agricultural production and food safety, it is important to accurately and extensively estimate the heavy metal(loid) pollution contents in farmland soil. Remote sensing technology provides a feasible method for the rapid determination of heavy metal(loid) contents. In this study, the contents of Ni, Hg, Cr, Cu, and As in the agricultural soil of the Suzi River Basin in Liaoning Province were taken as an example. The spectral data, with Savitzky–Golay smoothing, were taken as the original spectra (OR), and the spectral transformation was achieved by continuum removal (CR), reciprocal (1/R), root means square ($\sqrt{\mathrm{R}}$), first-order differential (FDR), and second-order differential (SDR) methods. Then the spectral indices were calculated by the optimal band combination algorithm. The correlation between Ni, Hg, Cr, Cu, and As contents and spectral indices was analyzed, and the optimal spectral indices were selected. Then, multiple linear regression (MLR), partial least squares regression (PLSR), random forest regression (RFR), and adaptive neuro-fuzzy reasoning system (ANFIS) were used to establish the estimation model based on the combined optimal spectral indices method. The results show that the combined optimal spectral indices method improves the correlation between spectra and heavy metal(loid), the MLR model produces the best estimation effect for Ni and Cu (${\mathrm{R}}^2=0.713 \mathrm{and} 0.855$, RMSE = 5.053 and 8.113, RPD = 1.908 and 2.688, respectively), and the PLSR model produces the best effect for Hg, Cr, and As (${\mathrm{R}}^2=$ 0.653, 0.603, and 0.775, RMSE = 0.074, 23.777, and 1.923, RPD = 1.733, 1.621, and 2.154, respectively). Therefore, the combined optimal spectral indices method is feasible for heavy metal(loid) estimation in soils and could provide technical support for large-scale soil heavy metal(loid) content estimation and pollution assessment.

Satellite-based drought indices have been shown to be effective and convenient in detecting drought conditions. The temperature vegetation dryness index (TVDI) is one of the most frequently used drought indices; however, it is not suitable for areas with high fractional vegetation cover (FVC). In this study, a modified temperature vegetation dryness index (mTVDI) was constructed by using the multispectral vegetation dryness index (MVDI) proposed by a PROSAIL simulation and water stress experiments which was based on the theory of the TVDI and utilized MODIS data. Compared with the TVDI, the mTVDI presents a more triangular feature space, as well as obviously increased R² values for dry and wet edges (from 0.37–0.90 to 0.53–0.91 for dry edges and from 0.00–0.77 to 0.24–0.80 for wet edges). The mTVDI was evaluated using standardized precipitation evapotranspiration indices (SPEIs), precipitation, potential evapotranspiration (PET), and the crop water deficit index (CWDI), and the results confirmed that the mTVDI can better reflect the actual spatial changes, compared to the TVDI, under high FVC, as well as presenting an increased Pearson correlation coefficient (by 0.06–0.10) when compared with SPEIs. Moreover, the good performance of the mTVDI in major drought events indicates its reliability and accuracy for drought monitoring. Overall, the mTVDI is a reliable and accurate satellite-based dryness index suitable for high FVC conditions.

In this paper, aiming to solve the problem of vital information security as well as neural network application in optical encryption system, we propose an optical image encryption method by using the Hopfield neural network. The algorithm uses a fuzzy single neuronal dynamic system and a chaotic Hopfield neural network for chaotic sequence generation and then obtains chaotic random phase masks. Initially, the original images are decomposed into sub-signals through wavelet packet transform, and the sub-signals are divided into two layers by adaptive classification after scrambling. The double random-phase encoding in 4f system and Fresnel domain is implemented on two layers, respectively. The sub-signals are performed with different conversions according to their standard deviation to assure that the local information’s security is guaranteed. Meanwhile, the parameters such as wavelength and diffraction distance are considered as additional keys, which can enhance the overall security. Then, inverse wavelet packet transform is applied to reconstruct the image, and a second scrambling is implemented. In order to handle and manage the parameters used in the scheme, the public key cryptosystem is applied. Finally, experiments and security analysis are presented to demonstrate the feasibility and robustness of the proposed scheme.