No abstract

Monitoring urban expansion and greenspace change is an urgent need for planning and decision-making. This paper presents a methodology integrating Principal Component Analysis (PCA) and hybrid classifier to undertake this kind of work using a sequence of multi-sensor SPOT images (SPOT-2,3,5) and Sentinel-2A data from 1996 to 2016 in Hangzhou City, which is the central metropolis of the Yangtze River Delta in China. In this study, orthorectification was first applied on the SPOT and Sentinel-2A images to guarantee precise geometric correction which outperformed the conventional polynomial transformation method. After pre-processing, PCA and hybrid classifier were used together to enhance and extract change information. Accuracy assessment combining stratified random and user-defined plots sampling strategies was performed with 930 reference points. The results indicate reasonable high accuracies for four periods. It was further revealed that the proposed method yielded higher accuracy than that of the traditional post-classification comparison approach. On the whole, the developed methodology provides the effectiveness of monitoring urban expansion and green space change in this study, despite the existence of obvious confusions that resulted from compound factors.

Non-destructive nutrition diagnosis provides effective technological support for agricultural sustainability. According to the plant nutrition mechanism, leaf characteristics displays different changing trends under nitrogen (N), phosphorus (P), and potassium (K) nutrition stress. In this study, the dynamic capture of rice leaf by scanning was used to research the changing regulation of leaf characteristics under nutrition stress. The leaf characteristics were extracted by mean value and regionprops functions in MATLAB, and the leaf dynamics were quantified by calculating the relative growth rate. Stepwise discriminant analysis and leave one out cross validation were applied to identify NPK deficiencies. The results indicated that leaves with N deficiency presented the lowest extension rate and the fastest wilt rate, followed by P and K deficiencies. During the identification, both morphological and color indices of the first incomplete leaf were effective indices for identification, but for the third fully expanded leaf, they were mainly color indices. Moreover, the first incomplete leaf had comparative advantage in early diagnosis (training accuracy 73.7%, validation accuracy 71.4% at the 26th day after transplantation), and the third fully expanded leaf generated higher accuracy at later stage. Overall, dynamic analysis expanded the application of leaf characteristics in identification, which contributes to improving the diagnostic effect.

Soil moisture is a key variable in ecology, environment, agriculture, and hydrology. The Soil Moisture Active Passive (SMAP) satellite provides global soil moisture products with reliable accuracy since 2015. However, significant gaps of SMAP soil moisture appeared over Tibetan Plateau. Considering the important role of the Tibetan Plateau in global climate and environment, it is essential to develop methods to infill the gaps to generate seamless SMAP soil moisture data. To address this issue, we proposed two methods, machine learning and geostatistics technique. For the machine learning technique, we train a Random Forest algorithm which aims to match the output of available SMAP L3 soil moisture using a series of input variables such as SMAP brightness temperature (TBH, TBV) in ascending orbits (6:00 PM local time), surface temperature, MODIS NDVI, land cover, DEM and other auxiliary data. Then, the established RF estimators were applied to the SMAP brightness temperature from descending orbits (6:00 AM local time) to reconstruct complete soil moisture data over the Tibetan Plateau. For the geostatistics technique, the Ordinary Kriging was applied to the available SMAP L3 soil moisture pixels to interpolate complete soil moisture data. To cross-validate the performances of the algorithms, we assume certain areas with available SMAP SM values as missing, and then compared the gap-filling results with the actual ones. The cross-validations show that the gap-filling results from two algorithms were highly correlated to the official SMAP SM products with high coefficients of determination (R 2 RF = 0.97, R 2 OK = 0.85) and low RMSE (RMSERF = 0.015 cm 3 /cm 3 , RMSEOK = 0.036 cm 3 /cm 3 ). Furthermore, the gap-filling soil moisture data present a better correlation with the SMOS soil moisture data (R = 0.55 ~ 0.7) than the GLDAS simulations (R = 0.18 ~ 0.62). The reconstructed soil moisture from RF (R = 0.71) and OK (R = 0.55) algorithms are well related to the Maqu network measurements. Thus, the machine learning and geostatistics algorithms have the potential to reproduce the missing SMAP soil moisture products over the Tibetan Plateau.