Shadows exist universally in sunlight-source remotely sensed images, and can interfere with the spectral morphological features of green vegetations, resulting in imprecise mathematical algorithms for vegetation monitoring and physiological diagnoses; therefore, research on shadows resulting from forest canopy internal composition is very important. Red edge is an ideal indicator for green vegetation’s photosynthesis and biomass because of its strong connection with physicochemical parameters. In this study, red edge parameters (curve slope and reflectance) and the normalized difference vegetation index (NDVI) of two species of coniferous trees in Inner Mongolia, China, were studied using an unmanned aerial vehicle’s hyperspectral visible-to-near-infrared images. Positive correlations between vegetation red edge slope and reflectance with different illuminated/shaded canopy proportions were obtained, with all R²s beyond 0.850 (p < 0.01). NDVI values performed steadily under changes of canopy shadow proportions. Therefore, we devised a new vegetation index named normalized difference canopy shadow index (NDCSI) using red edge’s reflectance and the NDVI. Positive correlations (R² = 0.886, p < 0.01) between measured brightness values and NDCSI of validation samples indicated that NDCSI could differentiate illumination/shadow circumstances of a vegetation canopy quantitatively. Combined with the bare soil index (BSI), NDCSI was applied for linear spectral mixture analysis (LSMA) using Sentinel-2 multispectral imaging. Positive correlations (R² = 0.827, p < 0.01) between measured brightness values and fractional illuminated vegetation cover (FIVC) demonstrate the capacity of NDCSI to accurately calculate the fractional cover of illuminated/shaded vegetation, which can be utilized to calculate and extract the illuminated vegetation canopy from satellite images.

Timely and accurately estimates of crop biomass and grain yield estimation are crucial for agricultural management. Optical remote sensing techniques can provide crop parameters (e.g., biomass, fractional vegetation cover (FVC)) at regional and larger scales. However, such techniques saturate at high crop canopy cover and cannot detect biomass stored in reproductive organs. The AquaCrop model can be used to estimate FVC, biomass, and grain yield output based on crop growth environmental parameters (e.g., temperature, rainfall, irrigation). In this work, we developed a method for estimating and mapping crop biomass and grain yield using unmanned aerial vehicle (UAV) remote sensing images and AquaCrop. The combination of low-cost UAV remote sensing data and AquaCrop can be used to map wheat biomass and grain yield before harvest. This work investigated whether biomass and grain yield can be predicted using measurements of biomass and FVC in several winter-wheat fields at an early growing stage using ground-based and remote sensing-based methods. FVC, biomass, and grain yield were measured at several key growing stages (jointing (S1), heading (S2), flowering (S3), grain filling (S4)) and harvest (H) during 2014–2015. We specifically evaluated the performances of using field- and UAV-based FVC and biomass measurements from different growing-stage combinations (e.g., S1–S4, S1–S3, and S1–S2) to calibrate the AquaCrop model and estimate biomass and grain yield. The results indicate that winter-wheat biomass and grain yield can be estimated by calibrating AquaCrop using (i) biomass and (ii) biomass and FVC. The results also reveal that the biomass and grain yield estimated using FVC and AquaCrop have poor accuracy compared with the biomass and grain yield estimated using (i) only biomass and (ii) biomass and FVC. The results suggest that the combined use of UAV remote sensing and AquaCrop can be used to obtain maps of biomass and biomass yield. When estimating winter-wheat biomass and grain yield one month (13 May) before harvest (11 June), the predicted biomass and grain yield agreed with the measured values (biomass: n = 96, coefficient of determination (R²) = 0.61, mean absolute error (MAE) = 1.69 t ha^–1, root-mean-square error (RMSE) = 2.10 t ha^–1, normalized RMSE (nRMSE) = 18.5%; grain yield: n = 48, R² = 0.63, MAE = 0.96 t ha^–1, RMSE = 1.16 t ha^–1, nRMSE = 21.9%). When estimating winter-wheat biomass and grain yield one and a half months (26 April) before harvest (11 June), the predicted biomass and grain yield was lower than when estimating winter-wheat biomass and grain yield one month before harvest (biomass: n = 144, R² = 0.55, MAE = 2.52 t ha^–1, RMSE = 3.34 t ha^–1, nRMSE = 23.6%; grain yield: n = 48, R² = 0.34, MAE = 1.68 t ha^–1, RMSE = 2.17 t ha^–1, nRMSE = 35.7%).

Remote sensing image classification is a common application of remote sensing images. In order to improve the performance of Remote sensing image classification, multiple classifier combinations are used to classify the Landsat-8 Operational Land Imager (Landsat-8 OLI) images. Some techniques and classifier combination algorithms are investigated. The classifier ensemble consisting of five member classifiers is constructed. The results of every member classifier are evaluated. The voting strategy is experimented to combine the classification results of the member classifier. The results show that all the classifiers have different performances and the multiple classifier combination provides better performance than a single classifier, and achieves higher overall accuracy of classification. The experiment shows that the multiple classifier combination using producer’s accuracy as voting-weight (MCC_mod2 and MCC_mod3) present higher classification accuracy than the algorithm using overall accuracy as voting-weight (MCC_mod1).And the multiple classifier combinations using different voting-weights affected the classification result in different land-cover types. The multiple classifier combination algorithm presented in this article using voting-weight based on the accuracy of multiple classifier may have stability problems, which need to be addressed in future studies.

The crop above-ground biomass (AGB) is critically important for monitoring crop growth, and its accurate estimation can be used by agricultural managers to improve farmland management and to predict crop grain yield. Many studies have shown that models for the estimation of AGB for multiple crop growth stages based on optical remote sensing spectral indices (SIs) often underestimate the crop AGB in later growth stages due to saturation problems. The purpose of this study was to estimate winter-wheat AGB using (i) high-frequency information obtained from the image wavelet decomposition (IWD) of unmanned aerial vehicle (UAV)-based digital images of winter-wheat canopy (i.e. high-frequency IWD information), and (ii) variables obtained from the continuous wavelet transform (CWT) of hyperspectral images of winter-wheat canopy (i.e. hyperspectral CWT variables). Digital and hyperspectral images were acquired using a digital camera and an imaging spectrometer, both mounted on a UAV. Unlike optical SIs, high-frequency IWD information and hyperspectral CWT variables may not be limited by saturation problems for high winter-wheat canopy cover. Our results indicate that high-frequency IWD information and hyperspectral CWT variables both increase (decrease) with increasing winter-wheat AGB. A multiple linear stepwise regression technique was used to analyse the performance of (1) SIs, (2) CWT, (3) IWD, (4) SIs + CWT, (5) SIs + IWD, (6) CWT + IWD, and (7) SIs + CWT + IWD, for estimating AGB. Our results indicate that: (i) the method based only on optical SIs may not support the estimation of winter-wheat AGB for multiple growth stages (SIs: coefficient of determination (R²) = 0.62, mean absolute error (MAE) = 1.30 t ha^–1, root-mean-square error (RMSE) = 1.63 t ha^–1); (ii) the combined use of the IWD of digital images of winter-wheat canopy and the CWT of hyperspectral images of crop canopy can support the estimation of winter-wheat AGB for multiple growth stages (CWT + IWD: R² = 0.85, MAE = 0.79 t ha^–1, RMSE = 1.01 t ha^–1), including later growth stages; and (iii) the combined use of SIs and the IWD of digital images of winter-wheat canopy can improve the estimation accuracy of winter-wheat AGB (SIs + IWD: R² = 0.80, MAE = 0.93 t ha^–1, RMSE = 1.22 t ha^–1), which may indicate that imaging spectrometers and cheap digital cameras have distinct advantages and can both be used to obtain AGB estimates using different methods. This work provides a new perspective on the use of high-frequency IWD information, hyperspectral CWT variables, and their combination to estimate AGB for multiple crop growth stages.