Chlorophyll is an essential pigment for photosynthesis in crops, and leaf chlorophyll content can be used as an indicator for crop growth status and help guide nitrogen fertilizer applications. Estimating crop chlorophyll content plays an important role in precision agriculture. In this study, a variable, rate of change in reflectance between wavelengths ‘a’ and ‘b’ (RCRWa-b), derived from in situ hyperspectral remote sensing data combined with four advanced machine learning techniques, Gaussian process regression (GPR), random forest regression (RFR), support vector regression (SVR), and gradient boosting regression tree (GBRT), were used to estimate the chlorophyll content (measured by a portable soil–plant analysis development meter) of rice. The performances of the four machine learning models were assessed and compared using root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). The results revealed that four features of RCRWa-b, RCRW551.0–565.6, RCRW739.5–743.5, RCRW684.4–687.1 and RCRW667.9–672.0, were effective in estimating the chlorophyll content of rice, and the RFR model generated the highest prediction accuracy (training set: RMSE = 1.54, MAE =1.23 and R2 = 0.95; validation set: RMSE = 2.64, MAE = 1.99 and R2 = 0.80). The GPR model was found to have the strongest generalization (training set: RMSE = 2.83, MAE = 2.16 and R2 = 0.77; validation set: RMSE = 2.97, MAE = 2.30 and R2 = 0.76). We conclude that RCRWa-b is a useful variable to estimate chlorophyll content of rice, and RFR and GPR are powerful machine learning algorithms for estimating the chlorophyll content of rice.

Surface soil moisture (SSM) retrieval over agricultural fields using synthetic aperture radar (SAR) data is often obstructed by the vegetation effects on the backscattering during the growing season. This paper reports the retrieval of SSM from RADARSAT-2 SAR data that were acquired over wheat and soybean fields throughout the 2015 (April to October) growing season. The developed SSM retrieval algorithm includes a vegetation-effect correction. A method that can adequately represent the scattering behavior of vegetation-covered area was developed by defining the backscattering from vegetation and the underlying soil individually to remove the effect of vegetation on the total SAR backscattering. The Dubois model was employed to describe the backscattering from the underlying soil. A modified Water Cloud Model (MWCM) was used to remove the effect of backscattering that is caused by vegetation canopy. SSM was derived from an inversion scheme while using the dual co-polarizations (HH and VV) from the quad polarization RADARSAT-2 SAR data. Validation against ground measurements showed a high correlation between the measured and estimated SSM (R 2 = 0.71, RMSE = 4.43 vol.%, p < 0.01), which suggested an operational potential of RADARSAT-2 SAR data on SSM estimation over wheat and soybean fields during the growing season. of soil moisture data over a large region multiple times is very time-consuming and costly. Satellite remote sensing through optical or microwave sensors provides an effective and cost-efficient means of monitoring and assessing SSM at different scales [7-10]. For example, Sadeghi et al.[8] estimated SSM with a RMSE (root mean square error) less than 0.04 cm 3 /cm −3 with local calibration from Sentinel-2 and Landsat-8 observations for the Walnut Gulch and Little Washita watersheds. Tomer et al. [11] used the RADARSAT-2 images to calculate relative soil moisture. When comparing with the SMOS soil moisture, the results showed good temporal behavior with RMSE of approximately 0.05 m 3 /m 3 and a correlation coefficient of approximately 0.9. For passive microwave, the SMAP-radiometer-based soil moisture data product meets its expected performance of 0.04 m 3 /m 3 volumetric soil moisture (unbiased RMSE) [10]. Optical remote sensing is often hampered by cloud cover, while microwave remote sensing has all-weather day-and-night penetration capabilities [11]. However, the lower spatial resolutions of the passive microwave sensors in orbit limit their applicability to retrieve SSM over individual agricultural fields [12]. Currently, only active sensors, like radars, especially Synthetic Aperture Radars (SARs) (such as C-band Sentinel-1A and 1B, RADARSAT-2, and L-band ALOS-2), can provide observations at high spatial resolutions of about 10 m to 100 m with a relatively coarse temporal resolution (2-4 weeks) [2,12].There is a high correlation between radar backscattering coefficient and SSM since the dielectric constant of soil is directly proportional to the amount of water held in the soil [13]. Therefore, the...

Rice false smut (RFS), caused by Ustilaginoidea virens, is a significant grain disease in rice that can lead to reduced yield and quality. In order to obtain spatiotemporal change information, multitemporal hyperspectral UAV data were used in this study to determine the sensitive wavebands for RFS identification, 665–685 and 705–880 nm. Then, two methods were used for the extraction of rice false smut-infected areas, one based on spectral similarity analysis and one based on spectral and temporal characteristics. The final overall accuracy of the two methods was 74.23 and 85.19%, respectively, showing that the second method had better prediction accuracy. In addition, the classification results of the two methods show that the areas of rice false smut infection had an expanding trend over time, which is consistent with the natural development law of rice false smut, and also shows the scientific nature of the two methods.

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