Comparison of CWSI and T_(s)-T_(a)-VIs in moisture monitoring of dryland crops(sorghum and maize)based on UAV remote sensing

Monitoring agricultural drought using remote sensing data is crucial for precision irrigation in modern agriculture.Utilizing unmanned aerial vehicle(UAV)remote sensing,we explored the applicability of an empirical crop water stress index(CWSI)based on canopy temperature and three-dimensional drought indices(TDDI)constructed from surface temperature(T_(s)),air temperature(T_(a))and five vegetation indices(VIs)for monitoring the moisture status of dryland crops.Three machine learning algorithms(random forest regression(RFR),support vector regression,and partial least squares regression)were used to compare the performance of the drought indices for vegetation moisture content(VMC)estimation in sorghum and maize.The main results of the study were as follows:(1)Comparative analysis of the drought indices revealed that T_(s)-T_(a)-normalized difference vegetation index(TDDIn)and T_(s)-T_(a)-enhanced vegetation index(TDDIe)were more strongly correlated with VMC compared with the other indices.The indices exhibited varying sensitivities to VMC under different irrigation regimes;the strongest correlation observed was for the TDDIe index with maize under the fully irrigated treatment(r=-0.93).(2)Regarding spatial and temporal characteristics,the TDDIn,TDDIe and CWSI indices showed minimal differences Over the experimental period,with coefficients of variation were 0.25,0.18 and 0.24,respectively.All three indices were capable of effectively characterizing the moisture distribution in dryland maize and sorghum crops,but the TDDI indices more accurately monitored the spatial distribution of crop moisture after a rainfall or irrigation event.(3)For prediction of the moisture content of single crops,RFR models based on TDDIn and TDDIe estimated VMC most accurately(R^(2)>0.7),and the TDDIn-based model predicted VMC with the highest accuracy when considering multiple-crop samples,with R^(2)and RMSE of 0.62 and 14.26%,respectively.Thus,TDDI proved more effective than the CWSI in estimating crop water content.

Automated Soil Moisture Monitoring Wireless Sensor Network for Long-Term Cal/Val Applications

The design and development of a wireless sensor network for soil moisture measurement in an unlevelled 10 km × 10 km area, is described. It was specifically deployed for the characterization of a reference area, in campaigns of calibration and validation of the space mission SMOS (Soil Moisture and Ocean Salinity), but the system is easily extensible to monitor other climatic or environmental variables, as well as to other regions of ecological interest. The network consists of a number of automatic measurement stations, strategically placed following soil homogeneity and land uses criteria. Every station includes acquisition, conditioning and communication systems. The electronics are battery operated with the help of solar cells, in order to have a total autonomous system. The collected data is then transmitted through long radio links, with ling ranges above 8 km. A standard PC linked to internet is finally used in order to control the whole network, to store the data, and to allow the remote access to the real-time data.

Application of multi-criteria decision-making methods to identification of soil moisture monitoring sites in an urban catchment in South Australia

When choosing sites for monitoring of soil moisture for hydrological purposes,a suitable process that considers the factors influencing soil moisture level should be followed.In this study,two multi-criteria decision-making(MCDM)methods,the multi-influencing factor(MIF)method and the analytical hierarchy process(AHP)method,were used to identify the optimal soil moisture monitoring(SMM)sites in the Dry Creek Catchment in South Australia.The most representative areas for nine SMM sites were obtained using the MIF method,considering the factors of rainfall,soil type,land use,catchment slope,elevation,and upslope accumulated area(UAA).The AHP method was used to select the optimal sites using the site-specific criteria.30.3%of the catchment area in the Australian Water Resources Assessment Landscape(AWRA-L)Grid_DC2 can be considered acceptable as representative area with the MIF method.Four potential sites were evaluated for each AWRA-L grid using the relative weights of the site-specific criteria with the AHP method.The Grid_DC2 required two sites that had the highest overall weight chosen with the AHP analysis.The procedure was repeated for the remaining four AWRA-L grids within the study area to select the required SMM sites.

Monitoring model for predicting maize grain moisture at the filling stage using NIRS and a small sample size

The change in the maize moisture content during different growth stages is an important indicator to evaluate the growth status of maize.In particular,the moisture content during the grain-filling stage reflects the grain quality and maturity and it can also be used as an important indicator for breeding and seed selection.At present,the drying method is usually used to calculate the moisture content and the dehydration rate at the grain-filling stage,however,it requires large sample size and long test time.In order to monitor the change in the moisture content at the maize grain-filling stage using small sample set,the Bootstrap re-sampling strategy-sample set partitioning based on joint x-y distances-partial least squares(Bootstrap-SPXY-PLS)moisture content monitoring model and near-infrared spectroscopy for small sample sizes of 10,20,and 50 were used.To improve the prediction accuracy of the model,the optimal number of factors of the model was determined and the comprehensive evaluation thresholds RVP(coefficient of determination(R^(2)),the root mean square error of cross-validation(RMSECV)and the root mean square error of prediction(RMSEP))was proposed for sub-model screening.The model exhibited a good performance for predicting the moisture content of the maize grain at the filling stage for small sample set.For the sample sizes of 20 and 50,the R^(2) values were greater than 0.99.The average deviations of the predicted and reference values of the model were 0.1078%,0.057%,and 0.0918%,respectively.Therefore,the model was effective for monitoring the moisture content at the grain-filling stage for a small sample size.The method is also suitable for the quantitative analysis of different concentrations using near-infrared spectroscopy and small sample size.