Estimation of Crop Biomass Using GF-3 Polarization SAR Data Based on Genetic Algorithm Feature Selection

In recent years,Polarization SAR(PolSAR)has been widely used in the filed of crop biomass estimation.However,high dimensional features extracted from PolSAR data will lead to information redundancy which will result in low accuracy and poor transfer ability of the estimation model.Aiming at this problem,we proposed a estimation method of crop biomass based on automatic feature selection method using genetic algorithm(GA).Firstly,the backscattering coefficient,the polarization parameters and texture features were extracted from PolSAR data.Then,these features were automatically pre-selected by GA to obtain the optimal feature subset.Finally,based on this subset,a support vector regression machine(SVR)model was applied to estimate crop biomass.The proposed method was validated using the GaoFen-3(GF-3)QPSΙ(C-band,quad-polarization)SAR data.Based on wheat and rape biomass samples acquired from a synchronous field measurement campaign,the proposed method achieve relative high validation accuracy(over 80%)in both crop types.For further analyzing the improvement of proposed method,validation accuracies of biomass estimation models based on several different feature selection methods were compared.Compared with feature selection based on linear correlation,GA method has increased by 5.77%in wheat biomass estimation and 11.84%in rape biomass estimation.Compared with the method of recursive feature elimination(RFE)selection,the proposed method has improved crops biomass estimation accuracy by 3.90%and 5.21%,respectively.

Crop Yield Estimation with Farmers＇ Appraisal on Weather Condition

Crop yield is mainly affected by weather condition, inputs, and agriculture policies. In the crop yield estimation, farmers＇ perception on weather conditions lead to the assessment of how well yield would be compared to the previous seasons. This paper applies Bayesian estimation method to estimate crop yield with farmers＇ appraisal on weather condition. The paper shows that crop yield estimation with farmers＇ appraisal on weather condition takes into account risk proportionally to climate change. In light of the United Nations efforts aimed to build a consolidated agriculture statistical system across countries, the statistical model developed here should provide an important tool both for the crop yield estimation and food price analysis.

Smart Techniques for LULC Micro Class Classification Using Landsat8 Imagery

Wheat species play important role in the price of products and wheat production estimation.There are several mathematical models used for the estimation of the wheat crop but these models are implemented without considering the wheat species which is an important independent variable.The task of wheat species identification is challenging both for human experts as well as for computer vision-based solutions.With the use of satellite remote sensing,it is possible to identify and monitor wheat species on a large scale at any stage of the crop life cycle.In this work,nine popular wheat species are identified by using Landsat8 operational land imager(OLI)and thermal infrared sensor(TIRS)data.Two thousand samples of eachwheat crop species are acquired every fifteen days with a temporal resolution of ten multispectral bands(band two to band eleven).This study employs random forest(RF),artificial neural network,support vector machine,Naive Bayes,and logistic regression for nine types of wheat classification.In addition,deep neural networks are also developed.Experimental results indicate that RF shows the best performance of 91%accuracy while DNN obtains a 90.2%accuracy.Results suggest that remotely sensed data can be used in wheat type estimation and to improve the performance of the mathematical models.

Detect and attribute the extreme maize yield losses based on spatio-temporal deep learning

Providing accurate crop yield estimations at large spatial scales and understanding yield losses under extreme climate stress is an urgent challenge for sustaining global food security.While the data-driven deep learning approach has shown great capacity in predicting yield patterns,its capacity to detect and attribute the impacts of climatic extremes on yields remains unknown.In this study,we developed a deep neural network based multi-task learning framework to estimate variations of maize yield at the county level over the US Corn Belt from 2006 to 2018,with a special focus on the extreme yield loss in 2012.We found that our deep learning model hindcasted the yield variations with good accuracy for 2006-2018(R^(2)=0.81)and well reproduced the extreme yield anomalies in 2012(R^(2)=0.79).Further attribution analysis indicated that extreme heat stress was the major cause for yield loss,contributing to 72.5%of the yield loss,followed by anomalies of vapor pressure deficit(17.6%)and precipitation(10.8%).Our deep learning model was also able to estimate the accumulated impact of climatic factors on maize yield and identify that the silking phase was the most critical stage shaping the yield response to extreme climate stress in 2012.Our results provide a new framework of spatio-temporal deep learning to assess and attribute the crop yield response to climate variations in the data rich era.