Retrieval of leaf biochemical properties by inversed PROSPECT model and hyperspectral indices:an application to Populus euphratica polymorphic leaves

Leaf biochemical properties have been widely assessed using hyperspectral reflectance information by inversion of PROSPECT model or by using hyperspectral indices, but few studies have focused on arid ecosystems. As a dominant species of riparian ecosystems in arid lands, Populus euphratica Oliv. is an unusual tree species with polymorphic leaves along the vertical profile of canopy corresponding to different growth stages. In this study, we evaluated both the inversed PROSPECT model and hyperspectral indices for estimating biochemical properties of P. euphratica leaves. Both the shapes and biochemical properties of P. euphratica leaves were found to change with the heights from ground surface. The results indicated that the model inversion calibrated for each leaf shape performed much better than the model calibrated for all leaf shapes, and also better than hyperspectral indices. Similar results were obtained for estimations of equivalent water thickness （EWT） and leaf mass per area （LMA）. Hyperspectral indices identified in this study for estimating these leaf properties had root mean square error （RMSE） and R2 values between those obtained with the two calibration strategies using the inversed PROSPECT model. Hence, the inversed PROSPECT model can be applied to estimate leaf biochemical properties in arid ecosystems, but the calibration to the model requires special attention.

Diurnal and seasonal variations in carbon fluxes in bamboo forests during the growing season in Zhejiang province, China

Bamboo forest is an important forest type in subtropical China and is characterized by fast growth and high carbon sequestration capacity. However, the dynamics of carbon fluxes during the fast growing period of bamboo shoots and their correlation with environment factors are poorly understood. We measured carbon dioxide exchange and climate variables using open-path eddy covariance methods during the 2011 growing season in a Moso bamboo forest(MB, Phyllostchys edulis) and a Lei bamboo forest(LB, Phyllostachys violascens) in Zhejiang province,China. The bamboo forests were carbon sinks during the growing season. The minimum diurnal net ecosystem exchange(NEE) at MB and LB sites were-0.64 and-0.66 mg C m^(-2) s^(-1), respectively. The minimum monthly NEE, ecosystem respiration(RE), and gross ecosystem exchange(GEE) were-99.3 ± 4.03, 76.2 ±2.46, and^(-1)91.5 ± 4.98 g C m^(-2) month^(-1), respectively,at MB site, compared with-31.8 ± 3.44, 70.4 ± 1.41,and^(-1)57.9 ± 4.86 g C m^(-2) month^(-1), respectively, at LB site. Maximum RE was 92.1 ± 1.32 g C m^(-2) month^(-1) at MB site and 151.0 ± 2.38 g C m^(-2) month^(-1) at LB site.Key control factors varied by month during the growing season, but across the whole growing season, NEE and GEE at both sites showed similar trends in sensitivities to photosynthetic active radiation and vapor pressure deficit,and air temperature had the strongest correlation with RE at both sites. Carbon fluxes at LB site were more sensitive to soil water content compared to those at MB site. Both onyear(years when many new shoots are produced) and offyear(years when none or few new shoots are produced)should be studied in bamboo forests to better understand their role in global carbon cycling.

Evaluation of Farmer Training Satisfaction Level in Hubei Province

Based on fully understanding the significance of farmer training,this paper builds the evaluation index system for farmer training satisfaction level. Then this paper employs the field survey data about Yichang and Jingzhou in Hubei Province to evaluate the farmer training satisfaction level in Hubei Province. Results show that farmers have high level of satisfaction on agricultural training in Hubei Province,and the average satisfaction level reaches 0. 8556; there are regional differences in the farmer training satisfaction level in Hubei Province; the index weight is not entirely directly proportional to the training satisfaction level in the evaluation index system. Finally,from training courses,training teachers,training organization and follow-up services,this paper brings forward the recommendations for improving farmer training satisfaction level in Hubei Province,improve farmer training system,further improve the effectiveness of training and promote farmers' quality.

Soil erosion assessment by RUSLE model using remote sensing and GIS in an arid zone

In this research,we used the Revised Universal Soil Loss Equation(RUSLE)and Geographical Information System(GIS)to predict the annual rate of soil loss in the District Chakwal of Pakistan.The parameters of the RUSLE model were estimated using remote sensing data,and the erosion probability zones were determined using GIs.The estimated length slope(LS),crop management(C),rainfall erosivity(R),soil erodibility(K),and support practice(P)range from 0-68,227,0-66.61%,0-0.58,495.99-648.68 MJ/mm.t.ha^(-1).year^(-1),0.15-0.25 MJ/mm.t.ha^(-1).year^(-1),and 1 respectively.The results indicate that the estimated total annual potential soi loss of approximately 4,67,064.25 t.ha^(-1).year^(-1) is comparable with the measured'sediment ioss of 11,631 t.ha^(-1).year^(-1) during the water year 2020.The predicted soil erosion rate due to an increase in agricultural area is approximately 164,249.31 t.ha^(-1).year^(-1).In this study,we also used,Landsat imagery to rapidly achieve actual land use classification.Meanwhile,38.i3%of the region was threatened by very high soil erosion,where the quantity of soil erosion ranged from 365487.35 t.ha^(-1).year^(-1),Integrating GIS and remote sensing with the RUSLE model helped researchers achieve their final objectives.Land-use planners and decision-makers use the result's spatial distribution of soil erosion in District Chakwal for conservation and management planning.