Soybean Biomass Inversion Models Based On Remote Sensing In Cultivated Land At The Field Scale

The cultivated land production capacity reveals the production capacity of cultivated land,and the real-time monitoring of the cultivated land productivity can make the limited cultivated land resources achieve a higher outcome.The analysis of the spatial distribution of cultivated land productivity and the scientific evaluation of the comprehensive production capacity of cultivated land in different regions can provide an important basis for improving land use efficiency,intensive land use,and formulating production increase measures.Crop biomass is a manifestation of cultivated land productivity,especially the biomass per unit area of crops,which can reflect the growth of crop populations within the scope of the cultivated land.Real-time and accurate monitoring of farmland crop biomass,adjustment of investment in agricultural production management data,variable fertilization and pesticide application are not only effective ways to reduce agricultural input and increase income,but also reduce resource waste,increase farmland productivity,and ensure food safety and promotion.An effective way for farmers to increase production and income.Moreover,estimates of crop biomass at the field scale can also provide a basis for crop yield prediction.In short,biomass monitoring is of great significance for improving agricultural production efficiency,protecting the agricultural environment,and enhancing the competitiveness of agricultural products.In order to accurately monitor and manage crop biomass in the field,SPOT-6 multi-spectral data and topographic data were used to establish a variety of models for the inversion mapping of soybean biomass in critical growth period.Based on soil temperature,moisture,organic matter,soil erosion,solar radiation and other factors,the spatial pattern and causes of soybean growth differences in different growth periods and different topography were analyzed.The results show that:(1)The multiple regression model for biomass estimation that combines topographic variables with vegetation indices can achieve high estimation accuracy.In July and August,R~2 reached 0.83 and the RMSE was 79.59g/m~2.(2)The spatial pattern of biomass varies with the slope aspect and position and over a growing season.Biomass was greater on sunny upper slopes than on shady lower slopes in June but then increased rapidly on the shady and bottom slopes in July,such that the difference between the shady and sunny slopes gradually narrowed.This trend continued until the biomass at the bottom was greater than that on the shady slopes,while the vegetation on the sunny slope was at aminimum in August.In addition,the gully region had low biomass accumulation,presumably due to poor water and fertilizer retention on the steep slopes.(3)The analysis of biomass impact factors found that,from late May to mid June,soybeans were in the germination stage and seedling stage.The total amount of solar radiation and soil temperature were the dominant factors affecting soybean growth and development.In mid-July,the overall temperature of the field increased.At this time,the soil moisture and organic matter content became the key factors in the flowering stage and the initial stage of pod formation.In August,the bottom of the slope relied on the advantages of organic matter and water and fertilizer accumulation,and the biomass increased the most in the later period;During the maturity of soybean in September,the leaves of soybeans turned yellow to litter,and the overall biomass decreased.Biomass is the basis for the formation of yield.Accurate monitoring of biomass helps to increase the grain yield of cultivated land and ensure the safety of cultivated land.Remote sensing means monitoring crop biomass is of great significance for increasing food production and protecting cultivated land productivity.The findings of this paper provide guidance for accurately monitoring crop growth and implementing targeted field management.