Quickly and efficiently monitoring soil heavy metal content is crucial for protecting the natural environment human health. Estimating in soils using hyperspectral data a cost-efficient method but challenging due to effects of complex landscapes properties. One challenges how make lab-derived model based on samples applicable mapping contents metals air-borne or space-borne imagery at regional scale. For this purpose, our study proposed novel from HuanJing-1A (HJ-1A) HyperSpectral Imager...

Quickly and efficiently monitoring soil nutrient contents using remote sensing technology is of great significance for farmland productivity, food security sustainable agricultural development. Current research has been conducted to estimate map in large areas hyper-spectral techniques, however, it difficult obtain accurate estimates. In order improve the estimation accuracy contents, we introduced a GA-BPNN method, which combined back propagation neural network (BPNN) with genetic algorithm...

Soil nutrients play a vital role in plant growth and thus the rapid acquisition of soil nutrient content is great significance for agricultural sustainable development. Hyperspectral remote-sensing techniques allow quick monitoring nutrients. However, at present, obtaining accurate estimates proves to be difficult due weak spectral features low accuracy estimation models. This study proposed new method improve estimation. Firstly, characteristic variables, we employed partial least squares...

Rapid and accurate agricultural land evaluation provides essential guidance for the supervision allocation of resources; it also helps to ensure food security. Previous work has mainly evaluated quality at county level by using field sampling data based on a factor approach. However, is difficult achieve uniform, large-scale via conventional approaches because its spatial heterogeneity, as well large temporal economic costs associated with acquisition. In this study, we integrated publicly...

Flooding is a critical factor that limits the establishment of symbiosis between rice and arbuscular mycorrhizal fungi (AMF) in wetland ecosystems. The distribution carbon resources roots acclimation strategies to flooding stress presence AMF are poorly understood. We conducted root box experiment, employing nylon sheets or meshes create separate fungal chambers either prevented allowed any molecules pass through. found colonization rate expression genes OsD14L OsCERK1, which involved...

With the development of human society, cultivated land resources' utilization has become more diversified, and multifunction is critical to meet demand. However, studies on are mostly based data single time node or discontinuous years, which results may be influenced by abnormal function years. To assess its trade-off/synergy relationships accurately objectively, this study uses continuous long series analyze multifunctionality land. Firstly, Long short-term memory (LSTM) method was applied...

Efficiently obtaining leaf nitrogen content (LNC) in rice to monitor the nutritional health status is crucial achieving precision fertilization on demand. Unmanned aerial vehicle (UAV)-based hyperspectral technology an important tool for determining LNC. However, intricate coupling between spectral information and remains elusive. To address this, this study proposed estimation method LNC that integrates hybrid preferred features with deep learning modeling algorithms based UAV imagery. The...

The ability to rapidly and effectively monitor the alpine grassland aboveground biomass (AG-AGB) is very important resource management sustainable use. present satellite-driven models for estimating AG-AGB still need be improved due incomplete monitoring indicators a lack of sufficient in situ measurements. Thus, this study proposes new estimation model based on MODIS SRTM data improving accuracy AG-AGB. In model, first six were obtained from 33 spectral environmental using Extreme Gradient...

Rapid and efficient assessment of cultivated land quality (CLQ) using remote sensing technology is great significance for protecting land. However, it difficult to obtain accurate CLQ estimates the current satellite-driven approaches in pressure-state-response (PSR) framework, owing limitations linear models spectral indices. In order improve estimation accuracy CLQ, this study used four evaluation (the traditional model; partial least squares regression, PLSR; back propagation neural...

Soil exchange cations are a basic indicator of soil quality and environmental clean-up potential. The accurate efficient acquisition information on cation content is great importance for the monitoring pollution prevention. At present, few scholars focus exchangeable using remote sensing technology. This study proposes new method estimating hyperspectral data. In particular, we introduce Boruta successive projection (SPA) algorithms to screen feature variables, use Guangdong Province, China,...

Efficient monitoring of cultivated land quality (CLQ) plays a significant role in protection. Soil spectral data can reflect the state land. However, most studies have used crop information to estimate CLQ, and there is little research on using soil for this purpose. In study, hyperspectral were utilized first time evaluate CLQ. We obtained optimal variables from dry gradient boosting decision tree (GBDT) algorithm combined with variance inflation factor (VIF). Two estimation algorithms...

An accurate assessment of the stocking rate is crucial for maintaining stable function and sustainable use alpine grassland ecosystem. A new scenario design method to evaluate reasonable presented in current work. First, climate change quantified by potential net primary productivity (NPPp) measured adopting Zhou Guangsheng model, NPP generated anthropogenic activities (NPPh) estimated distinction between NPPp actual (NPPa) calculated with application Carnegie–Ames–Stanford Approach (CASA)...

Soil fertility affects crop yield and quality. A quick, accurate evaluation of soil is crucial for agricultural production. Few satellite image-based studies have quantified during the growth period. Therefore, this study proposes a new approach to quantitative fertility. Firstly, optimal spectral variables were selected using integration an extreme gradient boosting (XGBoost) algorithm with variance inflation factor (VIF). Then, based on where red-edge indices introduced first time,...

This study proposes a method for determining the optimal image date to improve evaluation of cultivated land quality (CLQ). Five vegetation indices: leaf area index (LAI), difference (DVI), enhanced (EVI), normalized (NDVI), and ratio (RVI) are first retrieved using PROSAIL model Gaofen-1 (GF-1) images. The indices then introduced into four regression models at different growth stages assessing CLQ. CLQ is finally determined according root mean square error (RMSE). tested validated in rice...

There has been substantial research for estimating and mapping soil moisture content (SMC) of large areas using remotely sensed images by developing models thermal inertia (STI). However, it is still a great challenge to accurately estimate SMC because the impact vegetation canopies vegetation-induced shadows in mixed pixels on estimates. In this study, new method was developed increase estimation accuracy an irrigated area located YingKe Heihe, China, ASTER data. method, original model bare...

Leaf area index (LAI) is one of the most important canopy structure parameters utilized in process-based models climate, hydrology, and biogeochemistry. In order to determine reliability applicability satellite LAI products, it critical validate products. Due surface heterogeneity scale effects, difficult accuracy improve spatio-temporal we propose a new multi-scale product validation method based on crop growth cycle. this method, used PROSAIL model derive Advanced Spaceborne Thermal...

Abstract The deployment of scientific and reasonable cultivated land quality (CLQ) monitoring points can provide timely accurate information on the current situation changes in CLQ. conventional method selecting CLQ are based use patches including different grades large patches, which reduces reliability Moreover, mainly considers only CLQ, resulting inaccessibility some points. There exist knowledge gaps deploying reliable sites present. Thus, to improve monitoring, this study presented a...

Soil and plant analyzer development (SPAD) value leaf nitrogen concentration (LNC) based on dry weight are important indicators affecting rice yield quality. However, there few reports the use of machine learning algorithms hyperspectral monitoring to synchronously predict SPAD LNC indica rice. Meixiangzhan No. 2, a high-quality rice, was grown at different rates. A device with an integrated handheld clip-on spectrometer internal quartz-halogen light source conducted monitor spectral...

The deployment of scientific and reasonable cultivated land quality (CLQ) monitoring points can provide timely accurate information on the current situation changes in CLQ, which is highly important to protect national food security. conventional methods selecting CLQ are based use patches. As there may be different grades large patches, being selected as reduces reliability CLQ. Moreover, point method mainly considers only ignores road accessibility terrain factors, resulting...