Objectives:　The　nonlinear　remote　sensing　ecological　index　(nRSEI)　is　a　recently　proposed　ecological　index,　which　used　the　kernel　principal　component　analysis　(kPCA)　algorithm　to　integrate　four　indicators　of　the　existing　remote　sensing　ecological　index　(RSEI)　rather　than　using　the　traditional　principal　component　analysis　(PCA)　technique.　The　main　reason　for　using　kPCA　was　that　in　the　Beijing　area　the　correlations　between　wetness,　greenness,　dryness,　and　heat　that　are　four　indicators　used　in　RSEI　were　generally　weak,　so　the　kPCA　that　is　specially　developed　to　deal　with　variables　with　nonlinear　relationship　was　needed　to　handle　these　four　non-linear　indicators.　This　paper　aims　to　examine　the　correlation　strength　of　these　four　indicators　in　the　Beijing　area　to　see　whether　their　relationship　is　strong　or　weak　and　analyzes　the　effectiveness　of　the　accuracy　assessment　method　that　was　used　for　the　validation　of　the　new　index.　Methods:　Through　examining　correlation　coefficients　and　scatter　diagrams,　the　correlation　between　the　four　indicators　is　investigated　to　find　out　whether　the　relationship　between　the　indicators　is　linear　or　non-linear.　Also,　the　effectiveness　of　the　validation　for　the　new　index　is　analyzed.　Results:　The　results　show　that　the　four　indicators　are　strongly　linearly　correlated　with　each　other,　therefore,　the　kPCA　was　not　suitable　for　the　intergra-dation　of　the　four　indicators.　The　methods　used　for　the　accuracy　assessment　of　the　new　index　also　have　obvious　defects　and　thus　failed　to　effectively　validate　the　accuracy　of　the　new　index.　In　addition,　some　important　issues　related　to　prepare　remote　sensing　papers　are　also　discussed.　These　include　the　feasibility　of　remote　sensing　modeling,　the　robustness　of　the　model,　the　scale　consistency　of　the　sub-indicators　in　the　model,　the　validation　method　for　the　model,　the　selection　of　reference　images　for　the　validation,　and　the　required　sample　size　for　accuracy　validation.　Conclusions:　The　nRSEI　is　not　a　suitable　index　for　the　assessment　of　regional　ecological　status　as　it　mistakenly　employed　kPCA　to　intergrade　the　four　indicators　that　are　linearly　related.　©　2023　Wuhan　University.　All　rights　reserved.